Data Engineer - VC Backed Startups
Join SignalFire’s Talent Network for Data Engineer Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Data Engineering talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Data Engineers who are excited about building scalable data infrastructure, developing reliable pipelines, and enabling teams to make better decisions with trusted data.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for engineers who are:
✔ Passionate about building reliable, scalable data systems and infrastructure
✔ Experienced in transforming complex datasets into trusted, accessible data products
✔ Excited to establish data foundations in fast-moving startup environments
✔ Comfortable partnering with engineering, product, analytics, and machine learning teams
✔ Interested in improving how data is collected, modeled, governed, and used across an organization
Typical Roles & Responsibilities
Design, build, and maintain scalable batch and real-time data pipelines
Develop reliable data models, transformation workflows, and shared datasets for analytics and operational use cases
Build and manage cloud-based data warehouses, lakehouses, and data platforms
Integrate data from product, customer, financial, and third-party systems
Establish standards for data quality, testing, lineage, observability, and documentation
Partner with analytics, product, engineering, and business teams to understand data requirements
Support machine learning and AI applications by developing dependable training, feature, and inference data pipelines
Improve the performance, scalability, and cost efficiency of data infrastructure
Build self-service tools and frameworks that make data easier to discover and use
Implement appropriate access controls, privacy safeguards, and data-governance practices
Troubleshoot pipeline failures, data-quality issues, and performance bottlenecks
Help define the company’s broader data architecture and technical roadmap
Common Qualifications
While each startup has its own hiring criteria, many Data Engineer roles in our network look for:
3+ years of experience in data engineering, software engineering, analytics engineering, or a related technical role
Strong programming skills in Python, Java, Scala, or a similar language
Advanced proficiency in SQL and experience designing scalable data models
Experience building and maintaining production ETL or ELT pipelines
Familiarity with cloud platforms such as AWS, GCP, or Azure
Experience with modern data warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, or Databricks
Knowledge of workflow orchestration, transformation, and data-quality tooling
Understanding of distributed systems, data storage formats, and batch or streaming architectures
Ability to collaborate with technical and non-technical stakeholders to translate business needs into data solutions
Strong judgment around reliability, scalability, governance, and infrastructure tradeoffs
Experience in venture-backed startups or rapidly scaling technology companies may be preferred
💡 Technologies You Might Work With:
Languages: Python, SQL, Java, Scala, Go
Warehouses & Lakehouses: Snowflake, BigQuery, Redshift, Databricks, Delta Lake
Pipelines & Transformation: Airflow, Dagster, Prefect, dbt, Fivetran, Airbyte
Streaming & Processing: Kafka, Spark, Flink, Kinesis, Pub/Sub
Cloud & Infrastructure: AWS, GCP, Azure, Docker, Kubernetes, Terraform
Data Quality & Observability: Great Expectations, Monte Carlo, Soda, DataHub, OpenLineage
Databases & Storage: PostgreSQL, MySQL, DynamoDB, MongoDB, S3
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Data Engineering roles across our portfolio.
Data Engineer - VC Backed Startups
Join SignalFire’s Talent Network for Data Engineer Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Data Engineering talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Data Engineers who are excited about building scalable data infrastructure, developing reliable pipelines, and enabling teams to make better decisions with trusted data.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for engineers who are:
✔ Passionate about building reliable, scalable data systems and infrastructure
✔ Experienced in transforming complex datasets into trusted, accessible data products
✔ Excited to establish data foundations in fast-moving startup environments
✔ Comfortable partnering with engineering, product, analytics, and machine learning teams
✔ Interested in improving how data is collected, modeled, governed, and used across an organization
Typical Roles & Responsibilities
Design, build, and maintain scalable batch and real-time data pipelines
Develop reliable data models, transformation workflows, and shared datasets for analytics and operational use cases
Build and manage cloud-based data warehouses, lakehouses, and data platforms
Integrate data from product, customer, financial, and third-party systems
Establish standards for data quality, testing, lineage, observability, and documentation
Partner with analytics, product, engineering, and business teams to understand data requirements
Support machine learning and AI applications by developing dependable training, feature, and inference data pipelines
Improve the performance, scalability, and cost efficiency of data infrastructure
Build self-service tools and frameworks that make data easier to discover and use
Implement appropriate access controls, privacy safeguards, and data-governance practices
Troubleshoot pipeline failures, data-quality issues, and performance bottlenecks
Help define the company’s broader data architecture and technical roadmap
Common Qualifications
While each startup has its own hiring criteria, many Data Engineer roles in our network look for:
3+ years of experience in data engineering, software engineering, analytics engineering, or a related technical role
Strong programming skills in Python, Java, Scala, or a similar language
Advanced proficiency in SQL and experience designing scalable data models
Experience building and maintaining production ETL or ELT pipelines
Familiarity with cloud platforms such as AWS, GCP, or Azure
Experience with modern data warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, or Databricks
Knowledge of workflow orchestration, transformation, and data-quality tooling
Understanding of distributed systems, data storage formats, and batch or streaming architectures
Ability to collaborate with technical and non-technical stakeholders to translate business needs into data solutions
Strong judgment around reliability, scalability, governance, and infrastructure tradeoffs
Experience in venture-backed startups or rapidly scaling technology companies may be preferred
💡 Technologies You Might Work With:
Languages: Python, SQL, Java, Scala, Go
Warehouses & Lakehouses: Snowflake, BigQuery, Redshift, Databricks, Delta Lake
Pipelines & Transformation: Airflow, Dagster, Prefect, dbt, Fivetran, Airbyte
Streaming & Processing: Kafka, Spark, Flink, Kinesis, Pub/Sub
Cloud & Infrastructure: AWS, GCP, Azure, Docker, Kubernetes, Terraform
Data Quality & Observability: Great Expectations, Monte Carlo, Soda, DataHub, OpenLineage
Databases & Storage: PostgreSQL, MySQL, DynamoDB, MongoDB, S3
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Data Engineering roles across our portfolio.
Head of AI/ML (Director/VP) - VC Backed Startups
Join SignalFire’s Talent Network for Head of AI/ML (Director/VP) Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring AI and machine learning leaders. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Heads of AI/ML, including Director- and VP-level leaders, who are excited about defining AI strategy, building high-performing teams, and translating emerging technologies into differentiated products and business outcomes.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for leaders who are:
✔ Passionate about building AI-native products and applying machine learning to meaningful customer problems
✔ Experienced in defining AI/ML strategy and leading teams from research and experimentation through production deployment
✔ Excited to partner with founders, product leaders, and engineering teams to shape company and product direction
✔ Comfortable balancing technical depth, organizational leadership, and commercial impact
Typical Roles & Responsibilities
Define and execute the company’s AI and machine learning strategy in alignment with product and business priorities
Build, lead, and develop high-performing teams across machine learning, applied AI, data science, and research
Identify high-impact opportunities to apply AI and translate them into differentiated product capabilities
Lead the development, evaluation, deployment, and continuous improvement of production ML systems
Establish technical standards for model quality, experimentation, reliability, observability, and responsible AI
Guide decisions across model selection, fine-tuning, retrieval, data strategy, infrastructure, and build-versus-buy tradeoffs
Partner with engineering and product leaders to integrate AI capabilities into scalable customer-facing products
Oversee data collection, labeling, governance, and feedback loops required to improve model performance
Evaluate emerging models, research, and tooling while maintaining a practical focus on customer and business value
Communicate AI strategy, capabilities, limitations, and investment priorities to executive teams, boards, customers, and partners
Support recruiting, organizational design, and workforce planning for the company’s AI and ML functions
Help establish safeguards around privacy, security, bias, explainability, and regulatory requirements
Common Qualifications
While each startup has its own hiring criteria, many Head of AI/ML roles in our network look for:
10+ years of experience across machine learning, artificial intelligence, data science, or software engineering, including meaningful leadership experience
Proven experience building and scaling AI/ML teams in startup or high-growth technology environments
Track record of developing and deploying machine learning systems into production
Strong technical foundation across modern ML methods, model evaluation, data pipelines, and production infrastructure
Experience applying large language models, generative AI, deep learning, or traditional machine learning to real-world products
Ability to connect technical investments to product differentiation, customer outcomes, and business value
Experience partnering closely with product, engineering, data, and go-to-market leaders
Strong judgment around model quality, latency, cost, scalability, safety, and reliability
Ability to operate effectively across hands-on technical leadership, team management, and executive-level strategy
Advanced degree in computer science, machine learning, statistics, mathematics, or a related field may be preferred but is not always required
💡 Technologies You Might Work With:
Languages & Frameworks: Python, PyTorch, TensorFlow, JAX, scikit-learn, Hugging Face
Generative AI: Large language models, multimodal models, retrieval-augmented generation, fine-tuning, prompt engineering, agentic systems
Data & Infrastructure: Spark, Databricks, Snowflake, Kafka, Airflow, vector databases, feature stores
Cloud & MLOps: AWS, GCP, Azure, Kubernetes, Docker, MLflow, Weights & Biases, SageMaker, Vertex AI
Models & Platforms: OpenAI, Anthropic, Google, Meta, open-source foundation models, proprietary model architectures
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future AI and machine learning leadership roles across our portfolio.
Head of AI/ML (Director/VP) - VC Backed Startups
Join SignalFire’s Talent Network for Head of AI/ML (Director/VP) Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring AI and machine learning leaders. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Heads of AI/ML, including Director- and VP-level leaders, who are excited about defining AI strategy, building high-performing teams, and translating emerging technologies into differentiated products and business outcomes.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for leaders who are:
✔ Passionate about building AI-native products and applying machine learning to meaningful customer problems
✔ Experienced in defining AI/ML strategy and leading teams from research and experimentation through production deployment
✔ Excited to partner with founders, product leaders, and engineering teams to shape company and product direction
✔ Comfortable balancing technical depth, organizational leadership, and commercial impact
Typical Roles & Responsibilities
Define and execute the company’s AI and machine learning strategy in alignment with product and business priorities
Build, lead, and develop high-performing teams across machine learning, applied AI, data science, and research
Identify high-impact opportunities to apply AI and translate them into differentiated product capabilities
Lead the development, evaluation, deployment, and continuous improvement of production ML systems
Establish technical standards for model quality, experimentation, reliability, observability, and responsible AI
Guide decisions across model selection, fine-tuning, retrieval, data strategy, infrastructure, and build-versus-buy tradeoffs
Partner with engineering and product leaders to integrate AI capabilities into scalable customer-facing products
Oversee data collection, labeling, governance, and feedback loops required to improve model performance
Evaluate emerging models, research, and tooling while maintaining a practical focus on customer and business value
Communicate AI strategy, capabilities, limitations, and investment priorities to executive teams, boards, customers, and partners
Support recruiting, organizational design, and workforce planning for the company’s AI and ML functions
Help establish safeguards around privacy, security, bias, explainability, and regulatory requirements
Common Qualifications
While each startup has its own hiring criteria, many Head of AI/ML roles in our network look for:
10+ years of experience across machine learning, artificial intelligence, data science, or software engineering, including meaningful leadership experience
Proven experience building and scaling AI/ML teams in startup or high-growth technology environments
Track record of developing and deploying machine learning systems into production
Strong technical foundation across modern ML methods, model evaluation, data pipelines, and production infrastructure
Experience applying large language models, generative AI, deep learning, or traditional machine learning to real-world products
Ability to connect technical investments to product differentiation, customer outcomes, and business value
Experience partnering closely with product, engineering, data, and go-to-market leaders
Strong judgment around model quality, latency, cost, scalability, safety, and reliability
Ability to operate effectively across hands-on technical leadership, team management, and executive-level strategy
Advanced degree in computer science, machine learning, statistics, mathematics, or a related field may be preferred but is not always required
💡 Technologies You Might Work With:
Languages & Frameworks: Python, PyTorch, TensorFlow, JAX, scikit-learn, Hugging Face
Generative AI: Large language models, multimodal models, retrieval-augmented generation, fine-tuning, prompt engineering, agentic systems
Data & Infrastructure: Spark, Databricks, Snowflake, Kafka, Airflow, vector databases, feature stores
Cloud & MLOps: AWS, GCP, Azure, Kubernetes, Docker, MLflow, Weights & Biases, SageMaker, Vertex AI
Models & Platforms: OpenAI, Anthropic, Google, Meta, open-source foundation models, proprietary model architectures
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future AI and machine learning leadership roles across our portfolio.
Forward Deployed Engineer (FDE) - VC Backed Startups
Join SignalFire’s Talent Network for Forward Deployed Engineer Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Forward Deployed Engineering talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Forward Deployed Engineers who are excited about working directly with customers to solve complex technical problems, deploy production-ready solutions, and shape how emerging technologies are implemented in real-world environments.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for engineers who are:
✔ Passionate about solving complex customer problems through software and technical implementation
✔ Excited to work at the intersection of engineering, product, and customer success
✔ Comfortable operating in ambiguous, fast-moving startup environments
✔ Interested in translating customer requirements into scalable technical solutions
✔ Strong communicators who can partner effectively with both technical and non-technical stakeholders
Typical Roles & Responsibilities
Partner directly with customers to understand their workflows, technical environments, and business requirements
Design, build, and deploy production-ready solutions using the company’s core platform or APIs
Develop custom integrations, applications, and technical workflows tailored to customer needs
Troubleshoot complex implementation, infrastructure, data, and product issues
Collaborate with product and engineering teams to translate customer feedback into platform improvements
Guide customers through technical architecture, deployment, testing, and adoption
Create reusable tools, documentation, and implementation frameworks that improve delivery across customers
Support pilots and proofs of concept while helping customers move successfully into production
Identify recurring customer needs that can inform product strategy and roadmap priorities
Common Qualifications
While each startup has its own hiring criteria, many Forward Deployed Engineer roles in our network look for:
3+ years of experience in software engineering, solutions engineering, technical consulting, or a similar customer-facing technical role
Strong coding skills in languages such as Python, JavaScript or TypeScript, Java, Go, or C++
Experience building APIs, integrations, data pipelines, or production applications
Familiarity with cloud platforms such as AWS, GCP, or Azure
Ability to understand customer requirements and translate them into practical technical solutions
Strong debugging, systems thinking, and problem-solving skills
Experience working with technical and non-technical stakeholders
Comfort managing multiple customer deployments or technical workstreams simultaneously
Willingness to travel periodically, depending on the company and customer environment
💡 Technologies You Might Work With:
Languages & Frameworks: Python, JavaScript, TypeScript, React, Node.js, Java, Go, C++
Data & Infrastructure: PostgreSQL, Snowflake, Databricks, Kafka, Spark, Redis, Elasticsearch
Cloud & DevOps: AWS, GCP, Azure, Docker, Kubernetes, Terraform, GitHub Actions
AI & Machine Learning: Large language models, retrieval-augmented generation, model APIs, vector databases, machine learning pipelines
Integrations & Tools: REST APIs, GraphQL, webhooks, enterprise data systems, CRM and ERP platforms
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Forward Deployed Engineer roles across our portfolio.
Forward Deployed Engineer (FDE) - VC Backed Startups
Join SignalFire’s Talent Network for Forward Deployed Engineer Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Forward Deployed Engineering talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Forward Deployed Engineers who are excited about working directly with customers to solve complex technical problems, deploy production-ready solutions, and shape how emerging technologies are implemented in real-world environments.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for engineers who are:
✔ Passionate about solving complex customer problems through software and technical implementation
✔ Excited to work at the intersection of engineering, product, and customer success
✔ Comfortable operating in ambiguous, fast-moving startup environments
✔ Interested in translating customer requirements into scalable technical solutions
✔ Strong communicators who can partner effectively with both technical and non-technical stakeholders
Typical Roles & Responsibilities
Partner directly with customers to understand their workflows, technical environments, and business requirements
Design, build, and deploy production-ready solutions using the company’s core platform or APIs
Develop custom integrations, applications, and technical workflows tailored to customer needs
Troubleshoot complex implementation, infrastructure, data, and product issues
Collaborate with product and engineering teams to translate customer feedback into platform improvements
Guide customers through technical architecture, deployment, testing, and adoption
Create reusable tools, documentation, and implementation frameworks that improve delivery across customers
Support pilots and proofs of concept while helping customers move successfully into production
Identify recurring customer needs that can inform product strategy and roadmap priorities
Common Qualifications
While each startup has its own hiring criteria, many Forward Deployed Engineer roles in our network look for:
3+ years of experience in software engineering, solutions engineering, technical consulting, or a similar customer-facing technical role
Strong coding skills in languages such as Python, JavaScript or TypeScript, Java, Go, or C++
Experience building APIs, integrations, data pipelines, or production applications
Familiarity with cloud platforms such as AWS, GCP, or Azure
Ability to understand customer requirements and translate them into practical technical solutions
Strong debugging, systems thinking, and problem-solving skills
Experience working with technical and non-technical stakeholders
Comfort managing multiple customer deployments or technical workstreams simultaneously
Willingness to travel periodically, depending on the company and customer environment
💡 Technologies You Might Work With:
Languages & Frameworks: Python, JavaScript, TypeScript, React, Node.js, Java, Go, C++
Data & Infrastructure: PostgreSQL, Snowflake, Databricks, Kafka, Spark, Redis, Elasticsearch
Cloud & DevOps: AWS, GCP, Azure, Docker, Kubernetes, Terraform, GitHub Actions
AI & Machine Learning: Large language models, retrieval-augmented generation, model APIs, vector databases, machine learning pipelines
Integrations & Tools: REST APIs, GraphQL, webhooks, enterprise data systems, CRM and ERP platforms
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Forward Deployed Engineer roles across our portfolio.
Data Scientist (Senior/Staff) - VC Backed Startups
Join SignalFire’s Talent Network for Senior/Staff Data Scientist Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Data Science talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Senior and Staff Data Scientists who are excited about using data, experimentation, and machine learning to solve complex product and business problems at high-growth startups.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for data scientists who are:
✔ Passionate about using data to improve products, customer outcomes, and business decisions
✔ Experienced in experimentation, statistical analysis, predictive modeling, or causal inference
✔ Excited to work closely with product, engineering, operations, and business teams
✔ Comfortable operating with incomplete data and ambiguous problems in fast-moving startup environments
✔ Interested in building scalable analytical frameworks, models, and decision-making systems
Typical Roles & Responsibilities
Partner with product, engineering, and business leaders to identify high-impact opportunities for data science
Design and analyze experiments to evaluate product changes, growth initiatives, and operational strategies
Develop predictive, forecasting, recommendation, ranking, or optimization models
Apply statistical methods and causal inference techniques to measure impact and inform decisions
Build metrics, analytical frameworks, and dashboards that improve visibility into product and business performance
Translate complex analyses into clear recommendations for technical and non-technical stakeholders
Collaborate with engineers to productionize models and integrate data science into customer-facing products
Identify patterns in user, customer, operational, and market data
Establish best practices for experimentation, model evaluation, data quality, and analytical rigor
Mentor other data scientists and raise the technical standard of the broader data organization
Help shape the company’s data strategy, tooling, and long-term analytical roadmap
Common Qualifications
While each startup has its own hiring criteria, many Senior and Staff Data Scientist roles in our network look for:
5+ years of experience in data science, applied statistics, machine learning, decision science, or a related field
Strong proficiency in Python, R, SQL, or similar analytical languages
Experience with statistical modeling, experimentation, causal inference, forecasting, or predictive analytics
Track record of using data to influence product strategy, customer outcomes, or business performance
Ability to work with large, complex, and imperfect datasets
Experience partnering closely with product managers, engineers, operators, and executive stakeholders
Strong communication skills and the ability to explain technical findings clearly
Experience developing models or analytical systems that are used in production or operational decision-making
Strong judgment around methodology, measurement, tradeoffs, and uncertainty
Experience in venture-backed startups or rapidly scaling technology companies may be preferred
Advanced degree in statistics, economics, computer science, mathematics, operations research, or a related field may be preferred, but is not always required
💡 Technologies You Might Work With:
Languages & Analysis: Python, R, SQL, pandas, NumPy, SciPy
Modeling & Machine Learning: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow
Experimentation & Statistics: A/B testing, causal inference, Bayesian methods, time-series analysis
Data Platforms: Snowflake, BigQuery, Redshift, Databricks, Spark
Visualization & Analytics: Looker, Tableau, Mode, Hex, Amplitude
Workflow & Development: Jupyter, dbt, Airflow, Git, Docker, cloud platforms
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Senior and Staff Data Scientist roles across our portfolio.
Data Scientist (Senior/Staff) - VC Backed Startups
Join SignalFire’s Talent Network for Senior/Staff Data Scientist Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Data Science talent. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Senior and Staff Data Scientists who are excited about using data, experimentation, and machine learning to solve complex product and business problems at high-growth startups.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for data scientists who are:
✔ Passionate about using data to improve products, customer outcomes, and business decisions
✔ Experienced in experimentation, statistical analysis, predictive modeling, or causal inference
✔ Excited to work closely with product, engineering, operations, and business teams
✔ Comfortable operating with incomplete data and ambiguous problems in fast-moving startup environments
✔ Interested in building scalable analytical frameworks, models, and decision-making systems
Typical Roles & Responsibilities
Partner with product, engineering, and business leaders to identify high-impact opportunities for data science
Design and analyze experiments to evaluate product changes, growth initiatives, and operational strategies
Develop predictive, forecasting, recommendation, ranking, or optimization models
Apply statistical methods and causal inference techniques to measure impact and inform decisions
Build metrics, analytical frameworks, and dashboards that improve visibility into product and business performance
Translate complex analyses into clear recommendations for technical and non-technical stakeholders
Collaborate with engineers to productionize models and integrate data science into customer-facing products
Identify patterns in user, customer, operational, and market data
Establish best practices for experimentation, model evaluation, data quality, and analytical rigor
Mentor other data scientists and raise the technical standard of the broader data organization
Help shape the company’s data strategy, tooling, and long-term analytical roadmap
Common Qualifications
While each startup has its own hiring criteria, many Senior and Staff Data Scientist roles in our network look for:
5+ years of experience in data science, applied statistics, machine learning, decision science, or a related field
Strong proficiency in Python, R, SQL, or similar analytical languages
Experience with statistical modeling, experimentation, causal inference, forecasting, or predictive analytics
Track record of using data to influence product strategy, customer outcomes, or business performance
Ability to work with large, complex, and imperfect datasets
Experience partnering closely with product managers, engineers, operators, and executive stakeholders
Strong communication skills and the ability to explain technical findings clearly
Experience developing models or analytical systems that are used in production or operational decision-making
Strong judgment around methodology, measurement, tradeoffs, and uncertainty
Experience in venture-backed startups or rapidly scaling technology companies may be preferred
Advanced degree in statistics, economics, computer science, mathematics, operations research, or a related field may be preferred, but is not always required
💡 Technologies You Might Work With:
Languages & Analysis: Python, R, SQL, pandas, NumPy, SciPy
Modeling & Machine Learning: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow
Experimentation & Statistics: A/B testing, causal inference, Bayesian methods, time-series analysis
Data Platforms: Snowflake, BigQuery, Redshift, Databricks, Spark
Visualization & Analytics: Looker, Tableau, Mode, Hex, Amplitude
Workflow & Development: Jupyter, dbt, Airflow, Git, Docker, cloud platforms
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Senior and Staff Data Scientist roles across our portfolio.
Applied AI Scientist/Researcher (Senior/Staff) - VC Backed Startups
Join SignalFire’s Talent Network for Senior/Staff Applied AI Scientist & Researcher Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Applied AI Scientists and Researchers. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Senior and Staff Applied AI Scientists and Researchers who are excited about developing advanced AI capabilities, solving complex technical problems, and translating emerging research into differentiated products.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for scientists and researchers who are:
✔ Passionate about advancing the capabilities and real-world applications of artificial intelligence
✔ Experienced in developing, adapting, and evaluating modern machine learning models
✔ Excited to translate research and experimentation into production-ready product capabilities
✔ Comfortable operating at the intersection of research, engineering, product, and customer needs
✔ Interested in solving open-ended technical problems in fast-moving startup environments
Typical Roles & Responsibilities
Research, develop, and evaluate machine learning methods that improve product capabilities and customer outcomes
Design experiments to test new model architectures, training approaches, data strategies, and system designs
Adapt foundation models through fine-tuning, post-training, prompt optimization, retrieval, or other techniques
Develop evaluation frameworks and benchmarks for model quality, reliability, safety, and performance
Build prototypes and proofs of concept that demonstrate the potential of emerging AI techniques
Partner with AI/ML engineers and software engineers to translate successful experiments into production systems
Improve model accuracy, reasoning, latency, efficiency, robustness, and cost
Curate, generate, and evaluate datasets used for training, fine-tuning, and model assessment
Investigate model failures, edge cases, and unexpected behavior to identify opportunities for improvement
Stay current with relevant research and determine which advances can create practical product value
Communicate findings, tradeoffs, and technical recommendations to product, engineering, and executive stakeholders
Mentor other scientists and contribute to the company’s research culture, technical standards, and AI roadmap
Publish research, contribute to open-source projects, or represent the company within the broader technical community where appropriate
Common Qualifications
While each startup has its own hiring criteria, many Senior and Staff Applied AI Scientist and Researcher roles in our network look for:
5+ years of experience in machine learning, artificial intelligence, applied research, or a related technical field
Strong foundation in deep learning, statistics, optimization, and experimental design
Experience developing or adapting models for real-world product applications
Expertise in one or more areas such as natural language processing, generative AI, computer vision, multimodal learning, reinforcement learning, recommendation systems, or speech
Proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, or JAX
Experience with model training, fine-tuning, post-training, evaluation, or inference
Ability to design rigorous experiments and draw sound conclusions from incomplete or ambiguous results
Track record of translating research concepts into prototypes, production systems, or measurable product improvements
Ability to collaborate closely with research, engineering, product, and domain experts
Strong written and verbal communication skills, including the ability to explain complex technical concepts clearly
Staff-level candidates may be expected to define research direction, lead cross-functional initiatives, and influence broader AI strategy
Advanced degree in computer science, machine learning, statistics, mathematics, or a related field may be preferred, although equivalent applied experience may be considered
💡 Technologies You Might Work With:
Languages & Frameworks: Python, PyTorch, TensorFlow, JAX, Hugging Face, scikit-learn
Models & Techniques: Large language models, transformers, multimodal models, diffusion models, reinforcement learning, recommendation and ranking systems
Model Adaptation: Fine-tuning, reinforcement learning from feedback, preference optimization, distillation, synthetic data, prompt optimization
AI Systems: Retrieval-augmented generation, agents, tool use, structured generation, reasoning systems, model routing
Evaluation & Experimentation: Offline and online evaluation, human evaluation, benchmarking, red teaming, interpretability, model observability
Data & Infrastructure: Spark, Databricks, Snowflake, vector databases, distributed training, GPUs, Kubernetes
Models & Platforms: OpenAI, Anthropic, Google, Meta, open-source foundation models, and proprietary model architectures
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Senior and Staff Applied AI Scientist and Researcher roles across our portfolio.
Applied AI Scientist/Researcher (Senior/Staff) - VC Backed Startups
Join SignalFire’s Talent Network for Senior/Staff Applied AI Scientist & Researcher Roles at VC-Backed Startups
🛑 This is not an application for a specific job. Instead, this is a way to get on the radar of VC-backed startups that are actively hiring Applied AI Scientists and Researchers. If you have any questions, please direct inquiries to talentnetwork@signalfire.com.
At SignalFire, we partner with top early-stage startups that are shaping the future of technology. Our portfolio spans 200+ innovative companies across AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS.
We’re looking to connect with exceptional Senior and Staff Applied AI Scientists and Researchers who are excited about developing advanced AI capabilities, solving complex technical problems, and translating emerging research into differentiated products.
By joining SignalFire’s Talent Network, your profile will be shared with our portfolio companies, giving you visibility into exclusive early-stage opportunities that may not be publicly listed.
Who Should Join?
We’re looking for scientists and researchers who are:
✔ Passionate about advancing the capabilities and real-world applications of artificial intelligence
✔ Experienced in developing, adapting, and evaluating modern machine learning models
✔ Excited to translate research and experimentation into production-ready product capabilities
✔ Comfortable operating at the intersection of research, engineering, product, and customer needs
✔ Interested in solving open-ended technical problems in fast-moving startup environments
Typical Roles & Responsibilities
Research, develop, and evaluate machine learning methods that improve product capabilities and customer outcomes
Design experiments to test new model architectures, training approaches, data strategies, and system designs
Adapt foundation models through fine-tuning, post-training, prompt optimization, retrieval, or other techniques
Develop evaluation frameworks and benchmarks for model quality, reliability, safety, and performance
Build prototypes and proofs of concept that demonstrate the potential of emerging AI techniques
Partner with AI/ML engineers and software engineers to translate successful experiments into production systems
Improve model accuracy, reasoning, latency, efficiency, robustness, and cost
Curate, generate, and evaluate datasets used for training, fine-tuning, and model assessment
Investigate model failures, edge cases, and unexpected behavior to identify opportunities for improvement
Stay current with relevant research and determine which advances can create practical product value
Communicate findings, tradeoffs, and technical recommendations to product, engineering, and executive stakeholders
Mentor other scientists and contribute to the company’s research culture, technical standards, and AI roadmap
Publish research, contribute to open-source projects, or represent the company within the broader technical community where appropriate
Common Qualifications
While each startup has its own hiring criteria, many Senior and Staff Applied AI Scientist and Researcher roles in our network look for:
5+ years of experience in machine learning, artificial intelligence, applied research, or a related technical field
Strong foundation in deep learning, statistics, optimization, and experimental design
Experience developing or adapting models for real-world product applications
Expertise in one or more areas such as natural language processing, generative AI, computer vision, multimodal learning, reinforcement learning, recommendation systems, or speech
Proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, or JAX
Experience with model training, fine-tuning, post-training, evaluation, or inference
Ability to design rigorous experiments and draw sound conclusions from incomplete or ambiguous results
Track record of translating research concepts into prototypes, production systems, or measurable product improvements
Ability to collaborate closely with research, engineering, product, and domain experts
Strong written and verbal communication skills, including the ability to explain complex technical concepts clearly
Staff-level candidates may be expected to define research direction, lead cross-functional initiatives, and influence broader AI strategy
Advanced degree in computer science, machine learning, statistics, mathematics, or a related field may be preferred, although equivalent applied experience may be considered
💡 Technologies You Might Work With:
Languages & Frameworks: Python, PyTorch, TensorFlow, JAX, Hugging Face, scikit-learn
Models & Techniques: Large language models, transformers, multimodal models, diffusion models, reinforcement learning, recommendation and ranking systems
Model Adaptation: Fine-tuning, reinforcement learning from feedback, preference optimization, distillation, synthetic data, prompt optimization
AI Systems: Retrieval-augmented generation, agents, tool use, structured generation, reasoning systems, model routing
Evaluation & Experimentation: Offline and online evaluation, human evaluation, benchmarking, red teaming, interpretability, model observability
Data & Infrastructure: Spark, Databricks, Snowflake, vector databases, distributed training, GPUs, Kubernetes
Models & Platforms: OpenAI, Anthropic, Google, Meta, open-source foundation models, and proprietary model architectures
What Happens Next?
Submit your application to join SignalFire’s Talent Ecosystem.
We review applications on an ongoing basis to identify strong candidates.
If there’s a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
No match yet? We’ll keep your profile on file for future Senior and Staff Applied AI Scientist and Researcher roles across our portfolio.
The full posting opens here — pay, setting and the full description, without leaving the list.