Page 1
Loading more openings…
You've reached the end of the list.
Applied AI Engineer
Omnifold · San Francisco, California, United States
Pay
$140k–230k
Setting
On-site
Applied AI Engineer
Location
San Francisco HQ; East Coast (General)
Employment Type
Full time
Location Type
On-site
Department
Applied AI
Overview
Application
Omnifold’s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.
What You’ll Do
Engage directly with real customer problems to deeply understand data and operational challenges
Frame customer problems clearly
Lead technical and value-oriented demos that resonate with both technical and non-technical stakeholders
Work hands-on with data using Python and SQL
Build custom demos and prototypes to support GTM activities
Act as a trusted technical partner during evaluations, pilots, and implementations
Support active customer deployments to ensure full value of platform is realized
What We’re Looking For
Technical sales or consulting experience (sales engineer, forward-deployed engineer, solution architect)
Prior experience as a data scientist or selling a product which required a foundational understanding of machine learning
Experience supporting complex, high-value enterprise sales cycles
Excellent project management skills and attention to detail
Experience using technologies such as Python and SQL to answer complex questions and solve business problems
Strong business intuition paired with technical rigor
Hands-on, builder mindset; willingness to dive into new technologies / topics as the situation requires
Confident engaging with business and technical stakeholders alike
How We Assess This Role
Technical assessment: Core data analysis and machine learning skills
Business impact presentation: In-depth walkthrough of an example where you have solved a business problem using AI, machine learning, or optimization
Demo: Create a demo in our application and showcase it in front of our team
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Omnifold for a position based in the United States. Employers on this board attest they are hiring domestically.
MTS - ML Research Engineer
Omnifold · San Francisco, California, United States
Pay
$95k–320k
Setting
On-site
MTS - ML Research Engineer
Location
San Francisco HQ
Employment Type
Full time
Location Type
On-site
Department
Research
Overview
Application
Member of Technical Staff, ML Research Engineer
Omnifold trains custom AI models for each customer's supply chain - purpose-built systems that forecast demand, optimize decisions, and adapt continuously to a changing world. The research team is responsible for the core intelligence that makes this possible: developing new model architectures, curating proprietary data assets, and pushing the boundaries of what ML can do.
What makes this job interesting:
You will work on problems that frontier models can't solve. Supply chain dynamics require modeling physical systems and processes.
You will own the full research cycle, from hypothesis to production model, with direct visibility into real-world impact.
You will work at the intersection of machine learning models, optimization, LLM reasoning capabilities, and proprietary data - a combination few research teams are building
What you'll own:
Training models for forecasting and optimization across complex, multi-variable supply chain environments
Building and curating proprietary data assets that carry signal about real-world physical and commercial systems
Integrating LLM knowledge and reasoning capabilities into purpose-built models to maximize accuracy and adaptability
Continuously improving model performance as market conditions shift (consumer sentiment, product launches, geopolitical changes, competitive dynamics)
What we're looking for:
5+ years of industry machine learning engineering, including and experimentation
Experience with time-series forecasting, mathematical modeling, optimization, or related domains
Understanding of LLMs, including fundamentals and practical system design including tool use and eval design
Experience working with messy, heterogeneous real-world data
Experience working with large code bases
Academic or industry research experience preferred
Comfort operating in a fast-moving, early-stage environment where research directly feeds production systems
Location: San Francisco (in-person, 5 days per week)
Omnifold’s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Omnifold for a position based in the United States. Employers on this board attest they are hiring domestically.
MTS - Infrastructure
Omnifold · San Francisco, California, United States
Pay
Not listed
Setting
On-site
Infrastructure Team
Omnifold trains custom AI models that help planners forecast the future. We are hiring for our Infrastructure Team, who own the systems that make everything else possible.
What makes this job interesting:
We train a unique model for each customer, which means model training and inference work differently here than at any other company. You’ll never get more reps building model training infrastructure!
Our team has very fast iteration speed but needs robust monitoring to pick up signal on user patterns. This is especially important as our application interface for AI-driven forecasting is unique on the market.
What you’ll own
Deployment: Reliable processes for getting models and services into production
Security: Data isolation between customers, product security, infrastructure hardening (SOC2 compliance and beyond)
Cloud resource management: GPU allocation, instance sizing, cost optimization
Monitoring and logging: Visibility into what's running, what's failing, and why
Data and ML ops: ETL pipelines from varied customer data sources, model versioning and lifecycle management
Automated testing: Building the test infrastructure that lets us ship with confidence
What we’re looking for
Experience with cloud computing (especially GPU workloads), CI/CD infrastructure-as-code. We run on AWS
Familiarity with or interest in ML workflows
Security fundamentals: encryption, access controls, compliance basics
Python proficiency
Must have a strong Computer Science background
Location: San Francisco (in-person, 5 days per week)
Omnifold’s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.
Listed by Omnifold for a position based in the United States. Employers on this board attest they are hiring domestically.
Infrastructure Tech Lead
Omnifold · San Francisco, California, United States
Pay
$95k–225k
Setting
On-site
Infrastructure Tech Lead
Location
San Francisco HQ
Employment Type
Full time
Location Type
On-site
Department
Engineering
Overview
Application
Infrastructure Tech Lead / Principal Engineer
Omnifold trains custom AI models that help planners forecast the future. We are hiring our first infrastructure tech lead, who will own the systems that make everything else possible.
What makes this job interesting:
We train a unique model for each customer, which means model training and inference work differently here than at any other company. You’ll never get more reps building model training infrastructure!
Our team has very fast iteration speed but needs robust monitoring to pick up signal on user patterns. This is especially important as our application interface for AI-driven forecasting is unique on the market.
What you’ll own
Deployment: Reliable processes for getting models and services into production
Security: Data isolation between customers, product security, infrastructure hardening (SOC2 compliance and beyond)
Cloud resource management: GPU allocation, instance sizing, cost optimization
Monitoring and logging: Visibility into what's running, what's failing, and why
Data and ML ops: ETL pipelines from varied customer data sources, model versioning and lifecycle management
Automated testing: Building the test infrastructure that lets us ship with confidence
What we’re looking for
Experience with cloud computing (especially GPU workloads), CI/CD infrastructure-as-code. We run on AWS
Familiarity with or interest in ML workflows
Security fundamentals: encryption, access controls, compliance basics
Python proficiency
Ideally ~10 years of experience, including startup experience, with at least 3 years in a tech lead role. 5+ years in infrastructure, DevOps, or platform engineering roles
Must have a strong Computer Science background
Location: San Francisco (in-person, 5 days per week)
Omnifold’s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Omnifold for a position based in the United States. Employers on this board attest they are hiring domestically.
MTS - Applications
Omnifold · San Francisco, California, United States
Pay
Not listed
Setting
On-site
MTS - Applications
Location
San Francisco HQ
Employment Type
Full time
Location Type
On-site
Department
Engineering
Overview
Application
Member of Technical Staff, Applications
Omnifold trains custom AI models that help planners forecast the future. At the application layer, our users interact with those models - creating forecasts, understanding why predictions shifted, and feeding context back into the system.
What makes this job interesting:
You will build interfaces that are both data-dense and elegant
You will design thoughtful human-LLM interaction patterns for real-world problems
You will constantly be making real product and architecture decisions.
What we're looking for:
5+ years full-stack experience, weighted toward frontend
Must have a strong Computer Science background (CS Degree, or developed a product with core computer science fundamentals e.g. compilers, search)
Experience with data-dense interfaces: dashboards, analytics products, planning tools
TypeScript, React 18+, Tailwind, Python
Experience with end-to-end feature development (requirements -> concept -> mocks -> technical design -> implementation -> documentation)
The ability to amplify your good taste + judgement using modern AI tools
Useful but not required:
Experience building:
features involving LLM or ML interactions
Enterprise SaaS
Application security and other systems design experience
Location: San Francisco (in-person, 5 days per week)
Omnifold’s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Omnifold for a position based in the United States. Employers on this board attest they are hiring domestically.
MTS - ML Research Scientist
Omnifold · San Francisco, California, United States
Pay
$70k–295k
Setting
On-site
MTS - ML Research Scientist
Location
San Francisco HQ
Employment Type
Full time
Location Type
On-site
Department
Research
Overview
Application
Member of Technical Staff, ML Research Scientist
Omnifold trains custom AI models for each customer's supply chain - purpose-built systems that forecast demand, optimize decisions, and adapt continuously to a changing world. The research team is responsible for the core intelligence that makes this possible: developing new model architectures, curating proprietary data assets, and pushing the boundaries of what ML can do.
What makes this job interesting:
You will work on problems that frontier models can't solve. Supply chain dynamics require modeling physical systems and processes.
You will own the full research cycle, from hypothesis to production model, with direct visibility into real-world impact.
You will work at the intersection of machine learning models, optimization, LLM reasoning capabilities, and proprietary data - a combination few research teams are building
What you'll own:
Designing and training models for forecasting and optimization across complex, multi-variable supply chain environments
Building and curating proprietary data assets that carry signal about real-world physical and commercial systems
Integrating LLM knowledge and reasoning capabilities into purpose-built models to maximize accuracy and adaptability
Continuously improving model performance as market conditions shift (consumer sentiment, product launches, geopolitical changes, competitive dynamics)
What we're looking for:
Strong foundations in machine learning — experience developing and evaluating forecasting models, and LLM pipelines
Deep understanding of time-series forecasting, optimization, or related domains
Experience working with messy, heterogeneous real-world data
PhD or equivalent research experience preferred, but exceptional engineers with relevant industry experience will be considered
Comfort operating in a fast-moving, early-stage environment where research directly feeds production systems
Location: San Francisco (in-person, 5 days per week)
Omnifold’s Mission
Every bad forecast has a physical consequence. Unnecessary goods are manufactured, shipped, and stored. Emergency air freight is needed for misallocated products. Poor production planning means workers show up with nothing to do, or work frantic overtime. Inefficiency is everywhere.
Our mission is to eliminate waste and accelerate growth for every company with physical products.
Apply for this Job
Powered by
Privacy PolicySecurityVulnerability Disclosure
Listed by Omnifold for a position based in the United States. Employers on this board attest they are hiring domestically.
Select a role
The full posting opens here — pay, setting and the full description, without leaving the list.