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At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity. WHO WE ARE We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully. The Full-Stack Software Engineer, Internal Services builds and improves internal deployment tooling that helps Serve ship robot software safely, quickly, and at fleet scale. Responsibilities - Build, test, and maintain internal web services and user interfaces that support software release workflows for engineering, QA, and operations teams. - Focused work on frontend, backend, cloud infrastructure and embedded software.. - Collaborate with your teammates on the entire software lifecycle for internal services - design, implementation, testing, deployment and operations. - Add and improve observability across the deployment experience, including real-time status, logs, timelines, and audit trails that make it easier to understand rollout health and debug failures. - Strengthen CI/CD, automated testing, and release safety checks to improve confidence in deployment tooling and reduce operational risk. - Contribute to deployment governance features such as auditability, role-based access, and privileged action tracking. - High expectation on production quality clean, maintainable code and provide thoughtful PR feedback, including constructively challenging assumptions, identifying risks, and improving overall engineering quality. - Support production rollouts and release operations as needed, Participate in the oncall rotation for internal services. - Demonstrated experience delivering quality software with and without GenAl Qualifications - 5+ years of software engineering experience with meaningful ownership of production systems, internal tools, or other high-visibility software projects. - Strong backend experience with Node.js or TypeScript-based services. - Strong frontend experience with TypeScript and a modern web framework; Vue is preferred, but React experience is also acceptable. - Experience working with cloud platforms GCP perfered. - Hands-on experience designing, implementing, testing, and supporting production software with strong attention to reliability and maintainability. - Demonstrated ability to own a problem area end-to-end, work through ambiguity, and keep momentum on an important cross-functional project. - Strong communication and collaboration skills, including the ability to pair program regularly, work closely with stakeholders, and keep work moving when priorities shift. - Willingness to give candid, respectful technical feedback in code reviews and design discussions. - A practical, detail-oriented engineering mindset with strong testing instincts and careful consideration of downstream consequences. What Makes you Stand Out - Experience with deployment systems, release tooling, feature flags, fleet management, or internal developer platforms. - Experience building or supporting CI/CD workflows, ideally with GitHub Actions or a similar system. - Experience with relevant product areas such as scheduling, observability, audit trails, access control, or rollout governance. - Experience with GCP, Terraform, Kubernetes, or related infrastructure used to operate internal platforms at scale. - Experience supporting software used by multiple internal personas, especially engineering, QA, and operations teams. - Experience improving test coverage, structured logging, metrics, or tracing in an operationally important system. - Comfort working in a fast-moving environment with a strong bias for action and an expectation of personal ownership. *Please Note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada: - Canada - ALL: $115K - $160k CAD (Base)

Listed by Serve Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

98 days ago

VP of Security

Serve Robotics · los angeles, california , United States

Pay

$240k–300k

Setting

Remote

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At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity. WHO WE ARE We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully. The VP of Security is responsible for developing the security roadmap and ensuring alignment as the business continues to scale and grow the autonomous delivery platform. This role leads active initiatives, identifies opportunities to improve and accelerate the security function, and continues raising the bar on Serve’s security posture without slowing down the business. This role leads and develops a highly technical team, operates effectively at both strategic and operational levels, and makes security a force multiplier for the company. Responsibilities - Lead Serve’s security strategy across corporate security, platform security, product and robot security, governance, risk and compliance (GRC), and security operations. - Own the security roadmap and drive progress on initiatives, including endpoint security and compliance, enterprise SSO, enterprise security controls, security policy enforcement, incident response planning, robot identity, security and access controls, and support for SOX ITGC and SOC 2 readiness across the enterprise. - Partner closely with IT, Engineering, Product, and Platform teams to embed secure development practices across hardware and software development, cloud infrastructure, and enterprise systems. - Advance GRC maturity, including security policies, monitoring and SOPs, data protection and privacy training. - Build and mature incident response capabilities. - Develop and maintain security practices that support the safe and secure operation of Serve’s robotics platform, connected systems, and operational infrastructure. - Lead, mentor, and grow a high-performing security organization that balances technical excellence, operational discipline, urgency, and strong business partnership. - Translate complex security risks into clear business decisions for executives and actionable guidance for technical and non-technical stakeholders. - Establish metrics, dashboards, and reporting cadences that provide visibility into security posture, operational health, risk trends, roadmap progress, and business impact. - Evaluate and improve security tooling, monitoring capabilities, access management practices, endpoint controls, and operational security processes to support scalable company growth. - Support executive leadership and board-level reporting related to security strategy, enterprise risk, incident response readiness, audit readiness, and compliance initiatives. Qualifications - 10+ years of progressive security experience, including leadership of security teams in high-growth technology companies. - Experience as a senior security leader in a startup or scale-up environment where the company grew quickly and security had to mature into an auditable, enterprise-grade capability without creating unnecessary friction. - Strong technical depth across enterprise security, cloud security, endpoint security, identity and access management, vulnerability management, detection and response, secure software development, and infrastructure security. - Experience with governance, risk, and compliance programs, including security policies, risk management, vendor assessments, SOX ITGC, SOC 2 readiness, audit preparation, and executive-level security reporting. - Demonstrated ability to partner effectively with Engineering and Product teams on platform security, application security, secure SDLC practices, and infrastructure monitoring and controls. - Experience building and maturing incident response programs, escalation procedures, operational security controls, and business continuity practices. - Strong understanding of modern identity, access, monitoring, and endpoint ecosystems, including experience with enterprise collaboration, endpoint protection, monitoring, and security management platforms. - Ability to prioritize effectively, make pragmatic risk decisions, and drive execution across multiple parallel initiatives. - Excellent communication skills with the ability to influence technical teams, non-technical stakeholders, executive leadership, auditors, and board-level audiences. - A leadership style that is collaborative, direct, accountable, low-ego, and action-oriented. What Makes You Stand Out - Experience securing environments that include corporate IT, cloud infrastructure, third-party vendors and partners, robotics and/or IoT devices,, or other complex cyber-physical environments. - Experience supporting public company readiness initiatives, regulated operational environments, or enterprise compliance programs. - Experience supporting acquisitions, customer security reviews, enterprise partnerships, or vendor due diligence processes. - Familiarity with platforms such as Google Workspace, GitHub Enterprise, JumpCloud, SentinelOne, Wazuh, Vanta, Jira, Atlassian, Slack Enterprise, or similar enterprise security ecosystems. - Passion for building security organizations that balance operational agility, strong risk management, and business enablement in fast-moving environments. Additional Information - The position will require occasional travel and flexibility outside standard business hours during critical incidents, audits, or operational expansion. *Please Note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada: - Canada - ALL: $220k - $340k CAD (Base)

Listed by Serve Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

112 days ago

Sr. Manager, Data Engineering & Analytics

Serve Robotics · los angeles, california , United States

Pay

$211k–246k

Setting

Remote

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Listed by Serve Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

155 days ago

IT Identity and Access Management (IAM) Engineer

Serve Robotics · los angeles, california , United States

Pay

$140k–160k

Setting

Remote

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Listed by Serve Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

165 days ago

Lead Machine Learning Engineer

Serve Robotics · los angeles, california , United States

Pay

$225k–260k

Setting

Remote

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At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity. WHO WE ARE We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully. This role develops and scales large-scale machine learning training systems for multimodal robotics data, enabling the creation of high-performance autonomy models. By optimizing distributed training pipelines, neural network architectures, and data processing workflows, the position improves training efficiency, accelerates model iteration, and maximizes GPU utilization. The role collaborates closely with ML researchers and infrastructure teams, influencing the design, deployment, and performance of end-to-end autonomy models and the large-scale data pipelines that support them. Responsibilities - Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters. - Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time. - Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data. - Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance. - Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage. - Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training. - Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline. - Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies. - Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches. Qualifications - Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline. - Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments. - Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling. - Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows. - Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies. What Makes You Stand out - Experience identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput in machine learning systems. - Experience training machine learning models on robotics or autonomous driving datasets involving multimodal sensor inputs such as camera video, LiDAR point clouds, radar, or telemetry data. - Experience developing models that combine multiple data modalities (e.g., images, point clouds, and structured sensor data) into a unified learning system. - Peer-reviewed publications or significant research contributions in machine learning, robotics, or related areas. *Please note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada - Canada - ALL: $177k - $215k CAD

Listed by Serve Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

264 days ago

Lead Engineer, Reinforcement Learning & Scenario Generation

Serve Robotics · Redwood City , California , United States

Pay

$225k–300k

Setting

Remote

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At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity. WHO WE ARE We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully. The Lead Engineer, RL Scaling & Procedural Scenario Generation is responsible for building scalable training pipelines and generating high-fidelity synthetic scenarios. This role designs procedural simulation environments, creates diverse long-tail edge cases, and optimizes RL systems to train robust foundational models. This role sits at the intersection of simulation, machine learning, distributed systems, and content generation and has a high impact on how quickly and safely agents learn in simulation. Responsibilities - Develop RL algorithms that can help with terrain intelligence and social navigation behaviors. - Design, build, and optimize large-scale RL training pipelines (distributed compute, GPU clusters, containerized workflows). - Implement curriculum learning, domain randomization, and multi-agent RL strategies. - Optimize RL model performance, sample efficiency, and stability across thousands to millions of simulation steps. - Build automated tools for experiment orchestration, rollout collection, and metrics visualization. - Develop procedural generation pipelines for synthetic environments, agents, and dynamic behaviors. - Build tools to generate long-tail scenarios, sudden appearance of objects, traffic behaviors, rare events, and environmental variations. - Create systems for configuration, validation, and scoring of generated scenarios. - Collaborate with autonomy, ML, and safety teams to map real-world failures into repeatable synthetic simulation cases. - Design APIs to connect RL agents, scenario generators, planners, and environment simulators. - Debug and optimize simulation performance (real-time speed, determinism, reproducibility). - Work with 3D assets, traffic models, mapping systems (e.g., Isaac Sim, CARLA, Unity, Gazebo). - Partner with autonomy, data, and modeling teams to define training objectives and scenario requirements. - Translate real-world logs and edge cases into parameterized procedural content. - Document tools, frameworks, and workflows for internal users. Qualifications - Master’s degree in Robotics, AI, Computer Science, Mathematics, or a related field. - 7+ years of professional experience with shipping transformer based AI models handling complex navigation or manipulation tasks in AV or robotics solutions at scale in the real world. - 3+ years technical leadership/architecture experience - Strong experience with Reinforcement Learning (PPO, SAC, A3C, DQN, multi-agent RL, or equivalents). - Hands-on experience with distributed training frameworks (Ray RLlib, Accelerate, PyTorch Distributed, Kubernetes, or similar). - Proficiency in Python and C++ for performance-critical simulation or graphics pipelines. - Experience building or modifying simulation environments (Isaac Sim, Unity, Unreal, CARLA, Gazebo, MuJoCo or custom engines). - Experience with procedural generation (noise functions, rule-based systems, agent scripts, behavior trees). - Experience with GPU compute, containers, and cloud infrastructure. What Make You Stand Out - Background in generative AI (diffusion, LLMs) for scenario synthesis or environment creation. - Experience with traffic simulation (SUMO) or sensor simulation (LiDAR, camera pipelines). - Knowledge of CUDA, graphics engines, physics modeling, or rendering. * Please note: The base salary range listed in this job description reflects compensation for candidates based in the San Francisco Bay Area. We are also open to qualified talent working remotely across the: United States - Base salary range (U.S. – all locations): $190k - $230k USD Canada - Base salary range (Canada - all locations): $160k - $190k CAD

Listed by Serve Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.

Engineering

314 days ago

Software Engineer, ML Infrastructure

Serve Robotics · los angeles, california , United States

Pay

$155k–190k

Setting

Remote

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Listed by Serve Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.

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