Motional

Full-stack Engineer

• Architect and lead the development of highly scalable, distributed infrastructure services on Kubernetes.
• Set the technical vision and roadmap for the team, making high-impact decisions that accelerate AV development.
• Mentor and guide other engineers on the team, elevating the group's technical abilities through design reviews, pair programming, and direct feedback.
• Get hands-on to build and deploy the most complex components of the platform, from high-throughput data processing services to core compute orchestration.

Staff Software Engineer

Software Engineer

• Design, build, document, test, and maintain network, compute, and storage infrastructure of our cloud platform for the AV fleet.
• Monitor, support, and resolve issues as they arise in production.
• Assist with high-level analysis, system design, and code reviews.
• Communicate with stakeholders and team leads to identify system requirements.

Engineer, Developer Platform – Cloud Apps

Platform Engineer

• Architect and drive the execution of our hybrid cloud strategy. You will design the networking, security, and compute abstraction layers that allow seamless workload mobility between on-premises high-performance computing (HPC) and AWS.
• Serve as the primary architect for our Kubernetes-based developer framework. You will identify and implement core improvements to the framework to enhance scalability, cost tracking and access control, reduce configuration complexity, and increase developer velocity.
• Lead organization-wide initiatives to harden service reliability. You will define the standards for observability, failover strategies, and production-grade stability across all platform-layer services.
• Prototype and deploy GenAI-focused tooling and agents to automate Platform Engineering tasks. This includes leveraging LLMs for automated incident response, predictive infrastructure scaling, and intelligent log analysis.
• Act as the final technical authority for infrastructure decisions, ensuring all new platform services align with long-term scalability and security goals.

Principal Platform Engineer

Data Engineer

• Author comprehensive technical design documents to propose, evaluate, and implement scalable infrastructure and tooling solutions.
• Build intuitive internal CLI tools, web applications, and Python-based SDKs leveraging Ray.
• Architect, deploy, and maintain robust cloud infrastructure on AWS.
• Leverage modern data tooling and formats (dbt, Apache Iceberg / Parquet, Redshift) to build highly efficient data pipelines.
• Champion continuous integration and deployment best practices using GitLab, while deploying scalable services and workloads.
• Act as a technical pillar on the team, establishing standards and mentoring peers.

Senior Software Engineer, Data Engineering

Engineer

• Lead the research and development of novel algorithms for motion planning in autonomous driving, including but not limited to advanced search-based methods, sophisticated geometry-based methods, and decision making under uncertainty with a strong emphasis on probabilistic approaches. • Architect and integrate complex combinations of motion planning and prediction algorithms, driving their evaluation and refinement for real-world deployment. • Design and build a robust, scalable, and high-performance codebase that facilitates rapid exploration, prototyping, and rigorous evaluation of innovative motion planning approaches and algorithms. • Drive technical collaboration and interface seamlessly with perception and prediction components upstream and trajectory optimization, tracking and control components downstream, ensuring end-to-end system performance. • Leverage your deep software development and research expertise to teach others better software practices and principles, fostering a culture of technical excellence. • Guide and mentor junior team members, cultivating a culture of product-focused engineering, rigorous research, and advanced development.

Staff Motion Planning Engineer

• Own and expand backend implementation and architecture of our visualization tooling suite (e.g., data mining quality assurance, operations and log ingestion, annotation pipelines, telemetry viewers).
• Collaborate closely with ML, frontend, UX, data services, data mining, and data annotation teams to align tooling with real-world workflows
• Support scalable, performance APIs and services for visual development, debugging, and annotation systems.
• Help mature internal standards, observability, and deployment processes for our backend stack
• Maintain and mature the team’s CI/CD infrastructure and process.

Senior Full Stack Engineer

Systems Engineer

• Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication.
• Implement optimizations like kernel fusion, sharding, and tiling to improve step time.
• Optimize distributed training pipelines using frameworks such as PyTorch Distributed.
• Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.
• Optimize robust data loading pipelines that maximize training throughput.

ML Systems Engineer, ML Acceleration

• Lead and scale a high-impact trajectory planning team.
• Drive commercial launch readiness for trajectory planning.
• Apply a rigorous, metrics-driven framework to quantify and improve on-road performance.
• Manage the computational efficiency and real-time performance of trajectory generation algorithms.
• Lead the rapid triage and root-cause analysis (RCA) of critical fleet incidents.
• Oversee the successful execution of complex, multi-team initiatives.
• Coordinate with peer leads to provide technical leadership.
• Define and negotiate quarterly and annual technical roadmaps aligned with company-level autonomy milestones.

Principal Engineer – Trajectory Planning Lead

Machine Learning Engineer

• Architect and Train Distilled Models: Design and implement teacher-student model frameworks for multimodal sensor data. Develop training pipelines for knowledge distillation. Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint.
• Reinforcement Learning for Data Discovery: Build RL-based policy learning and reasoning systems for autonomous driving applications. Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. Explore reward shaping, environment modeling, and multi-agent RL where applicable.
• Optimize Model Deployment for Real-Time Inference: Collaborate with backend engineers to deploy distilled and RL models into production. Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. Implement model versioning, A/B testing, and monitoring for performance regressions.
• Research and Integrate Agentic Systems: Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. Integrate such systems into our broader autonomy stack as experimental or production components.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.
• Mentor and Collaborate: Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. Guide junior engineers in best practices for model training, evaluation, and deployment.