MongoDB is building a world-class team in North America to create tooling that helps customers modernize their applications and migrate their data from legacy relational databases to MongoDB in real-time. As companies modernise legacy workloads and data ecosystems, they are increasingly drawn to the flexibility and scalability of the document model. The tools developed by the Code Generation and Data Migration team are critical in this journey, helping customers with schema modeling, code generation, initial data loads, and continuous data synchronization.

We're looking for a Senior Engineer with a strong background in computer science fundamentals, systems design, experience in the Java ecosystem, streaming systems, and data-intensive applications to join our engineering team. In this role, you will be instrumental in designing, building, and optimizing the underlying data structures, algorithms, and database interactions that power our generative AI platform, code generation and migration tools. This involves crafting sophisticated orchestration layers, robust integration points, and high-performance data systems that seamlessly connect and leverage advanced AI capabilities for code generation and  building a sophisticated data migration suite using a modern technology stack, which includes Java, Spring Boot, Kafka, Debezium, and React.You will work on critical components that ensure the scalability, efficiency, and reliability of our services, collaborating closely with AI researchers, product management and other engineers to design and implement cutting-edge products that solve complex customer challenges.

This role will be based out of North America in the PST and MST zones only.

The ideal candidate for this role will have

• 2+ years of  engineering experience in backend systems, distributed systems, or core platform development
• Experience in one or several of Java, Rust, C/C++, and/or Python, with a strong understanding of systems-level programming, memory management, and performance tuning
• Experience with streaming data platforms such as Apache Kafka and Change Data Capture (CDC) tools like Debezium
• Experience with relational data modeling and hands-on experience with at least one SQL database (Postgres, MySQL, etc)
• Exposure to client-side technologies such as JavaScript and React is a plus
• Good understanding of algorithms, data structures and their time and space complexity
• Curiosity, a positive attitude, and a drive to continue learning
• Excellent verbal and written communication skills

Nice to Have

• Familiarity with cloud-native distributed systems (e.g., Kubernetes)
• Experience with NoSQL databases and understanding of their trade-offs is great, but not required. We'll teach you NoSQL
• Contributions to relevant open-source projects

Position Expectations

• Contribute high-quality, well-tested backend code to the data migration engine and core components of our generative AI orchestration platform
• Collaborate effectively with Product Management, AI researchers and machine learning engineers and designers to build and deliver on the product roadmap
• Work to develop robust and efficient backend services that orchestrate AI functionalities
• Identify and address performance bottlenecks and architectural challenges in our systems, particularly within data flow and orchestration
• Participate actively in code reviews to enforce best practices and patterns
• Help troubleshoot and resolve complex technical issues in our distributed systems
• Give and solicit feedback on technical design documents and pull requests
• Perform tasks related to process such as CI/CD, quality, testing, etc 

Success Measures

Within the first three months, you will have:

• Familiarize yourself with the MongoDB database and aggregation language
• Familiarize yourself with the backend tech stack including Java, Spring Boot, and Kafka
• Set up software development infrastructure (tech stack, build tools, etc) to enable development using the relevant tech stacks
• Started collaborating with your peers and contributed to code reviews

Within six months, you will have:

• Familiarised yourself with the rest of our the application modernization tool stack
• Delivered at least one large scale feature that spans the entire tech stack
• Reviewed and contributed to scope and technical design documents

Within 12 months, you will have:

• Become a key contributor to our backend stack, capable of taking on complex features independently
• Helped recruit and interview new members of the team
• Collaborated effectively with other teams at MongoDB on cross-functional projects

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform—the most widely available, globally distributed database on the market—helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and nearly 60,000 customers—including 75% of the Fortune 100 and AI-native startups—relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

REQ ID: 425547

Mission Summary:

Motional’s onboard autonomous driving system team works at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s next-generation on-board autonomous driving system. As part of this team, the High Performance Compute Platform team is responsible for Motional’s current and next-generation onboard autonomous driving system. If you are a software engineer and love the idea of working on embedded AI hardware and software compute systems to create the next generation of autonomous vehicles, we would love to talk with you. We are hiring in a range of levels, from graduate engineers to Staff engineers.

What You'll Be Doing:

• Design and develop infrastructure software on various hardware platforms for applications such as vision processing, radar systems, safety monitoring, etc., to be run on self-driving vehicles.
• Design test harnesses for embedded software components and full systems
• Provide technical mentorship to engineers.
• Proactively work with cross-functional engineering teams to solve complex and interesting problems.
  
  

What We're Looking For:

• Experience with creating detailed requirements from use cases.
• Ability to lead a technical initiative, including breaking down work and guiding other engineers through execution.
• Experience writing software for embedded platforms in C and C++.
• Experience with Test-Driven Development (TDD).
• Experience working on embedded Linux / RTOSs.
• Experience working with networks (Ethernet, CAN etc.) and the common networking protocols.
• Experience with debugging on embedded platforms.
• Experience writing software in Python and experience doing automation with shell scripting.
• Experience working with ARM Cortex MCUs or Microprocessors.
  
  

Bonus Points (not required):

• Experience working with large data pipelines, and platforms that require deterministic execution.
• Experience using inter-system communication protocols such as I2C and SPI
• Experience deploying Machine Learning models.
• Experience working with GPUs.
• Experience working directly with the Linux kernel or Device Drivers.
• Experience with Simulation and Code Generation, and knowing when their use is appropriate.
• Experience working with Bazel.
• Experience integrating various sensors, including Cameras, IMUs, Radars, LIDARs.
• Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

As a Staff Engineer within the Platform Engineering team, you will be a hands-on technical leader driving the evolution of Motional's global cloud and hybrid infrastructure. Operating as a senior individual contributor, you'll tackle our hardest platform problems directly — designing systems, creating code, and setting technical direction for initiatives that span multiple teams. Your work will help transform our R&D environment into a hardened, cost-efficient, and scalable commercial backbone, and ensure our internal platforms deliver the developer velocity required for rapid AV deployment.

What you'll be doing:

• Design and build major components of our cloud architecture — including networking, storage, compute abstraction layers. You'll own the technical design end-to-end and create code where it matters most
• Drive technical evolution of our Kubernetes-based developer platform. Identify the highest-leverage improvements to scalability, cost attribution, access control, and developer ergonomics — then design and implement them to reduce configuration complexity and accelerate engineering velocity
• Raise the reliability bar across Platform Engineering. Define and implement standards for SLIs, SLOs, observability, and failover, and partner with peer teams to roll them out across platform-layer services
• Drive FinOps practices — cost visibility, attribution, accountability as well as optimization recommendations
• Design and ship AI-native developer experiences by integrating LLM-powered tooling into CI, build systems, monitoring platforms, and local development workflows to remove friction and boost engineering throughput
  
  

What we're looking for:

• Bachelor's or Master's in Computer Science, Engineering, or a related field (or equivalent practical experience)
• 9 years in Software or Infrastructure Engineering, with significant experience in a senior IC capacity
• Deep, expert-level knowledge of Kubernetes internals and ecosystem (Operators (Crossplane, ArgoWorkflow/Airflow, KubeRay), Kubecost/OpenCost, Service Mesh, GitOps and other CNCF projects)
• Proven track record of designing or migrating complex workloads between on-prem data centers and major cloud providers (AWS, GCP, other)
• Experience with Infrastructure-as-Code (Terraform/CloudFormation, Terraform Cloud/Spacelift/Atlantis) and advanced observability tooling (OpenTelemetry, Prometheus/VictoriaMetrics, Grafana, Datadog)
• Expert in architecting AI-driven operations, leveraging LLMs and agentic workflows to automate incident response, predictive scaling, and intelligent system observability
• Demonstrated ability to influence technical roadmaps across an entire engineering organization and mentor senior technical staff
  
  

Bonus points (not required):

• Proven expertise in CI/CD orchestration, build systems (Bazel, RBE), frameworks
• Prior experience with large-scale machine learning training pipelines
• Experience leading technical projects in a globally distributed team
• Open-source contributions to Kubernetes, observability, or infrastructure tooling ecosystems

Mission Summary:

Motional is seeking a Staff Front End Engineer expert for its Web Visualization Tooling team.  This role will directly contribute through the creation of critical web tooling and visualization that allows us to improve our feedback loops for machine learning (ML) engineers and improve the performance and adaptability of our autonomous vehicle (AV) system.

What you'll be doing:

• Drive cross-team standards and best practices for web development
• Own cross team collaboration, ensuring deliveries are made on time and effectively
• Architect and scale a modern frontend ecosystem using React, TypeScript, and Vue.
• Collaborate closely with ML, frontend, UX, data services, data mining, and data annotation teams to align tooling with real-world workflows
• Build mission-critical UI systems used by a variety of internal customers.
• Design initiative experiences for complex, data-heavy workflows.
• Drive best practices around performance, scalability and maintainability
• Ship quickly and iterate in a product driven environment.
  
  

What we're looking for:

• Desire to design and build user-facing visual applications, and a passion for good UX.
• Strong proficiency in React, Typescript and modern frontend architecture
• Proficiency in Vue
• Strong product sense with a focus on UX and usability.
• Ability to work with high ownership and autonomy.
• Ability to work autonomously and independently, and to drive development initiatives with little oversight. High aptitude for learning and organization skills.
• Great interpersonal skills and ability to mentor and (on occasion) delegate to other engineers at various levels of relative seniority.
• A well-established technical design and documentation habit
• Capability to work directly with customers to identify foundational needs and provide solutions.
  
  

Bonus Points(not required): 

• Demonstrated project management expertise / advanced hands-on understanding of project management agility and operational excellence.
• A love for the continuous delivery of value.
• Exposure to backend systems (Node, Python)
• Experience in Game Development or 3D Visualization

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first-of-its-kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.

The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas later this year.

Mission Summary:

The Autonomy Subsystems team at Motional is focused on designing and evaluating modules of the autonomy software. We are seeking a talented senior engineer to contribute to evaluation of our machine learning subsystems. Our team performs a variety of evaluations of machine learning models, including offline model eval, evaluation of open and closed-loop re-simulations, and assessment of on-road performance. Our goal is to establish metrics that describe autonomy subsystem performance, evaluate against those metrics to validate performance, and provide meaningful insight to ML developers and teams, working closely alongside them to improve the performance of ML models on the car.

We are looking for a highly-technical systems engineer with experience designing and evaluating safety critical systems and machine learning models. Bring your passion for autonomy, robotics, and machine learning to our team to help build a production-ready system and prepare for our scaled robotaxi service deployment.

Responsibilities:

• Define, prototype, and validate advanced metrics that bring new insights to model performance evaluation for autonomy subsystems (mainly focused on perception, prediction, and planning).
• Assess coverage of existing training and testing, then curate test sets that sufficiently capture Motional’s intended deployment operational design domain, across many axes (agent types, interaction types, map/spatial, etc.).
• Evaluate machine learning model performance against these metrics and tests.
• Assess system-level performance and subsystem contribution to system behavior through large-scale data analysis of on-road and simulation events.
• Define performance requirements for autonomy subsystems, and supporting analyses required to establish these targets.
• Coordinate subsystem-level evaluation with other system-level testing efforts across the systems team in support of the safety case and safety claims.
  
  

Qualifications:

• An engineer who has demonstrated strong cross-functional collaborative skills and is highly self-starting. This is a highly cooperative role across systems, autonomy, infrastructure, and other parts of the organization.
• Proven engineer with 5+ years of experience working on high-tech safety critical systems or robotics, ideally from a systems, robotics, or similar background.
• Master’s degree in relevant fields (systems, robotics, ML, CS, etc.) or Bachelor’s degree plus significant relevant in-field experience.
• Skilled Python developer, familiarity with analyzing the large scale data required to train and test ML models.
• Experience designing unique metrics that capture key aspects of system behavior and are used to enhance model development.
  
  

Bonus Skills:

• Experience training machine learning models, familiarity with state-of-the-art ML model architectures.
• Deep experience with a specific aspect of machine learning for autonomous robotics (perception, prediction, or planning).
• Knowledge of C++, especially applied to safety critical systems.
• Experience with systems verification and validation techniques
• Experience with regression testing updates to already-deployed software in a safety-critical environment.
• Experience with automotive standards (E.g: ISO 12207, ISO 26262, ISO 21448)
• Experience with software quality management and assurance activities (E.g: IATF 16949, ISO 9001)
• Thorough understanding of robotics systems including sensors, actuators, mechatronics and software systems
• Work in cross collaboration teams to address problems and find solutions
  
  

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Travel Requirements (greater than 20%):
Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

Working Environment

The work environment characteristics described here represent those a team member encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and/or airborne particlesThe noise level in the environment is low to moderate. 

Motional AD LLC is an Equal Employment Opportunity employer and we welcome all qualified applicants.  All applicants will receive impartial consideration without regard to race, sex, color, national origin, age, disability, veteran status, genetic data, religion, gender identity, sexual orientation or other legally protected status.  Motional AD LLC is a Drug-free workplace.

Motional is a leading autonomous driving company on a mission to make driverless vehicles a safe, reliable, and accessible reality. Backed by Hyundai Motor Group, Motional is at the forefront of the physical AI revolution. Motional isn’t just building first-of-its-kind technology; we are transforming transportation to create safer streets and more sustainable mobility options.

The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas later this year.

We are seeking a highly skilled Software Engineer to play a critical role in evaluating and improving the performance of our autonomous vehicle (AV) technology. This engineer will be responsible for developing and implementing metrics and software tools, primarily using Python, to effectively measure, analyze, and report on the performance of our AV systems. Their work will directly contribute to accelerating the development of safe and reliable autonomous technologies by transforming complex AV data into actionable insights for the organization.

What You'll Be Doing:

• Design AV Performance Analysis Pipelines: Define, design, and implement key pipelines to extract on-road or simulated events and evaluate autonomous vehicle performance (e.g., lane change capability, oscillation/braking metrics, VRU emergence from occlusion).
• Develop Metrics Software and AI-Powered Tools: Leverage strong Python skills to develop frameworks, AI-powered tools, and systems for calculating, aggregating, and visualizing AV performance metrics from large datasets.
• Collaborate Cross-Functionally: Work with systems, software, AI, controls, simulation, and validation teams to understand needs and improve systems like Perception, Prediction, and Planning.
• Ensure Data Quality and Validation: Implement rigorous checks and validation processes to ensure the accuracy and integrity of data used for performance evaluation.
• Document and Share Knowledge: Create clear documentation for metrics and analysis tools, and share best practices with the engineering team.
• Advance Technical Expertise: Proactively evaluate and integrate new technologies and methodologies relevant to autonomous vehicles and software engineering.
  
  

What We're Looking For:

• 5+ years of software engineering experience and a Bachelor's degree in Computer Science or a related field; a Master's degree is a plus.
• Expert-level proficiency in Python and a strong understanding of modern software development principles and best practices.
• Strong analytical and problem-solving skills, particularly in the context of complex system performance evaluation and data analysis.
• Proficient with Git and standard version control workflows.
• Excellent communication and collaboration skills, with the ability to effectively communicate technical concepts and results to diverse audiences (engineers, product managers, leadership).
• Proven ability to work both independently and effectively as part of a multidisciplinary team.
• Strong attention to detail and a commitment to data quality, accuracy, and rigorous testing.
• Comfortable working in a fast-paced, agile, continuous integration/continuous delivery (CI/CD) environment.
  
  

Bonus Points (not required):

• Experience working with autonomous vehicle sensor data (e.g., LiDAR, camera, radar).
• Familiarity with simulation environments and data generated from AV simulations.
• Experience with data visualization tools (e.g., Looker) for presenting performance metrics.
• Knowledge of statistical analysis and machine learning techniques relevant to performance evaluation.

Success Metrics:

• Successful design and implementation of key autonomous vehicle performance metrics.
• Improvement in the efficiency and effectiveness of AV performance evaluation processes.
• Timely and accurate delivery of data and insights to AV development teams.
• Contribution to the overall advancement and safety of our autonomous vehicle technology.

This is a unique opportunity to make a meaningful impact at Motional by enhancing our data platform and accelerating development speed for our industry-leading autonomous vehicle metrics and capabilities ecosystem. We take pride in having some of the brightest and most dedicated talent in the world on our team. If you're innovative, driven, and eager to tackle challenges, we'd love to have you join us!

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Travel Requirements (greater than 20%):

Expected to travel for 1-2 weeks each quarter visiting all of our testing sites - mostly to LV, but also domestically as needed for the business.

Working Environment:

The work environment characteristics described here represent those a team member encounters while performing the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and/or airborne particlesThe noise level in the environment is low to moderate.

Mission Summary:

The Staff Systems Safety Engineer:

• Oversees the development and implementation of engineering safety processes for our autonomous vehicle systems
• Is responsible for reviewing hazards, risk assessments, and ensuring compliance with industry safety standards (ISO 26262, SOTIF, and ASIL).
• Collaborates with cross-functional teams, including software, hardware, and AI engineers, to ensure integration of safety into the design and validation of our AV system
  
  

Essential Duties:

• Ensure and oversee the development and implementation of  system safety processes for autonomous vehicle platforms.
• Ensure hazard analysis and risk assessments (HARA) for AV components and subsystems are conducted.
• Assist in the definition and verification of functional safety requirements in compliance with ISO 26262 and SOTIF (ISO 21448).
• Collaborate with hardware, software, and AI teams to integrate safety measures into perception, decision-making, and control systems.
• Support the development of failure mode and effects analysis (FMEA) and fault tree analysis (FTA) for AV systems.
• Ensure compliance with regulatory and industry safety standards, including SAE J3016, NHTSA, and UNECE WP.29.
• Ensure the development and execution of safety verification and validation plans, including simulation, testing, and real-world driving scenarios.
• Work with certification bodies and regulators to ensure safety case documentation and approval processes.
• Contribute to the development of safety culture within the organization by providing guidance and training
  
  

Qualifications:

Educational Requirements

• Bachelor’s or Master’s degree in Systems Engineering, Electrical Engineering, Computer Science, Mechanical Engineering, or a related field 

Technical / Specialized Knowledge, Skills, Abilities:

• 5+ years industry and 3+ years of experience in safety engineering for automotive, aerospace, or robotics systems.
• Strong knowledge of functional safety standards (ISO 26262, SOTIF, ASIL).
• Knowledge of systems / software engineering standards (INCOSE, ISO15288, ASPICE CMMI)
• Experience with hazard analysis, risk assessment, and safety case development.
• Proficiency in safety analysis tools (e.g., Medini Analyze, Ansys, Fault Tree+, FMEA software).
• Familiarity with autonomous vehicle architectures, including perception, planning, and control systems.
• Experience working with real-time embedded systems and software safety.
• Strong problem-solving, analytical, and communication skills.
• Experience with machine learning safety challenges in AVs.
• Knowledge of cybersecurity principles related to vehicle safety (ISO/SAE 21434).
• Experience in safety assurance for AI-driven systems.
• Previous work with automotive OEMs, Tier 1 suppliers, or AV startups

Physical Demands:

While performing the duties of this job, the employee is frequently required to sit, talk, or hear. The employee is occasionally required to stand and at times for long periods; walk; use hands to finger, handle, or feel; reach with hands and arms. The employee must occasionally lift and move up to 50 pounds.

Working Environment:

The work environment characteristics described here are representative of those a team member encounters while performing the essential functions of this job.  Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. 

While performing the duties of this job, the team member is regularly exposed to the office environment, outside weather conditions, road conditions, and pedestrian traffic.  The team member is regularly exposed to mechanical and computer parts. The team member is occasionally exposed to fumes and airborne particles.  The noise level in the environment is low to moderate. When traveling, the team member will be exposed to airports, airplanes, hotels and public transportation environments.

This role is based in our Pittsburgh or Las Vegas office and requires in-office days each week.

Mission Summary:

As a Technical Lead Manager at Motional, you'll lead a team responsible for the development and delivery of core trajectory planning algorithms via numerical optimization for autonomous vehicles. In this role, you will be at the forefront of shaping the motion planning and control systems that enable our fleet to navigate complex environments safely, efficiently, and comfortably. As a key leader, you will drive the development and continuous performance improvements of cutting-edge trajectory generation algorithms, collaborate with cross-functional teams (Planning, Perception, Prediction, Localization, ML Products, and Systems Engineering), and mentor a talented group of highly skilled engineers to deliver state-of-the-art solutions. 

If you are passionate about autonomous driving and being part of our commercial launch this year, thrive on solving challenging real-world problems, and eager to make a significant impact in a rapidly evolving field, we want to hear from you!

What you'll do:

• Lead and scale a high-impact trajectory planning team, defining its technical vision, execution strategy, and long-term organizational role within the Motion organization.
• Drive commercial launch readiness for trajectory planning by delivering on-road driving behavior improvements (safety, comfort, and assertiveness) across the fleet through feature expansion, continuous performance improvements, and innovation through state-of-the art methods.
• Apply a rigorous, metrics-driven framework to quantify and improve on-road performance. Champion leveraging existing training and evaluation pipelines to ensure scalable, production-ready delivery.
• Manage the computational efficiency and real-time performance of trajectory generation algorithms, ensuring latency budget compliance.
• Lead the rapid triage and root-cause analysis (RCA) of critical fleet incidents, translating real-world edge cases into immediate algorithmic refinements and validation sets.
• Oversee the successful execution of complex, multi-team initiatives, ensuring technical alignment from design through deployment while maintaining a rigorous standard for testing and validation to meet the high safety bars required for our commercial launch.
• Coordinate with peer leads to provide technical leadership, making consequential decisions on architectural direction, strategic investments, tactical execution, and technical debt reduction.Ensure technical excellence through rigorous design and code reviews while actively facilitating the professional growth and mentorship of your engineering team. Set and drive ambitious goals that challenge the team to deliver industry-leading trajectory planning solutions.
• Define and negotiate quarterly and annual technical roadmaps aligned with company-level autonomy milestones in coordinate with project managers and execute plans on schedule.
  
  

What we're looking for:

• Proven Engineering Leadership: 2+ years of experience managing high-performing development teams, with a demonstrable track record of inspiring, mentoring, and coaching engineers and driving measurable productivity gains.
• Ship-to-Production Mindset: Experience leading the delivery of production-quality algorithms in a high-stakes environment (Autonomous Vehicles, Robotics, or Aerospace). You know what it takes to move from a prototype to a commercially viable product.
• Strategic Vision: Ability to look beyond the immediate deliverables to define and defend a balanced technical roadmap that balances long-term architectural health with the urgent needs of a 2026 commercial launch.
• Technical Domain Expertise: Deep theoretical and practical expertise in numerical optimization and optimal control. You should be highly proficient in areas such as Model Predictive Control, nonlinear programming, and convex optimization applied to real-time trajectory generation.
• Advanced Motion Planning: Significant experience solving motion planning problems under uncertainty, including interaction-aware planning with dynamic agents and complex environmental constraints. Experience with Machine Learning based approaches is a significant plus.
• Full-Stack Robotics Context: Strong intuition for the entire robotics stack. You understand how upstream (Perception, Prediction) and downstream (Control, Actuation) systems impact trajectory feasibility and fleet safety.
• Production-Grade Software Development: Expertise in Modern C++ development within a Linux environment, with a focus on writing high-performance, real-time, and safety-critical code.
• Tooling Proficiency: Fluency in Python for rapid prototyping, data analysis, and visualization. Experience applying AI-augmented development workflows to large-scale Modern C++ codebases (e.g., agentic tools for multi-file refactoring, automated test generation, semantic code navigation, and code review acceleration) is strongly preferred. Experience with SQL or big-data frameworks for fleet-scale performance analysis is a significant plus.
• Influence & Clarity: Exceptional communication skills, with the ability to translate complex mathematical concepts into actionable plans for cross-functional stakeholders and executive leadership.
• Educational Foundation: Master’s or PhD in Robotics, Computer Science, Electrical/Mechanical Engineering, or a related quantitative field.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

Mission Summary:

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery.

As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of this engine: designing massive multimodal Teacher models that understand the world, and distilling them into hyper-efficient Student models that can scour exabytes of data in near real-time. You will work at the intersection of large-scale representation learning, retrieval optimization, and reasoning systems. Your work will directly influence how we compress knowledge into efficient encoders for fast search, and how we apply reinforcement learning to optimize data discovery workflows and intelligent querying. By building smarter mining tools, you will accelerate the entire model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• 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 Discover: 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.

What We're Looking For:

• BS in Computer Science, Machine Learning, or related field, or equivalent professional experience.
• 6+ years of hands-on experience in machine learning engineering, with a focus on model post training, optimization, and deployment.
• Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models.
• Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning.
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong software engineering fundamentals: testing, CI/CD, containerization, and system design.
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference.
• Demonstrated ability to ship production-grade ML systems and mentor team members.
• Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or related field.
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning.
• Background in autonomous driving, robotics, or real-time decision-making systems.
• Familiarity with multimodal learning, sensor fusion, or embodied AI.
• Experience building active learning loops, using the model to find the data that breaks the model.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publications or open-source contributions in RL, distillation, or efficient ML.
  
  

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.