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.

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.

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.

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 Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state-of-the-art foundation models to extract insights from Motional’s driving data, working at the intersection of large-scale representation learning and data retrieval. By building smarter mining tools and efficient data pipelines, you will accelerate the model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

What You'll Do:

• Build and Train ML Pipelines: Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). Focus on implementing supervised and self-supervised learning approaches to improve data search and retrieval.
• Support Model Deployment: Implement scalable data preprocessing and augmentation pipelines. Assist in applying standard optimization techniques (e.g., batch inference, quantization) to ensure models run efficiently in production environments.
• Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows to identify critical driving scenarios.
• Monitor Production Performance: Help build and maintain dashboards to monitor model health, data drift, and system performance. Identify regressions and assist in the operational support of our data mining services.
• Learn and Apply Best Practices: Follow software engineering standards (version control, CI/CD, unit testing) for ML code. Participate in code reviews and contribute to technical documentation.
• Collaborate Across Teams: Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions.
  
  

What We're Looking For (Must-Haves):

• BS or MS in Computer Science, Machine Learning, or a related field.
• Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable training models and evaluating them using standard metrics.
• Strong proficiency in Python with the ability to write clean, modular, and well-documented code.
• Working knowledge of version control, unit testing, and basic software design patterns.
• Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy.
• A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation and deployment basics.
• A proactive learner who thrives on constructive feedback and is eager to grow within a high-stakes engineering environment.
  
  

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.
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV)
  
  

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.

Mission Summary:

Motional is seeking a Senior Full Stack 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 improving the performance and adaptability of our autonomous vehicle (AV) system.

Role Responsibilities:

• 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.

Qualifications (required):

• Desire to design and build user-facing visual applications, and a passion for good UX.
• Strong proficiency in backend development with NodeJS and Python.
• Solid understanding and practical experience with AWS CDK in TypeScript.
• Ability to own and expand backend implementation and architecture of visualization tooling suite.
• Proven ability to collaborate effectively with cross-functional teams in diverse and highly technical domains of expertise.
• Experience in supporting scalable, high-performance APIs and services for visual development, debugging, and annotation systems.
• Ability to help mature internal standards, observability, and deployment processes for backend stack.
• Experience in maintaining and maturing CI/CD infrastructure and processes.
• 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

Preferred:

• Experience with full-stack and front-end development using NextJS and React.
• Demonstrated project management expertise / advanced hands-on understanding of project management agility and operational excellence.
• A love for the continuous delivery of value.

Nice to have:

• Knowledge of and experience with Golang
• UI/UX and product design experience.
• 3D Math and Linear Algebra fundamentals.
  
  

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 seasoned expert in Perception Capabilities, you'll field cutting-edge software products and solutions using state-of-the-art computer vision and deep learning. Your breadth of experience spans critical problem areas like detection, classification, segmentation, and tracking, along with essential supporting functions such as data engines, labeling policies, training pipelines, model optimization, simulation, and testing. You're driven by seeing your work significantly impact the product, taking initiative and owning outcomes.

You're eager to apply these skills to the perception stack of self-driving vehicles. You understand the unique challenges of safety-critical, open-world applications and are committed to ensuring engineering excellence and rigorous evaluation.

You'll devise creative solutions to complex problems, collaborating with interdisciplinary, capability-focused teams across the organization to design, implement, and validate end-to-end solutions.

What you’ll be doing:

• Lead the development and implementation of solutions that span across perception components and directly interface with downstream modules in the autonomy system.
• Analyze the performance of the perception subsystem to proactively identify potential limitations with respect to a set of system-level capabilities. Document potential hazards and lead the cross-team efforts to eliminate or mitigate them.
• Collaborate on innovative solutions to address “long tail” challenges. For example, developing techniques for synthetic data generation, advanced data mining, or leveraging language embeddings in our onboard models.
• Depending on your background and interests, you may also help optimize and deploy models to the vehicle, ensuring they operate within the constraints of our onboard hardware and meet our functional requirements and interface specifications.
• Interface with systems and safety engineers, test engineers, and data analysts to ensure appropriate requirements, test coverage, and performance metrics

What We’re Looking for:

• Master's or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field; or equivalent industry experience
• Strong leadership skills in executing large, complex technical initiatives
• Track record of successful interdisciplinary work and product-focused engineering
• Broad understanding of Deep Learning algorithms, architectures, and applications
• Experience designing, training, and analyzing neural networks for applications relevant to perception
• Fluency in Python, including common libraries for deep learning (PyTorch), scientific computing, and data analysis and visualization
• Advanced knowledge of software engineering principles, including software design, source control management, build processes, code reviews, and  testing methods 
• Excellent communication and interpersonal skills
• Experience mentoring and leading others

Bonus Points:

• Experience shipping a “real” product and balancing ongoing support with next-generation innovation
• Experience developing safety-critical products or components
• Publications in relevant conferences (CVPR, ICML, NeurIPS, etc.)
• Strong programming skills in C++ and/or CUDA programming
• Experience with TensorRT and model quantization

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.

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.

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.

You’re an engineer who is excited to work on cutting-edge software products and solutions using state-of-the-art computer vision and deep learning. You have a solid foundation in areas like detection, classification, segmentation, or tracking—whether through previous roles, internships, or research—and you’re eager to apply those skills in an impactful, production-focused environment.

You’re curious, motivated, and ready to learn from experienced teammates. You enjoy solving challenging technical problems, asking thoughtful questions, and collaborating with others to build high-quality software.

You’re excited about contributing to the perception stack for self-driving vehicles and learning what it means to build technology for safety-critical, open-world applications.

What you’ll be doing:

• Contribute to the design, development, and improvement of perception components that support downstream modules within the autonomy system.
• Help analyze perception system performance to identify areas for improvement and participate in efforts to address them.
• Work with teammates to explore creative approaches to “long-tail” challenges—for example, experimenting with synthetic data, data mining techniques, or new model architectures.
• Depending on your background and interests, you may also help optimize and deploy models to the vehicle, ensuring they operate within the constraints of our onboard hardware and meet our functional requirements and interface specifications.
• Interface with systems and safety engineers, test engineers, and data analysts to ensure appropriate requirements, test coverage, and performance metrics

What We’re Looking for:

• Master's or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field; or equivalent industry experience
• Solid understanding of fundamental deep learning concepts and computer vision techniques
• Experience building, training, or evaluating neural networks
• Proficiency in Python and familiarity with libraries like PyTorch or similar
• Exposure to software engineering fundamentals—version control, code reviews, testing, and collaborative development
• Clear communication skills and willingness to ask questions, share ideas, and work with others

Bonus Points:

• Previous experience with sensor fusion and/or previous exposure to the AV industry
• Experience shipping a “real” product and balancing ongoing support with next-generation innovation
• Exposure to safety-critical products or components
• Publications in relevant conferences (CVPR, ICML, NeurIPS, etc.)
• Strong programming skills in C++ and/or CUDA programming
• Experience with TensorRT and model quantization

Mission:

We are seeking a highly skilled and motivated Principal Engineer, Technical Lead to lead our large-scale AI model and software evaluation framework – Ground Truth Regression. The ideal candidate will have a strong background in data engineering, machine learning principals and leadership, with experience in developing and deploying advanced data analysis and large scale orchestration frameworks.

At Motional, large scale orchestration for data analysis plays a critical role in delivering our ML-centered autonomous driving vehicle. Our robo-taxi ML Models and software are developed by hundreds of developers and deployed multiple times a day – The Ground Truth Regression team provides the large scale perception analysis framework for validating all changes to perception which can impact the autonomous vehicle behavior. 

The GTRegression team validates the end impact of ML and software changes to the autonomous vehicle while those changes are in development, providing the tools for Root Cause Analysis, and safety consideration. We monitor for model errors, anomalies, rare objects & long-tail driving scenarios across thousands of driving hours. The team develops the full stack – AWS Kubernetes orchestration framework, ReSimulation, model metrics, regression reports, and deep dive analysis UI’s. The team works together with all other perception and ML teams to develop the metrics and analyze results.

What You'll Do: 

• Lead a team of engineers with a diverse skill set to deliver critical software supporting the development of the autonomous vehicle ML models and software.
• Drive innovation by researching and developing new large scale data warehousing and analysis.
• Own large-scale data analysis workflows that surface unexpected impacts of planned and unplanned changes to the ML models and software.
• Build high-quality datasets to improve ML products through training & edge case validation.
• Provide statistical depth on model performance & generalization through rigorous error and change analysis.
• Provide technical leadership and mentorship to the team, fostering a culture of collaboration and continuous improvement.
• Manage project timelines, resources, and deliverables to ensure successful project execution.

What We're Looking For:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Excellent leadership, communication, and problem-solving skills.
• Proven record leading a small diverse dedicated team supporting a large user base.
• Experience in machine learning, data mining, and statistical analysis.
• Proven experience in developing and deploying large scale cloud frameworks.
• Strong programming skills in Python.
• Experience with large-scale data processing and distributed computing technologies.
  
  

Bonus Points:

• Ph.D. in Computer Science or a related field.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Experience with machine learning in the autonomous driving domain.
• Publications or contributions to the AI/ML community.

Mission:

We are seeking a highly skilled and motivated Senior Data Analysis Engineer for our large-scale AI model and software evaluation framework – Ground Truth Regression. The ideal candidate will have a strong background in data engineering, machine learning principals and statistical analysis. 

At Motional, large scale orchestration for data analysis plays a critical role in delivering our ML-centered autonomous driving vehicle. Our robo-taxi ML Models and software are developed by hundreds of developers and deployed multiple times a day – The Ground Truth Regression team provides the large scale perception analysis framework for validating all changes to perception which can impact the autonomous vehicle behavior. 

The GTRegression team validates the end impact of ML and software changes to the autonomous vehicle while those changes are in development, providing the tools for Root Cause Analysis, and safety consideration. We monitor for model errors, anomalies, rare objects & long-tail driving scenarios across thousands of driving hours. The team develops the full stack – AWS Kubernetes orchestration framework, ReSimulation, model metrics, regression reports, and deep dive analysis UI’s. The team works together with all other perception and ML teams to develop the metrics and analyze results.

What You'll Do:

• Work with ML Engineers and Autonomy Software Developers to develop new data analysis metrics and KPI’s for the validation of the autonomous vehicle performance.
• Drive innovation by researching and developing new large scale data analysis systems.
• Own large-scale data analysis workflows that surface unexpected impacts of planned and unplanned changes to the ML models and software.
• Build high-quality datasets to improve ML products through training & edge case validation.
• Provide statistical depth on model performance & generalization through rigorous error and change analysis.

What We're Looking For:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Strong programming skills in Python, SciPy, and related data analysis frameworks.
• Experience in machine learning, data mining, and statistical analysis.
• Experience with large-scale data processing and distributed computing technologies.
• Strong communication and software development skills: goal setting, design documentation, test frameworks, collaborative cross team development.
  
  

Bonus Points:

• Ph.D. in Computer Science or a related field.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Experience with machine learning in the autonomous driving domain.
• Publications or contributions to the AI/ML community.