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 Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack.

What You'll Do:

• Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs.
• Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters.
• Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment.
• Elevate Engineering Excellence: Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning.
• Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges.
• Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design.
  
  

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

• BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)
• 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems
• Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)
• Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)
• Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency
• Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams
  
  

Bonus Points (Nice-to-Haves):

• MS/PhD in Computer Science, Machine Learning, or a related field.
• Background in autonomous driving, robotics, or complex real-time decision-making systems.
• Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning.
• Familiarity with multimodal learning, sensor fusion, or large foundation models.
• Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms.
• Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms.
  
  

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’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:

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.

Why this role is exciting

This is a high-impact role at the intersection of data science, mapping, and real-world mobility systems. You’ll work on the data and algorithms that define how Vay’s vehicles navigate cities safely and efficiently within our Operational Design Domain (ODD). Your work will directly unlock our ability to scale to new cities, improve routing performance, and solve complex real-world challenges in a rapidly evolving mobility environment.

What you’ll be driving

In this role, you will play a critical part in advancing Vay’s mapping and routing capabilities using data science and machine learning approaches.

• Develop automated approaches to map augmentation using video, connectivity, trip data, and open datasets such as OpenStreetMap.

• Build data science solutions that accelerate map creation and updates for new cities where Vay launches.

• Design and develop routing simulation tools to discover optimal strategies for improving ETA and ATA accuracy.

• Lead and contribute to initiatives within the Operational Design Domain (ODD) data science pod.

• Identify and automate inefficiencies in map generation workflows to enable faster and more frequent updates.

• Work cross-functionally with engineering, operations, and product teams to implement and deploy production-grade solutions.

• Help scale the impact of the data science team by driving key initiatives and reducing knowledge concentration on critical ODD topics.

What are we looking for

We’re looking for a data scientist who enjoys solving complex real-world problems and turning experimental ideas into production systems that power next-generation mobility.

• Strong experience applying data science or machine learning techniques to real-world problems.

• Ability to write high-quality, production-grade code.

• Experience building and deploying applied data science solutions across the full lifecycle—from concept to implementation and rollout.

• Strong Python experience and familiarity with modern data science workflows (e.g., Jupyter, GitHub, cloud compute environments).

• Experience working with geospatial data, routing systems, or map-based datasets is highly valuable.

• Comfortable working cross-functionally and owning complex initiatives in a fast-moving environment.

Nice to have

• Prior engineering experience or strong software engineering foundations.

• Experience with technologies such as Spark, TensorFlow, PyTorch, or similar ML frameworks.

Why choose Vay

💪 A global team of smart, motivated people from 30+ countries who love tackling hard problems and turning bold ideas into reality.

🌍 Huge scope for impact in a fast paced environment

💰 ESOP stock options: A stake in Vay’s future – not just a salary.

🌴 Unlimited Paid Vacation Days

🎫 401(k) programme

🏥 Health, Dental & Vision Insurance 

🚗 All Vay team members receive 30% off their Vay rides

🏋️‍♂️ Wellness Hub free gym access & wellness app subscriptions

🛍️ Exclusive external discounts

💼 $350 Home office budget 

✈️ Relocation financial assistance when relocating to Las Vegas 

🎊 Regular team events throughout the year