Position Overview

Woolpert is hiring a Senior Software Engineer to join our dynamic Atlas Labs team supporting our Geospatial sector. This position reports to the Director of Product and AI. Atlas Labs oversees technology development for Woolpert’s Geospatial sector, including various types of AI model development, R&D, software infrastructure, and user-facing products.

We are unable to provide visa sponsorship for this position.  Applicants must already possess work authorization in the country the job is located.

What You Will Do:

• Contribute to the planning, research, design, architecture, development, and testing of Atlas Labs software features. You’ll write code daily, definitely in Python and possibly in C++ or JavaScript.
• Work as part of a cross-functional Scrum team with domain experts in AI/ML, Geospatial Data, Software Engineering, and GIS.
• Collaborate with internal and external users to ensure the correctness, quality, and availability of Atlas Labs software products.

What You Will Bring:

• Meaningful employment experience working on commercial software products
• Experience writing production code in Python, preferably dealing with large datasets
• Demonstrated experience or academic output in a relevant domain:
• Geospatial Data Processing
• Computer Vision
• AI & Machine Learning
• LiDAR
• Robotics/Self-Driving/Autonomy
• General familiarity with relevant technologies:
• Linux, Docker, and GitHub
• AI/ML frameworks (Tensorflow, Pytorch)
• Cloud platforms (Azure, AWS, Google)

Nice to Have:

• Familiarity with GIS applications like ESRI, QGIS, Google Earth
• Familiarity with orchestration frameworks like Kubernetes, AirFlow, MLFlow
• Programming experience in C++ or JavaScript
• Experience with Agile processes like Scrum

Benefits You Will Love

Certified as a Great Place to Work, Woolpert is one of the fastest-growing architecture, engineering, and geospatial companies in the world. With over 75 offices and countless projects around the globe, you’ll work on meaningful assignments that improve quality of life around the world. In addition to offering competitive pay, we provide employees with a robust set of unique benefits, including:

• Freedom to Work program: Set your own schedule and location (as appropriate).
• Principal program: Earn the opportunity to become an owner of the firm.
• Flexible paid time off/vacation: Take time off when it makes sense for you and your team.
• Career development: Explore a wide range of learning and growth opportunities within and across industries.
• Health, life, vision, and dental insurance: Cover all your medical bases.
• Paid sick time, retirement plan, fitness reimbursement, pet insurance, identity protection, technology stipend, employee assistance program, and more!

#LI-Hybrid

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:

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:

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.

About us

TeleTracking began with a simple but powerful belief that no one should wait for the care they need. More than a slogan, it’s a promise to continually improve healthcare.

TeleTracking builds groundbreaking technology incorporating deep clinical expertise. Our solutions are used in the nation’s largest healthcare systems and around the world to positively impact patients, families and communities.

What’s your contribution to the TeleTracking story?

When you chose to bring your passion and skills to helping achieve our purpose, you’ll be part of a team that understands that there’s a human life behind every data point.   Your skills, curiosity, and compassion—will help fuel our innovation and achieve the TeleTracking promise of revolutionizing modern healthcare. 

About the role

We are seeking a highly skilled and experienced Senior Machine Learning Engineer to join our innovative team at TeleTracking Technologies, focusing on improving patient care. The ideal candidate will have a strong background in developing, deploying, and optimizing machine learning models, as well as expertise in data ingestion and modeling techniques. This role requires a deep understanding of hospital operations, healthcare data, clinical workflows, and regulatory requirements (such as HIPAA or GDPR). The successful candidate will be passionate about leveraging advanced technologies to enhance patient care and operational efficiency. 

What you will do

• Design, develop, and implement machine learning and deep learning models to address hospital-specific challenges such as patient flow optimization, resource allocation, bed management, and predictive analytics for patient outcomes.
• Build and optimize data ingestion and modeling pipelines as needed
• Utilize domain driven techniques and design patterns to build and contribute to technical
• design.
• Collaborate with cross-functional teams including data scientists, software engineers, clinicians, hospital administrators, and experts in TeleTracking Technologies to identify and develop high-impact machine learning solutions.
• Work with large-scale healthcare and hospital datasets including structured data (EHRs, hospital operational data), unstructured data (clinical notes, imaging).
• Ensure data privacy and security, adhering to healthcare regulations such as HIPAA and GDPR, especially when working with sensitive hospital data.
• Mentor junior engineers and data scientists, providing guidance on machine learning techniques, particularly those relevant to hospitals and healthcare systems.
• Monitor, troubleshoot, and enhance the performance of deployed models using MLOps best practices, ensuring they operate effectively in hospital environments.
• Write technical architectural and design documents.

What we look for

• Proven experience in end-to-end design and deployment of machine learning models from ideation to production in healthcare or similar settings.
• Strong programming skills and experience with object or component-oriented development software, one or more of: Python or R, with proficiency in ML frameworks, one or more of: TensorFlow, PyTorch, or Scikit-learn.
• Expertise in NLP, computer vision, or other specialized machine learning techniques applicable to healthcare and hospital environments.
• Deep knowledge of a scripting or statistical programming language (Python preferred). Ability to efficiently work with very large datasets and deal with non-standard machine learning datasets (class-imbalances, sparse matrices, etc.)
• Assess model performance; train multiple models; carry out tuning.  Run A/B tests on models.
• Comfortable writing complex SQL queries and developing python packages.
• Experience with cloud-based management and hosting, one or more of: AWS, Azure, GCS, CloudFormation, Terraform, or Ansible. Interest in developing services as well as the underlying infrastructure
• Experience with database management system software, one or more of: Oracle, MSSQL, MongoDB, MySQL, DynamoDB, or PostgreSQL. 
• Experience with Version Control Software, one or more of: git, Mercurial, CVS, TFS, or Subversion. 
• Strong understanding and experience executing several software development methodologies and life cycles. Ability to understand and translate business requirements into technical specifications.
• Experience with agile development practices.
• Excellent written and oral communication skills. Adept and presenting complex topics, influencing, and executing with timely / actionable follow-through.
• Strong analytical and problem-solving skills with the ability to convert information into practical training deliverables. Uses rigorous logic and methods to solve difficult problems.
• Knowledge of clinical workflows and hospital operations, and how technology can enhance efficiency and patient care.
• Familiarity with healthcare-specific machine learning challenges, such as data imbalance, longitudinal data, and real-time processing in hospital environments.
• Be an active listener, probe requirements for all projects from relevant stakeholders, stay nimble and willing to produce rapid iterations.

Education

• Bachelor's degree in computer science, Data Science, Machine Learning, Artificial Intelligence, or a related field; 7 or more years of experience.
• Master's or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field; 5 or more years of experience. (preferred)

Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future.

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:

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 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:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems, and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Mission Summary:

We are seeking an experienced and visionary Principle Level Tech Lead Manager to build and lead our new Machine Learning (ML) Acceleration team. This pivotal role will drive the strategy, development, and execution of initiatives aimed at significantly accelerating ML model training. The ultimate goal is to drastically reduce the development cycle for new ML models and enable rapid hot-patching for issues within our deployed autonomous vehicle services.

You will be a hands-on leader, blending deep technical expertise in ML systems and performance optimization with strong leadership and people management skills. You will recruit, mentor, and grow a high-performing team of engineers, fostering a culture of innovation, collaboration, and continuous improvement.

What you'll be doing:

• Team Leadership & Management:
• Build, lead, and manage a high-performing team of ML and infra engineers focused on acceleration.
• Provide technical guidance, mentorship, and career development opportunities to team members.
• Foster a collaborative and inclusive team environment.
• Define team goals, priorities, and roadmap in alignment with company objectives.
  
  
• Technical Strategy & Execution:
• Define the technical vision and strategy for ML acceleration across the organization.
• Identify and evaluate cutting-edge technologies and methodologies for speeding up ML training, including but not limited to data pipeline optimization, large scale distributed training, data loader optimization, hardware acceleration, and model optimization techniques.
• Design, develop, and implement scalable and efficient ML acceleration solutions.
  
  
• Cross-functional Collaboration:
• Collaborate closely with ML research, ML Training platform, and product teams to understand their needs and integrate acceleration solutions seamlessly.
• Communicate complex technical concepts and strategies to both technical and non-technical stakeholders.
• Act as a technical expert and advocate for ML acceleration initiatives across the company.
  
  
• Impact & Measurement:
• Regularly measure and report on the impact of acceleration efforts.
• Continuously seek opportunities for further optimization and innovation.
  
  

What we're looking for:

• Experience:
• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
• 8+ years of experience in software engineering, with at least 5+ years focused on Machine Learning systems and model performance optimization.
• 3+ years of experience in a technical lead or management role, with a proven track record of building and leading high-performing teams.
• Extensive experience with large-scale ML model training and deployment, ideally in a production environment.
• Strong understanding of distributed systems and cloud computing platforms (e.g., AWS, GCP, Azure).
  
  
• Technical Skills:
• Deep expertise in ML frameworks such as PyTorch or JAX.
• Proficiency in performance profiling and optimization techniques for Deep Neural Networks.
• Strong programming skills in Python; C++ experience is a plus.
• Knowledge of MLOps principles and practices.
  
  
• Leadership Skills:
• Exceptional leadership, communication, and interpersonal skills.
• Ability to attract, hire, and retain top engineering talent.
• Proven ability to drive complex technical projects from conception to completion.
• Strong problem-solving skills and a proactive, results-oriented mindset.
• Ability to thrive in a fast-paced, dynamic environment.

Motional’s global headquarters are located at 100 Northern Avenue in Boston, MA. Nestled in the bustling Seaport district with sweeping views of Boston Harbor and downtown Boston, the office is located close to major transit lines and a quick walk to various restaurants and popular attractions.

Mission Summary:

Motional’s CORE team is responsible for our vehicle’s Compute and Onboard Runtime Environment. We are creating a world leading AI compute platform for autonomous vehicles. This is the system that executes the software and neural networks that make our vehicles autonomous. 

The Next-Gen Technologies team is part of CORE. We work at the intersection of software engineering, machine learning, sensors, and hardware compute platforms to evolve Motional’s on-board vehicle architecture. If you are a software engineer and love the idea of working on embedded AI hardware and software systems to create the next generation of autonomous vehicles, we would love to talk with you. 

What You’ll Be Doing:

As a senior engineer in the Next-Gen Technologies team, you will help us improve the compute performance of our current generation and next-generation autonomous driving systems through participating in full lifecycle development from idea to proofs-of-concept to production. 

Specifically, you will:

• Focus deeply on ML model deployment, integration of multiple ML models, and ML model optimization on embedded compute platforms.
• Dive deep into the full ML software stack. Analyze ML workload performance on a variety of hardware processors, optimize ML models, improve ML software, and help us continually improve our stack through the application of efficient and effective ML approaches. 
• Design, develop, test, integrate, and optimize software and tools on a variety of ML compute architectures.
• Collaborate with deep learning experts in perception, prediction, and other autonomous driving application areas to enable algorithms on GPU, NPU, and other ML accelerator architectures.
• Optimize the utilization of GPU/NPU resources and sharing of GPU/NPU access across multiple programs running on the same system.
• Lead designs to determine the needs of the system and how to best meet those needs through continually improving our ML software stack.
• Advise peers and management on technical matters.

What We’re Looking For:

• Experience with machine learning accelerators, including GPUs, NPUs, TPUs, and their programming environments, including CUDA, TensorRT, or similar technologies.
• Strong experience with modern C++ development in a Linux environment.
• Experience with parallel and high-performance computing.
• Comfortable with experimentation and evaluating different options as we work towards finding solutions that work. 
• A degree in Software Engineering, Computer Science, Electrical or Electronic Engineering, or similar technical field of study, or you have equivalent knowledge gained through your practical experience.

Preferred, but not required:

• Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks. 
• Experience with embedded systems development for ARM-based system-on-chip architectures.
• Experience working in a MLOps or DevOps environment.
• Passion for self-driving technology and its potential for positive impact on the world.
  
  

This role is hybrid from our Boston office. It requires two in-office days each week, ideally Tuesday and Thursday.

On Behaviors, you'll have the opportunity to work with world-class ML engineers and research scientists to make self-driving vehicles a reality and create positive social impact. Our team works on the tech stack responsible for understanding highly complex driving scenes and generating safe, comfortable driving trajectories for our robotaxi using the latest deep neural networks. We are looking for engineers who are passionate about Level 5 autonomous driving, excited by intellectual challenges, and interested in pursuing career growth with a fast growing company.

What You’ll Be Doing:

You will be directly responsible for training, evaluation, and deployment of our ML-based scene understanding, behavior prediction, and planning models. Day to day responsibilities include:

• Designing and executing high value experiments based on collaborative input from other machine learning engineers
• Prototyping and implementing continuously improving metrics to evaluate the performance of our behavioral models across many desired scenarios and robotaxi capabilities
• Timely release of major model updates, including analysis of both off-line and on-road evaluation data
• Staying up to date with the latest trends in our fast moving industry, and proposing new architectures and network designs based on published literature
• Maintaining a high quality training and evaluation code-base, including portions of our training stack dedicated to dataset generation and evaluation

What We’re Looking For:

• In-depth understanding of common Machine Learning and Deep Learning algorithms
• Experience designing, training, and analyzing neural networks for at least one of the following applications: motion planning, object detection, sensor fusion, motion prediction, and/or multi-object tracking
• Strong track record of tackling less well-defined problems, and executing efficiently to deliver the successful outcome
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, and  testing methods
• Experience with PyTorch or other python-based deep learning frameworks
• Experience working with large data sets and deriving insights from data
• Experience mentoring and collaborating with others

Education
Masters or PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience

Bonus Points

• Publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• PhD in machine learning, computer vision, or robotics vision
• Experience working on autonomous vehicles or related fields
• Experience in deploying models into real-world environments
• Strong programming skills in C++ and/or CUDA programming
• This opportunity can support remote work within the United States, with occasional travel.

Working Conditions

While performing the duties of this job, the employee is frequently required to convey and detect communication

The employee occasionally required to remain stationary at times for up to 50% of working time, move in designated working areas; & operate and use office equipment

The employee may occasionally move up to 50 pounds

Motional is seeking a distinguished Machine Learning Engineer to join its Autonomy team in Behaviors. As a member of our team you will develop key features for our autonomous driving platform. In this role, you will apply machine learning to a mix of prediction, planning, and control problems. The candidate is required to have hands-on experience and deep knowledge in machine learning. Understanding of deep neural networks, autonomous vehicles, and domain adaptation is highly desirable. You should have strong research, SW development, communication, interpersonal, and analytical skills. 

What you’ll be doing:

• Researching, implementing, and evaluating deep-learning-based methods for prediction and planning for Motional’s Autonomous Vehicle products.
• Leading, Designing, running, and analyzing experiments and testing to evaluate the efficiency of our solutions on real-world data.
• Partnering with system software engineering specialists to ship industrial strength ML models.
• Communicating and collaborating with multi-functional teams.

What We're Looking For: 

• BS/MS/PhD in computer science, electrical engineering, mechanical engineering, applied math, or related fields (or equivalent experience)
• 15+ years of proven experience building ML systems for autonomous vehicles or similar robotics applications
• Deep understanding of large language models (LLM) and transformers
• Hands on experience building large scale production ML systems and deploying them at scale
• Excellent leadership and track record for innovation to help us build the next generation of our ML based planning solutions
• Experience with deep neural network (DNN) training, inference and optimization in leading frameworks (Pytorch, Tensorflow, TensorRT, etc.).
• Excellent understanding of the mathematical foundations of machine learning and deep learning.

Bonus Points: 

• Prior experience as a ML planning lead
• Proven publication record in ML for planning, vision, or related fields
• Prior experience building and deploying vision language action or chain of thought models

On Behaviors, you'll have the opportunity to work with world-class ML engineers and research scientists to make self-driving vehicles a reality and create positive social impact. Our team works on the tech stack responsible for understanding highly complex driving scenes and generating safe, comfortable driving trajectories for our robotaxi using the latest deep neural networks. We are looking for engineers who are passionate about Level 5 autonomous driving, excited by intellectual challenges, and interested in pursuing career growth with a fast growing company.

What You’ll Be Doing:

You will be directly responsible for training, evaluation, and deployment of our ML-based scene understanding, behavior prediction, and planning models. Day to day responsibilities include:

• Designing and executing high value experiments based on collaborative input from other machine learning engineers
• Prototyping and implementing continuously improving metrics to evaluate the performance of our behavioral models across many desired scenarios and robotaxi capabilities
• Timely release of major model updates, including analysis of both off-line and on-road evaluation data
• Staying up to date with the latest trends in our fast moving industry, and proposing new architectures and network designs based on published literature
• Maintaining a high quality training and evaluation code-base, including portions of our training stack dedicated to dataset generation and evaluation

What We’re Looking For:

• In-depth understanding of common Machine Learning and Deep Learning algorithms
• Experience designing, training, and analyzing neural networks for at least one of the following applications: motion planning, object detection, sensor fusion, motion prediction, and/or multi-object tracking
• Strong track record of tackling less well-defined problems, and executing efficiently to deliver the successful outcome
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, and  testing methods
• Experience with PyTorch or other python-based deep learning frameworks
• Experience working with large data sets and deriving insights from data
• Experience mentoring and collaborating with others

Education
Masters or PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Physics or a related field; or equivalent industry experience

Bonus Points

• Publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• PhD in machine learning, computer vision, or robotics vision
• Experience working on autonomous vehicles or related fields
• Experience in deploying models into real-world environments
• Strong programming skills in C++ and/or CUDA programming
• This opportunity can support remote work within the United States, with occasional travel.

Working Conditions

While performing the duties of this job, the employee is frequently required to convey and detect communication

The employee occasionally required to remain stationary at times for up to 50% of working time, move in designated working areas; & operate and use office equipment

The employee may occasionally move up to 50 pounds

On our Behaviors team (Prediction and ML Planner), you will have the opportunity to work with world-class ML engineers, whose mission is to make self-driving vehicles a reality and to create a positive social impact. The Behaviors team develops ML models that learn how different agents navigate complex real-world traffic scenarios. We leverage these models to set the behavior of our self-driving vehicle, and predict the behavior of other agents such as vehicles and vulnerable road users. 

We are looking for proven leaders and technical experts who are passionate about Level 4 autonomous driving technology, excited by intellectual challenges, and interested in pursuing career growth with a fast-growing company.

What You’ll Be Doing:

• Define and execute motion planning and prediction projects that improve our self-driving vehicles’ capability to safely, comfortably and legally navigate complex traffic scenarios
• Lead, manage and grow a team of engineers
• Design and lead the implementation of behavior models that leverage the latest advancements in machine learning, generative AI and reinforcement learning
• Productionize and deploy solutions onto autonomous vehicle fleets
• Collaborate with perception, simulation, data platform and integration teams to validate and enhance your products’ on-road performance
• Communicate strategies, progress and challenges to executive leadership

What We’re Looking for:

• Masters or Ph.D. in Computer Science or a related technical field; or equivalent industry experience
• Proven leadership skills at executing large, complex technical initiatives
• Extensive experience managing and leading engineers
• Experience with deep learning frameworks such as TensorFlow or PyTorch
• Fluency in Python, including standard scientific computing libraries
• Proven track record of designing, developing and deploying ML solutions for autonomous vehicles or robotics
• Advanced knowledge of software engineering principles including software design, source control management, build processes, code reviews, testing methods 
• Excellent communication and interpersonal skills

Bonus Points:

• Experience with embedded systems and real-time optimization, especially in the autonomous driving industry
• Experience with state of the art generative AI, and/or reinforcement learning, paradigms
• Proven track record of publications in relevant conferences (CVPR, ICML, NeurIPS, ICCV, ICL, etc.)
• Strong programming skills in C++ and/or CUDA programming

About The Role

The AI innovation team at Agility works on building and deploying next-generation robot foundation models and end-to-end policies on humanoid robots. Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to perform different real-world tasks. You will work on a team, running experiments on humanoid robots, and will research and implement methods which can be transferred into production.

About the Work

• Design, train, and deploy robust policies for locomotion, manipulation, and dynamic interactions with the environment.
• Develop core reinforcement learning infrastructure, including scalable training pipelines and evaluation frameworks.
• Design and implement new simulation environments and tasks to support training and deployment of control policies.
• Develop, design, and test imitation learning methods
• Collaborate with Robotics Software and AI engineering teams to develop policies which can be transferred to production

About You

• 3+ years of experience developing and deploying learning-from-demonstration
• Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch.
• Experience with modern learning-from-demonstration tools like DiffusionPolicy
• Experience with robot data collection, training, and testing on hardware to perform manipulation tasks.
• Ability to work collaboratively in a fast-paced environment to deliver safe, high-quality software
• MS in Robotics, Computer Science, or a related field.

Bonus Points

• PhD in Robotics, Computer Science, or a related field.
• Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA).
• Familiarity with robot simulation environments (e.g. Mujoco, Isaac Sim) and sim-to-real transfer techniques.
• Experience with modern reinforcement learning techniques for locomotion, manipulation, and whole-body control
• Experience with writing performant, high quality software in C++

This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The Unified Model development team is a group of highly skilled and experienced professionals who specialize in cutting-edge computer vision and machine learning technology. Together, we collaborate to create advanced detection models that are capable of generating representations of both static and dynamic actors. These representations are then utilized by various downstream autonomy consumers including motion planning, prediction, and localization.

The team's primary focus is on developing compute-efficient, multi-view, multi-sensor deep learning models that can perform a wide range of tasks such as 3D object detection, lane line detection, state estimation, and scene segmentation. The ultimate goal is to take these algorithms from the lab to the road, ensuring that they are optimized for onboard performance and able to function as production-grade vision systems on vehicles.

To achieve this goal, the team constantly stays up-to-date with the latest research literature and pushes the boundaries of what is possible. We are dedicated to developing cutting-edge ML algorithms and models that can help vehicles reason about the world around them in real-time.

What you’ll do: 

• Develop spatio-temporal machine learning models for detection, multi-sensor fusion, segmentation, etc.
• Read literature, analyze raw data, and design state-of-the-art solutions
• Transition solutions from the lab to the test track and public roads to ensure successful production-level implementation.
• Collaborate with perception experts and experienced roboticists on algorithm design, prototyping, testing, deployment, and productization
• Build and maintain industry-leading software practices and principles
• Develop clean and efficient software for perception modules interfacing with other key modules
• Show initiative and be a valued team member in a fast-paced, innovative, and entrepreneurial environment

What you'll need to succeed:

• A Bachelor's or Master's Degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics, or a related field plus 7 years of industry experience.
• Expert-level knowledge and experience in machine learning, with a proven track record of developing and deploying deep learning solutions using PyTorch, Tensorflow, or similar frameworks
• Experience in developing perception systems using sensors such as Camera, Radar, and LiDAR
• Strong experience in computer vision, perception, and point cloud processing
• Proven experience in shipping computer vision software products to industry or consumers
• At least 5 years of development experience in Python/C++ environments.

Level and title will ultimately be determined based one experience and interview performance.

Nice to have (optional):

• Ph.D. with machine learning focus, or equivalent experience
• Experience developing and deploying temporal computer vision models (for instance, video activity recognition or using multiple frames for improved results on other tasks).

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 13 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $179,200 - $268,800 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The Performance Prediction team builds the Machine Learning models, evaluation pipelines, and internal tools that help us understand how autonomy behavior changes across software releases. We work on problems that span behavior classification, ride quality detection, probabilistic trajectory prediction, and release regression analysis.

Our systems support both classical and modern ML approaches. That includes compact learned classifiers such as tree-based models for behavior and ride quality detection, as well as deep learning-based probabilistic prediction models for more complex autonomy tasks. We also build the software around those models: dataset definition, feature generation, training and tuning workflows, offline metrics, experiment tracking, and tools that help engineers inspect regressions at the slice and scenario level.

This role is a strong fit for someone who enjoys building reliable Python systems and applying rigorous ML evaluation methods in a safety-critical domain. In practice, the work is a mix of ML systems development, model and evaluation work, and internal tooling used by partner teams across autonomy.

What you’ll do: 

• Build production software for model training, offline evaluation, and release-comparison workflows
• Develop and improve learned models for performance prediction, including behavior classifiers and probabilistic prediction models
• Design training, validation, and holdout strategies that produce trustworthy results
• Define and track model and release metrics such as precision, recall, F1, ROC AUC, calibration quality, and task-specific forecasting metrics
• Run experiments, tune models, and analyze results with strong statistical rigor
• Build internal tools that help engineers compare software versions, inspect model outputs, and investigate regressions
• Partner with autonomy, simulation, and infrastructure teams to move ideas from prototype to production
• Raise engineering quality through testing, code review, CI, and maintainable interfaces across data, modeling, and product layers

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Strong software engineering skills in Python, including experience building modular, maintainable, well-tested systems in a shared codebase
• Experience developing, training, tuning, or productionizing supervised ML models
• Strong grounding in statistics and experimental design, including experience designing model training and evaluation tests
• Experience selecting and interpreting model metrics, thresholds, and tradeoffs for real-world decision-making
• Experience with ML tooling such as PyTorch, scikit-learn, or similar frameworks
• Experience working with large datasets using SQL, pandas, and NumPy
• Strong communication skills and the ability to work effectively across software, ML, and autonomy teams

Nice to have: 

• PhD in Computer Science, Machine Learning, Statistics, Robotics, or a closely related field is preferred
• Equivalent research-heavy industry experience is highly valued
• Experience with probabilistic forecasting, trajectory prediction, sequential modeling, or graph-based models
• Experience with classical ML methods such as random forests, gradient boosting, or calibrated linear models
• Experience with calibration, uncertainty estimation, ablation studies, error analysis, or release regression methodology
• Experience building internal analytics or ML tools with Dash, Plotly, Streamlit, or similar frameworks
• Experience with workflow orchestration or experimentation tools such as Dagster, Airflow, or Weights and Biases
• Experience with Bazel or other large-scale build systems
• Prior autonomous driving experience is helpful but not required. Strong experience with production ML systems and rigorous model evaluation is sufficient

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $179,200 - $268,800 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.  

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The State Estimation team is a group of highly skilled and experienced professionals who specialize in cutting-edge multi-object tracking, scene estimation, and machine learning technology. Together, we collaborate to create advanced Bayesian filters, graph models, and deep learning models that are capable of temporally tracking both static and dynamic actors as well as estimating road features. The State Estimation team is the interface of the perception system to various downstream autonomy consumers including motion planning, prediction, and localization.

The team's primary focus is on developing compute-efficient models and systems that can perform a wide range of tasks such as closed world multi-object tracking, track-to-detection data association, object motion forecasting, uncertainty estimation, lane line temporal smoothing, perception based road shape generation, and open world scene tracking and state estimation. The ultimate goal is to take these algorithms from the lab to the road, ensuring that they are optimized for onboard performance and able to function as production-grade perception systems on vehicles.

To achieve this goal, the team constantly stays up-to-date with the latest research literature and pushes the boundaries of what is possible. We are dedicated to developing cutting-edge tracking algorithms, ML algorithms, and models that can help vehicles reason about the world around them in real-time.

What you’ll do: 

• Develop machine learning models and Bayesian filtering algorithms for multi-object tracking, state estimation, and uncertainty estimation 
• Develop estimation algorithms for road feature estimation such as lane lines and speed limit as well as estimating the road shape. 
• Develop forecasting algorithms for actors in the scene to estimate time-to-collision and threat levels for automatic emergency braking  
• Read literature, analyze raw data, and design state-of-the-art solutions
• Transition solutions from the lab to the test track and public roads to ensure successful production-level implementation
• Collaborate with perception experts and experienced roboticist on algorithm design, prototyping, testing, deployment, and productization
• Build and maintain industry-leading software practices and principles
• Develop clean and efficient software for perception modules interfacing with other key modules
• Show initiative and be a valued team member in a fast-paced, innovative, and entrepreneurial environment

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Relevant knowledge and experience in machine learning, with a proven track record of developing and deploying deep learning solutions using PyTorch or similar frameworks
• Experience in developing multi-object tracking systems using classical algorithms or machine learning algorithms
• Strong experience in deep learning, Bayesian filtering, and optimization algorithms
• Proven experience in shipping perception software products to industry or consumers
• At least 4 years of development experience in Python/C++ environment

Nice to have: 

• Ph.D. with machine learning focus, or equivalent experience
• Experience developing and deploying machine learning models with compute constraints

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $179,200 - $268,800 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.  

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The Latitude AI Autonomy Behavior team is responsible for designing and building systems that allow an autonomous vehicle to understand the current scene around it, predict how the scene will evolve in the future and make decisions on how to respond.  The team focuses on deploying SOTA algorithms in limited compute environments, and writing efficient software architectures that take advantage of hardware optimizations.

You will play a key role in improving the quality and safety of the autonomy system through the optimization and integration of both modern (transformer, etc…) and classical ML (Random Forest, SVM, etc..) models. Our models utilize time series data to solve regression, generation, and classification tasks.  Improving model inference and latency on our target hardware allows us to deploy deeper and more powerful models and improve the vehicle’s ability to reason in complex situations.

What you’ll do: 

• Integrate and optimize algorithms and models for predicting the future behavior of other traffic participants in real time and planning a proper response
• Measure the statistical properties and quality of predictions and plans
• Build a system that safely handles nominal and out-of-distribution driving events
• Evaluate and verify the system's performance on real-world and simulated data
• Improve and deliver reliable software through continuous integration, automated testing and code reviews
• Collaborate across different teams to jointly achieve objectives in improving system performance
• Monitor system performance and ensure designs and implementation stay within resource constraints

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
• Fluency in modern C++ including good software design skills and experience with Python
• Familiarity with PyTorch, ONNX and deployment/optimization of models is desired
• Knowledgeable in areas pertaining to probabilistic inference, probability theory, statistics
• Application of Machine Learning in classification, regression, time series forecasting
• Willingness to dive into anything, but know when to ask for cross team collaboration
• Industry experience writing production-quality, performance-critical code, and maintaining large codebases is desired
• Strong written and verbal communication skills are required

What we offer you:

• Competitive compensation packages
• High-quality individual and family medical, dental, and vision insurance
• Health savings account with available employer match
• Employer-matched 401(k) retirement plan with immediate vesting
• Employer-paid group term life insurance and the option to elect voluntary life insurance
• Paid parental leave
• Paid medical leave
• Unlimited vacation
• 15 paid holidays
• Daily lunches, snacks, and beverages available in all office locations
• Pre-tax spending accounts for healthcare and dependent care expenses
• Pre-tax commuter benefits
• Monthly wellness stipend
• Adoption/Surrogacy support program
• Backup child and elder care program
• Professional development reimbursement
• Employee assistance program
• Discounted programs that include legal services, identity theft protection, pet insurance, and more
• Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives

Learn more about Latitude’s team, mission and career opportunities at lat.ai!

The expected base salary range for this full-time position in California is $179,200 - $268,800 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.

Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.

About Us

Are you ready to build the future of supply chain? At Gather AI, we’re not just creating software; we’re pioneering a new era of warehouse intelligence. We’ve developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining “on-time, in full” delivery.

If you’re looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We’re leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.

About the Team

You’ll lead the Machine Learning and FPT teams, working closely with the Director of Cloud Engineering and Director of Autonomy. Cross-departmentally, you’ll collaborate with Product and Business leadership to translate ML capabilities into product value. This is a high-visibility role with a direct line to executive leadership and significant influence over product direction.

About the Role

We are looking for a Director of Machine Learning to define our ML strategy, lead our computer vision organization, and drive the next phase of ML maturity at Gather AI. You will assess existing ML and CV capabilities, identify gaps and opportunities, and establish a clear technical vision for how machine learning will power our warehouse intelligence platform over the next 12–18 months.

The ideal candidate brings deep computer vision expertise, a track record of shipping production ML systems, and the leadership experience to build and scale a world-class ML team.

What You’ll Do

• Define and own the ML strategy and technical roadmap for Gather AI, aligned with product and business objectives
• Lead and grow the Machine Learning and FPT teams, establishing a culture of rigor, experimentation, and production-quality delivery
• Drive improvements to core computer vision models (object detection, segmentation, OCR) used across our drone and MHE Vision products
• Build out MLOps infrastructure — model training pipelines, deployment, monitoring, and CI/CD for ML workloads
• Collaborate with the Director of Cloud Engineering and Director of Autonomy to ensure ML systems integrate seamlessly into the broader platform
• Partner with Product and Operations to translate customer needs into ML-driven product capabilities

What You’ll Need

• 10+ years building and scaling production ML or computer vision systems
• 5+ years managing and growing ML engineering teams
• Deep expertise in computer vision: object detection, image segmentation, OCR, and CNN architectures
• Strong Python and PyTorch (or TensorFlow) proficiency, plus a track record of shipping ML models to production at scale
• MS or PhD in Computer Science, Machine Learning, or a related field (strong industry track record considered in lieu of advanced degree)

Nice to Have

• Experience with drone, robotics, or autonomous systems perception
• Edge ML deployment experience (ONNX, TensorRT, mobile/embedded inference)
• Familiarity with warehouse, logistics, or supply chain domain
• Experience with AWS or GCP ML services (SageMaker, Vertex AI)

About Us

Are you ready to build the future of the supply chain? At Gather AI, we’re not just creating software; we’re pioneering a new era of warehouse intelligence. We’ve developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining “on-time, in full” delivery.

If you’re looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We’re leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.

About the Team

This role work closely with a Senior Machine Learning Scientist and our Lead Machine Learning Infrastructure Engineer, alongside cross-functional partners in Cloud Engineering, Product, and Operations. The role spans both deep technical work and collaboration with teams closest to the customer. As a Principal-level IC, you'll have real input into technical direction — not just executing on decisions, but helping make them. 

About the Role

We are looking for a Principal Machine Learning Scientist to advance the state of our computer vision systems for warehouse inventory scanning. You will work across the full ML lifecycle — from research and model architecture through training, deployment, and production monitoring — with a focus on delivering measurable improvements to detection, segmentation, and OCR accuracy across our drone and MHE Vision products.

This role is ideal for a senior ML practitioner who wants principal-level impact on a core product, a variety of deployment targets (cloud, on-prem, embedded), and real-world data with tangible business outcomes.

What You’ll Do

• Advance core computer vision model performance (object detection, segmentation, OCR) for warehouse inventory scanning across drone and MHE Vision platforms
• Own the full ML lifecycle from research and experiment design through production deployment and monitoring — applying rigorous ablation studies and SOTA methodology
• Collaborate with the ML infrastructure team on model optimization and deployment across cloud and edge inference targets (ONNX, TensorRT, quantization)
• Work with Operations and Product to understand customer needs and translate them into ML improvements with measurable business impact
• Provide technical leadership and mentorship to the ML team, raising standards for experiment design, model evaluation, and production readiness
• Explore next-generation perception capabilities, including embedded and on-prem inference optimization for new deployment targets

What You’ll Need

• 10+ years of experience in machine learning or computer vision
• Deep expertise in CNNs, object detection, image segmentation, and OCR using PyTorch (preferred) or TensorFlow
• Strong Python proficiency and software engineering fundamentals; hands-on experience with OpenCV and GPU computing
• Track record of delivering production ML systems at scale, including model training, evaluation, and deployment
• MS or PhD in Computer Science, Machine Learning, Robotics, or a related field

Nice to Have

• Experience with drone, robotics, or autonomous systems perception
• Publications in top vision or robotics conferences (CVPR, ICCV, ICRA, NeurIPS, CoRL)
• Experience designing and deploying models for real-time inference on constrained compute platforms
• Warehouse, logistics, or supply chain domain experience

Agility Robotics is deploying humanoid robots that are solving real-world challenges in logistics and manufacturing. Perceiving and understanding the world is critical to Digit’s success in these applications. The Perception team is looking for a staff machine learning engineer to own the design and development of object detection and tracking algorithms.

Responsibilities:

• As a technical lead, you will own the architecture and technical roadmap for object perception systems used by the robot in production
• Design, develop, and deploy machine learning algorithms for multi-object detection, scene understanding, and 6-DoF object pose estimation
• Evaluate and drive adoption of state-of-the-art perception models
• Promote best practices in architecture, design, and testing to deliver high-quality, scalable software
• Optimize deep neural networks and associated data processing to run efficiently on embedded systems
• Collaborate with navigation, manipulation and hardware teams to align perception capabilities with product requirements

Requirements:

• 5+ years of experience deploying machine learning-based object detection algorithms on mobile robots with at least 2 years of experience in a technical leadership role
• Proficiency in related technical areas such as (but not limited to) deep convolutional neural networks, multi-object tracking, data association, supervised learning and pose estimation
• Strong mathematical fundamentals in linear algebra and numerical optimization and familiarity with core geometric concepts in computer vision
• Familiarity with common computer vision and machine learning libraries such as (but not limited to) PyTorch, OpenCV, NumPy, etc.
• Experience designing and optimizing algorithms for efficient execution across CPU and GPU architectures
• Experience with MLOps such as (but not limited to) data annotation services, data storage, model evaluation tools, and model deployment
• Publications in your field (CVPR, ICCV, RSS, ICRA preferred)

Bonus Qualifications:

• Experience using YOLO, Faster/Mask R-CNN
• Experience developing ML models for 3 or 6 dof pose estimation

This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.

Position Overview

We are seeking a proficient Embedded Security Engineer to join our team and contribute to the protection of our models deployed on robotic hardware. You will be responsible for conducting comprehensive threat modeling and security assessments, as well as supporting our engineering teams in adhering to established security standards and requirements. This role involves both software engineering techniques and deep learning methods to encode/encrypt trained models. A blend of technical acumen and collaborative skills is essential, as you will develop solutions to identified risks, write test cases for security controls, and actively participate in offensive security assessments.

Responsibilities

• Conduct detailed threat modeling and security assessments of critical assets.
• Regularly update threat models to reflect evolving threats and changes in the business environment.
• Identify and explore vulnerabilities in critical software components used across our environment.
• Create and implement technical solutions to mitigate identified risks, and maintain test cases to ensure the effectiveness and resilience of the solutions.
• Collaborate with engineering teams to ensure compliance with defined security standards and requirements.
• Provide support in the implementation of security measures.
• Lead penetration tests and red team exercises.

Preferred Qualifications

• BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
• Proficiency developing higher-level languages (e.g., PHP, Python, C++, or Java).
• Experience with deep learning software, frameworks, and APIs (e.g., PyTorch, etc.).
• Experience with enterprise security.
• Proven experience in threat modeling, security assessments, and penetration testing.
• Strong understanding of cybersecurity principles, frameworks, and standards.
• Understanding and experience with securing transfer and storage of deep neural network models.

Position Overview

We are looking for a Deep Learning Engineer to develop and refine models for robotic manipulation. You will design, train, and deploy algorithms that enable robots to interact intelligently and adapt to their environments. Working closely with our machine learning, robotics, and research teams, you’ll ensure these models are robust, efficient, and ready for real-world challenges. Your work will directly advance Skild AI’s robotics capabilities, enabling robots to perform complex tasks autonomously.

Responsibilities

• Design, implement, and optimize deep learning models for robotic manipulation.
• Model robotic manipulation processes to enable analysis, simulation, planning, and controls
• Collaborate with cross-functional teams to develop scalable and generalizable manipulation solutions for deployment in robotic systems.
• Work with inference, deployment, and application teams to integrate deep learning models seamlessly into robotic platforms.

Preferred Qualifications

• BS, MS, or higher degree in Computer Science, Robotics, Mechanical Engineering, or a related field, or equivalent practical experience.
• Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
• Proficiency with advanced deep learning techniques and architectures as well as reinforcement learning and or imitation learning.
• Experience with distributed deep learning systems.

Position Overview

We are looking for a Software Engineer to work at the forefront of deploying our cutting-edge AI models, enhancing the performance and capabilities of our embodied systems. You will be responsible for optimizing AI inference processes from lightweight to billion-parameter models, ensuring our robots operate with unmatched efficiency and intelligence in real-world environments. You will work at the intersection of systems and machine learning, directly contributing to making our AI models more powerful and adaptive by ensuring consistent performance in light of variable and perhaps unforeseen compute and hardware constraints.

Responsibilities

• Develop and optimize runtime AI inference pipelines for real-world robotic deployment.
• Build infrastructure, frameworks, and tooling to enable reliable integration of models into robotic systems and informative analysis of production models to drive the direction of architecture choice and deployment system design.
• Formulate specialized optimization solutions for various inference paradigms and scenarios (autoregressive models, denoising models, hierarchical models, state machines, multi-agent systems, cloud-based inference).
• Adapt optimization solutions to various compute, hardware, and networking constraints.

Preferred Qualifications

• BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
• Minimum of 3 years of industry experience.
• Proficiency developing in low-level systems languages (C, C++, Rust, Go), Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
• Deep understanding and practical experience with low-level systems concepts (multithreading, networking, embedded systems, memory management).
• Experience with CUDA.
• Deep understanding of state-of-the-art machine learning techniques and models.
• Experience optimizing various machine learning architectures.
• Experience with machine learning compilers.
• Experience optimizing model inference for robotic systems deployment.

Position Overview

We are looking for a Software Engineer to work at the forefront of developing and optimizing the software infrastructure and tools necessary for training cutting-edge AI models. You will focus on building robust, scalable, and efficient training pipelines and frameworks that support the entire machine learning lifecycle, from data preparation to model deployment. You will collaborate with researchers and machine learning engineers to ensure seamless integration and operation of training systems, pushing the boundaries of what AI can achieve in real-world robotics applications. You will explore new ways to efficiently make use of many types of data in our training pipeline.

Responsibilities

• Develop and maintain robust, scalable, and distributed training pipelines (data preprocessing, training orchestration, and model evaluation) and frameworks for large-scale AI models.
• Optimize training processes for performance and resource utilization, ensuring scalability and reliability.
• Collaborate with researchers and machine learning engineers to integrate state-of-the-art algorithms and techniques into training pipelines.
• Monitor and analyze training, identifying bottlenecks and proposing solutions to improve efficiency and performance.
• Ensure the robustness and reliability of the training infrastructure, including automated testing and continuous integration.

Preferred Qualifications

• BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
• Minimum of 3 years of industry experience.
• Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
• Strong background in distributed computing, parallel processing techniques, handling large-scale datasets and data preprocessing.
• Deep understanding of state-of-the-art machine learning techniques and models.
• Experience with cloud-based training environments (AWS, Google Cloud, Azure).
• Experience in developing and maintaining software tooling and infrastructure for machine learning.
• Deep understanding and practical experience with software engineering principles, including algorithms, data structures, and system design.
• Experience with continuous integration and automated testing frameworks.

Position Overview

We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.

Responsibilities

• Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
• Design and conduct experiments to train RL models and conduct real-world tests.
• Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
• Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
• Analyze and interpret experimental results, iterating on model design to achieve desired performance.
• Stay up-to-date with the latest research and advancements in reinforcement learning.

Preferred Qualifications

• BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
• Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
• Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
• Strong background in algorithms, data structures, and software engineering principles.
• Experience with physics simulation engines and tools for training RL.
• Deep understanding of state-of-the-art machine learning techniques and models.
• Extensive industry experience with reinforcement learning and robotic systems.

Position Overview

We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.

Responsibilities

• Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
• Design and conduct experiments to train RL models and conduct real-world tests.
• Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
• Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
• Analyze and interpret experimental results, iterating on model design to achieve desired performance.
• Stay up-to-date with the latest research and advancements in reinforcement learning.

Preferred Qualifications

• BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
• Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
• Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
• Strong background in algorithms, data structures, and software engineering principles.
• Experience with physics simulation engines and tools for training RL.
• Deep understanding of state-of-the-art machine learning techniques and models.
• Extensive industry experience with reinforcement learning and robotic systems.

Position Overview

We are seeking a versatile Robotics Engineer to develop and implement software solutions for our manipulation systems. The ideal candidate will have experience in controls, perception, and planning. You will be working on deploying state-of-the-art learning-based algorithms on real robot setups, focused on daily manipulation tasks. This role will require close collaboration with researchers and the ML team. The ideal candidate should be comfortable with building high-quality software for robotic manipulation and deploying it on robot hardware.

Responsibilities

• Help design algorithms, models, and techniques for various robotic manipulation tasks.
• Design hardware and software systems for various robotic manipulation tasks.
• Write controllers and perception stacks for real-world robotic deployment.
• Write and maintain production-level C++ and Python code for our robotic manipulation platforms.
• Collaborate with machine learning engineers to deploy state-of-the-art models on our robots.
• Continuously improve and optimize robotic software for performance, reliability, and scalability.

Preferred Qualifications

• BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
• Experience in designing robotic manipulation systems and deploying software on real robots.
• Experience with writing controllers and knowledge of robotics (e.g., kinematics, dynamics, control, motion planning, SLAM).
• Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
• Systems-level understanding of the various software modules and their interfaces in a robotic application (ROS/ROS2, simulators, etc.).
• Publications at top-tier ML, robotics or CV conferences (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ECCV, ICCV).
• Experience with designing custom robot hardware solutions.
• Proficiency with various robot learning techniques (RL, imitation learning, etc.).
• Experience working with modern computer vision algorithms, sensors (LiDAR, camera, radar, etc.), 3D (meshes, point clouds, etc.), segmentation, tracking, object detection.

About The Role:

We’re seeking an AI Intern to help our humanoid robot, Digit, navigate and interact with the world. You’ll be in the driver’s seat of an applied research project, moving past theory to see your code manifest in motion on hardware. You’ll work alongside some of the brightest minds in the industry, utilizing a fleet of Digits as your playground.

About the work:

• Lead a research project in robot learning, such as reinforcement learning for whole-body control or learning loco-manipulation skills from demonstration.
• Design and run experiments to evaluate learning-based robot behaviors in simulation and on hardware.
• Improve data collection, model training, and evaluation frameworks.
• Dive into robot logs and experimental data to diagnose failures and push the boundaries of what Digit can do.
• Present your findings to a world-class team of engineers and researchers.

About you:

• Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or a related field.
• Strong skills in Python and modern ML frameworks. (PyTorch, JAX, etc.).
• Excited to integrate, run, and evaluate your learned policies on the robot.
• Strong analytical and debugging skills.
• Ability to work independently on open-ended technical problems.

Preferred Skills and Experience:

• Expertise in one or more of the following areas:
• Reinforcement learning
• Learning from demonstration
• Multimodal learning (vision, proprioception, forces, etc)
• Sim-2-real
• Experience training machine learning models on real or simulated datasets.
• Experience working with robot hardware.
• Experience analyzing experimental data and communicating results.

Company Name: Hellbender Inc. 

Job Title: Perception Engineer

Department: Engineering 

Report to: VP of Software Engineering

Who We Are: 

Hellbender Inc. is a Pennsylvania Benefit Corporation who partners with innovators to mature their Artificial Intelligence enabled computing sensors and produce their solutions onshore at prototype to high volumes. 

Hellbender is intentionally forming an inclusive work environment and is committed to uplifting the historically marginalized identities including veterans, women, trans and non-white races in our community and industry.

Job Summary: 

As a Perception Engineer at Hellbender you will help lead the design and development of AI-enabled computer vision solutions for a wide variety of industrial and commercial applications. For this role you will need to possess a strong background in building advanced perception models, state estimation, object tracking, and sensor fusion for use in autonomous systems. United States veteran and diverse candidates are encouraged to apply at any experience level. 

Duties and Responsibilities: 

• Develop and optimize computer vision and machine learning algorithms for object detection, classification, tracking, and scene understanding in varied and dynamic environments
• Utilize data from a variety of sensors, including cameras, LiDAR, and radar, to create comprehensive perception systems
• Implementation and evaluation of calibration procedures, particularly with stereo and sensor fusion scenarios
• Conduct extensive testing and validation of perception systems; document and troubleshoot issues as they arise 
• Collaborate with hardware engineers to calibrate sensors and integrate perception capabilities into broader autonomous systems
• Collaborate with other software engineers to deploy perception models in both prototyping and production environments 
• Support the team during the proposal phase, by aiding the specification and selection of appropriate sensors and compute needs for each application.

Qualifications, Skills, and Attributes:

Must haves:

• Bachelor's or Master's degree in Computer Science, Robotics, Electrical Engineering, or a related field, with a focus on computer vision or machine learning
• Proficient in programming languages and tools commonly used in perception systems development (Python, C++, OpenCV, etc.)  and experience with relevant AI and machine learning frameworks (TensorFlow, PyTorch, etc.) 
• 3+ years of professional experience building perception systems that incorporate camera, LiDAR, and radar data 

Nice to have:

• Experience designing and developing computer vision, machine learning, and sensor fusion for autonomous systems
• Experience with procedural 3D modeling and photorealistic rendering
• Experience deploying machine learning models on embedded platforms, such as NVIDIA Jetson or Hailo TPUs, is highly preferred 

Job Type: Full-time 

Benefits:  

• 401(k)
• 5% Safe Harbor Contribution to 401k by Hellbender
• Dental insurance, 100% by Hellbender for employees and their families
• Health insurance, 100% by Hellbender for employees and their families
• Vision insurance, 100% by Hellbender for employees and their families
• 7.5 weeks total Paid time off
• Flexible spending account
• Hellbender Inc. options through our Equity Incentive Plan
• Profit Sharing (through 401k)
• Hybrid work environment & flexible work schedule 

Work Location: UPARC Pittsburgh

Opportunity for advancement: 

Hellbender is a growing business and follows a policy that prioritizes internal promotions. As new positions are created new opportunities for advancement will be created as well.