Mission Summary:

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

What You'll Be Doing:

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

What We're Looking For:

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

Bonus Points (not required):

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

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

Mission Summary:

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

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

What You'll Do:

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

What We're Looking For:

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

Bonus Points (Nice-to-Haves):

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

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

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

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.

Senior Vision AI Engineer 
(Cognitive Vision and Industrial AI)

Location: Mid-Atlantic Region
Company: Linque
Reports to: Head of Vision AI

About Proconex

Proconex is a trusted leader in industrial automation and process control solutions, proudly serving the industry for over 75 years. With a rich legacy of delivering high-performance systems and equipment to process industries—including oil & gas, chemicals, power, and life sciences—we are now entering an exciting new chapter.

We are  building on a solid foundation of over 10 years of successful deployments with our next generation of software products. Our software solutions are designed to empower industrial operators with operational analytics, enabling smarter, more efficient, and more reliable operations. This is a unique opportunity to be part of a growth-focused team that is taking proven technology to the next level.

About the Role

Linque is expanding its Vision AI and Cognitive Automation platform focused on applying real-time computer vision and AI to industrial, biopharmaceutical, and process manufacturing environments. We are seeking a Senior Vision AI Engineer to help lead the development, deployment, and customer-facing application of advanced vision AI solutions. This role sits at the intersection of deep technical execution, platform development, and real-world industrial use cases.

Key Responsibilities

• Design, develop, and deploy production-grade computer vision and machine learning models
• Build optimized end-to-end vision pipelines for real-time and near-real-time applications
• Translate complex industrial problems into scalable AI architecture
• Collaborate cross-functionally with product, engineering, and commercial teams
• Participate in technical customer discussions, pilots, and evaluations
• Support prototypes through pilot and production transitions
• Mentor junior engineers and contribute to technical best practices

Required Qualifications

• 5+ years’ experience in computer vision and machine learning
• Strong hands-on experience deploying AI systems in production
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, Keras)
• Deep knowledge of Video / Image processing tools (e.g. Mask R-CNN, ResNet, U-Net)
• Knowledge of object and anomaly detection (Autoencoders, FastFlow, Transformers)
• Tunning experience for CNNs and Vision Models (e.g., MLFlow, NVIDIA TAO, MMD, MMS)
• Mastery of model interchange and GPU acceleration (e.g. ONNX, CUDA, cuDNN)
• Experience working with image/video pipelines, container orchestration (local and cloud)
• Understanding of the full ML / AI lifecycle

Preferred Qualifications

• Experience with real-time vision systems
• Background in industrial automation or regulated environments
• Experience with edge or cloud deployment
• Ability to communicate complex technical topics clearly

Why Join Linque

• Apply AI to real industrial problems with measurable impact
• Collaborate with experienced AI and industrial leaders
• Influence the evolution of next-generation Vision AI platforms
• Flexible, growth-oriented environment

Apply today to learn more about this exciting new opportunity at Proconex, the exclusive Emerson Impact Partner for the Mid-Atlantic region!

About the Role:

As a Senior Data Scientist at Perpay, you will be a critical player in the day-to-day success of the business, supporting new and existing product initiatives through strong analytical skills and machine learning expertise. In addition, you will be responsible for helping shape the future of the data science team by developing processes and systems that scale with our rapidly growing business. This is an exciting opportunity to make a positive impact on the execution of Perpay’s mission and the lives of our customers. We are looking for someone who is passionate about all aspects of data science: from machine learning and exploratory data analysis to communication of results and real-time deployments. This crucial role will sit on our data science team and report to the Data Science Manager. If you’re looking for a collaborative team that you can bounce ideas off of and learn from, this is where you belong. We are about being curious, setting the standard and building good habits at Perpay.

Our greatest strength is our people and we’d love for you to be one of them!

Responsibilities:

• Drive the end-to-end completion of data science initiatives, collaborating with stakeholders from ideation through execution, ensuring projects deliver strong impact to our customer’s financial lives and the health of the business
• Proactively identify opportunities for the data science team to improve and expand Perpay’s business model using techniques from exploratory data analysis to machine learning models, leveraging subject matter expertise of Perpay’s business model
• Support the growth of the business by delivering scalable models and algorithms while maintaining and troubleshooting existing resources
• Deploy and maintain real-time model inference resources and infrastructure
• Deploy and maintain machine learning pipelines including batch processing and continuous training architecture
• Contribute to the cross-discipline adoption and responsible use of AI/LLM models and services at Perpay 
• Present results and analyses to the a wide range of audiences: from Perpay's leadership team to non-technical peers
• Contribute to defining standards and best practices concerning workflow and adoption of new technologies within the Data Science team
• Act as an informal mentor peers

What You’ll Bring:

• 5+ years of experience* and expertise applying machine learning techniques in industry
• 5+ years of experience* and expertise in producing data analysis, exploratory data analysis and data visualization in industry
• 5+ years of experience* and expertise in developing, deploying, maintaining real-time software (e.g. models exposed as APIs)
• 5+ years of experience* and expertise in Cloud compute infrastructure and best practices, including familiarity with tools such as terraform and Cloud Formation
• Bachelor's degree or higher in a quantitative/technical field (Mathematics, Statistics, Engineering, Chemistry, Physics)
• Excellent communication skills, across a range of audiences from technical to non-technical
• Strong business acumen, demonstrated through subject matter expertise in at least one industry
• Capable of managing projects “end to end” including managing project scope, timelines and workloads
• Advanced proficiency in Python and standard data science stack (e.g. scikit-learn, pandas, numpy, matplotlib, etc.)
• Team player: willingness and ability to establish strong working relationships not just within the data science team itself, but across the organization

*Internships not included

Nice to have:

• Expertise in multiple advanced machine learning methodologies: e.g. deep learning, probabilistic programming, mathematical optimization, etc.
• Deep familiarity in multiple advanced machine learning frameworks: e.g. tensorflow, pytorch, pymc, etc.
• Experience with scalable compute frameworks such as Spark or Polars
• Experience with deployment frameworks such as Kubernetes