Machine Learning Engineer

Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives.

Why Join Us? Be part of a team pushing the boundaries of machine learning and robotics in a fast-paced, intellectually rich environment. Here, you’ll have the opportunity to work on impactful projects, grow your technical expertise, and contribute to groundbreaking advancements that define the next generation of intelligent machines.

At the forefront of AI and Robotics, our mission is to tackle the most pressing challenges to create intelligent machines that help make our lives better. We’re building a collaborative, dynamic team in our new Cambridge, MA office, where creative solutions and cutting-edge research meet to shape the future of robotics. As a Machine Learning Engineer, you’ll work across disciplines to develop transformative technologies for robotic systems. If designing and implementing state-of-the-art machine learning models, architecting scalable infrastructure for model training, inference, optimization and data processing of high-performance pipelines excites you, join us in advancing machine intelligence!

• Model Deployment & Maintenance: Train, deploy, and sustain a variety of ML models on both cloud and on-prem infrastructure to enhance robotics capabilities
• Pipeline Innovation: Build and refine ML pipelines, encompassing every lifecycle phase from training and evaluation to optimization and deployment
• Collaborative Development: Partner closely with research and engineering teams to design, test, and implement robust model architectures suited for production
• Quality & Reliability: Elevate code quality through regular peer reviews and champion best practices in our software processes
• Continuous Learning: Engage actively in our Institute’s vibrant research environment to stay at the cutting edge of advancements in ML architectures, frameworks and applications.

• Technical Background: BS or MS in Computer Science, Engineering, or equivalent; 6 years of experience (3 with MS or PhD) as a machine learning engineer, software engineer, or applied scientist
• Expert Coding Skills: Proficiency in production-level data processing and ML training in Python, C , or similar languages
• Engineering Best Practices: Proficient in software practices like version control (Git), CI/CD, and issue tracking
• Cloud Expertise: Hands-on experience with cloud platforms like GCP and AWS
• Machine Learning Frameworks Knowledge: Proficiency with deep learning frameworks such as PyTorch, TensorFlow, or Flax.
• Modeling Mastery: In-depth hands on experience with state-of-the-art ML techniques—transformers, diffusion models, multimodal modeling—applied across domains like robotics, computer vision, and NLP.

• Advanced ML Techniques: Hands-on with reinforcement learning, imitation learning, incremental learning, or model optimization/compression.
• Simulation Experience: Familiarity with robotics simulators like MuJoCo, Isaac Sim, or Drake.
• Edge Deployment: Experience deploying models on robotic devices and/or with ROS.
• Big Data Processing: Skills in parallelized data frameworks like Hadoop, Spark, or Ray.
• Scalable Training Expertise: Familiarity with distributed training using tools like Ray, PyTorch Lightning, or KubeFlow.
• MLOps & Deployment: Expertise in MLOps practices (model versioning, monitoring, scalable deployment).
• Containerization & Orchestration: Experience with Docker, Kubernetes, and orchestration tools (Airflow, AWS Step Functions).
• DevOps & Automation: Proficiency in CI/CD pipelines, IaC, and containerized environments.

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