1

Reinforcement Learning Jobs in Massachusetts (NOW HIRING)

Develop visual models that support reinforcement learning and imitation learning policies, including end‑to‑end visuomotor policies that map visual observations to robot actions. * Improve our ...

New

next page

Showing results 1-20

Reinforcement Learning information

See Massachusetts salary details

$31.1K

$63.7K

$87.4K

How much do reinforcement learning jobs pay per year?

As of Aug 7, 2026, the average yearly pay for reinforcement learning in Massachusetts is $63,722.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,200.00 and $74,300.00 per year, depending on experience, location, and employer.

What does a reinforcement learning professional do?

A typical day for a Reinforcement Learning professional involves designing and implementing learning algorithms, running experiments, analyzing data, and iterating on models to improve performance. You might collaborate closely with data scientists, software engineers, and product managers to integrate your solutions into broader systems or products. Regular activities also include reading recent research literature and participating in team meetings to discuss progress and obstacles. This dynamic role often balances deep technical work with teamwork to drive innovative applications in areas such as robotics, recommendation systems, or autonomous systems.

What are the key skills and qualifications needed to thrive in the reinforcement learning position?

To thrive in a Reinforcement Learning role, you need a solid background in mathematics, statistics, machine learning, and programming (commonly with Python), typically supported by a relevant degree such as in computer science or engineering. Experience with frameworks like TensorFlow, PyTorch, OpenAI Gym, and familiarity with large-scale computing systems are highly valued. Strong problem-solving abilities, curiosity, and effective collaboration and communication skills help you excel in multidisciplinary research and project teams. These capabilities are crucial for designing, implementing, and refining complex algorithms that learn from interaction to solve real-world problems.

What is a reinforcement learning?

A Reinforcement Learning (RL) job involves designing, developing, and optimizing algorithms that enable machines to learn from interactions with their environment. RL professionals work on applications in robotics, finance, gaming, and autonomous systems, leveraging techniques like deep reinforcement learning and policy optimization. Responsibilities often include researching new models, implementing RL algorithms, and improving AI performance. Strong programming skills, knowledge of machine learning frameworks, and an understanding of mathematical concepts like probability and optimization are essential.

What are the most commonly searched types of Reinforcement Learning jobs in Massachusetts? The most popular types of Reinforcement Learning jobs in Massachusetts are:
What are popular job titles related to Reinforcement Learning jobs in Massachusetts? For Reinforcement Learning jobs in Massachusetts, the most frequently searched job titles are:
What cities in Massachusetts are hiring for Reinforcement Learning jobs? Cities in Massachusetts with the most Reinforcement Learning job openings:
Infographic showing various Reinforcement Learning job openings in Massachusetts as of August 2026, with employment types broken down into 63% Full Time, 28% Part Time, and 9% Contract. Highlights an 100% In-person job distribution, with an average salary of $63,722 per year, or $30.6 per hour.

Machine Learning / Reinforcement Learning Infrastructure Engineer

Eka Robotics

Cambridge, MA • On-site

$118K - $155K/yr

Full-time

Re-posted 4 days ago


Job description

Job Summary:
Eka Robotics is on a mission to build intelligence for the physical world through advanced robotics. They are seeking a Reinforcement/Machine Learning Infrastructure Engineer to design, implement, and maintain large-scale model training systems that will enhance their robotics research and deployment efforts.
Responsibilities:
• Own Training Infrastructure: Design, implement, and maintain robust systems for large-scale model training, including job orchestration, scheduling, checkpointing, and experiment tracking.
• Developer Experience & Tooling: Build streamlined, intuitive abstractions for launching, monitoring, debugging, and reproducing experiments, minimizing friction and maximizing productivity for our research teams.
• Scale Distributed Training: Work closely with researchers to reliably scale reinforcement learning and machine learning pipelines across compute clusters.
• Resource Management: Ensure efficient allocation and utilization of cloud-based compute resources while building the foundational systems needed for future scaling.
• Collaborate with Researchers: Partner with the research team to understand their needs, build infrastructure that supports cutting-edge methods, guide best practices for training at scale, and contribute to core JAX model and training code.
Qualifications:
Required:
• BS, MS or higher in Computer Science, Computer Engineering, Machine Learning or a related technical field.
• Strong software engineering fundamentals with a proven track record of building ML training infrastructure, internal developer platforms, or scalable systems.
• Hands-on experience with large-scale training using JAX (preferred), PyTorch, or TensorFlow.
• Familiarity with distributed training, multi-host setups, data pipelines, and managing workloads on cloud platforms or orchestration systems (e.g., Kubernetes, SLURM, GCP, AWS).
• Strong cross-functional communication skills, a deep ownership mindset, and a passion for building tools that improve the developer experience.
• Experience building automated testing pipelines, CI/CD for ML workflows, and custom logging/telemetry stacks.
Preferred:
• Background in robotics, reinforcement learning or other machine learning systems.
• Experience designing abstractions that balance researcher flexibility with system reliability.
Company:
Founded in , the company is headquartered in Cambridge, MA, US, , with a team of 11-50 employees. The company is currently Early Stage.