MatrixSpace
Machine Learning Engineer
$165K–$200K
Hybrid in Burlington, MA · Full-time · Posted Jul 8, 2026
- Experience
- 3-5 years developing and deploying machine learning systems in production environments.
- Education
- BS, MS, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Machine Learning, AI, Robotics, or a related field.
- Visa sponsorship
- Must be legally authorized to work in the United States without employer sponsorship; may be required to obtain and maintain a U.S. government security clearance in the future.
About the job
Productionizes machine learning research into deployable software and infrastructure for AI-enabled radar and sensing systems.
What you'll do
- Implement and productionize ML algorithms in C++ and Python, with attention to performance, maintainability, scalability, and real-time operation.
- Build ML infrastructure, tooling, and training pipelines; design AI agents, agentic workflows, and LLM-powered applications.
- Deploy and maintain AI workloads across edge, near-edge, and cloud environments, collaborating with research and engineering teams.
What they ask for
- BS, MS, or PhD in a listed related field.
- Strong hands-on programming experience in C++ and Python.
- Experience building AI agents, LLM-based applications, or intelligent automation systems.
- Strong problem-solving skills and ability to work across the full development lifecycle; excellent communication and collaboration skills.
Nice to have
- Experience with radar, RF sensing, sensor fusion, computer vision, robotics, or autonomous systems.
- Experience with agent frameworks, edge model optimization tools, embedded systems or hardware acceleration, or MLOps and production systems.
Pay
From $165,000 to $200,000 per year (USD; salary; base or total pay not specified).
Tools
C++, Python, LangChain, LangGraph, LlamaIndex, AutoGen, Semantic Kernel, TensorRT
AI focus
Production ML for radar and sensing, including AI agents, agentic workflows, LLM-powered applications, and deployment across edge, near-edge, and cloud environments.
Model-written summary · the original posting is authoritative