Job description

Help us bridge machine learning research and real-world deployment! MatrixSpace develops AI-enabled radar and sensing systems that help people understand what's happening in the world around them. By combining advanced radar, edge computing, and AI, we deliver situational awareness in environments where traditional sensing solutions struggle. We're looking for a hands-on Machine Learning Engineer who enjoys turning cutting-edge ML research into production-ready software. You'll partner closely with our Data Scientists, taking new algorithms and implementing them in performant, maintainable, and scalable production systems. You'll also help build the ML infrastructure and tooling that accelerates future research, while ensuring our AI solutions are reliable enough for real-world deployment. If you're technically curious, highly collaborative, and motivated by solving complex real-world problems, we'd love to talk. What You'll Do • Partner with Data Scientists to transform research algorithms into robust, production-quality software. • Implement machine learning algorithms in high-performance C++ and Python with a focus on maintainability, scalability, and real-time performance. • Build and improve machine learning infrastructure, tooling, and training pipelines that enable faster experimentation and more efficient model development. • Design and implement AI agents, agentic workflows, and LLM-powered applications. • Deploy and maintain AI workloads across edge, near-edge, and cloud environments. • Collaborate across engineering and research teams to transition prototypes into production systems. What We're Looking For This position requires working directly or indirectly with the US Government in restricted environments. Candidates must be legally authorized to work in the United States without employer sponsorship and may be required to obtain and maintain a U.S. government security clearance in the future.' This is NOT a fully remote position! Required • BS, MS, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Machine Learning, AI, Robotics, or a related field. • Strong hands-on programming experience in C++ and Python. • 3-5 years of experience developing and deploying machine learning systems in production environments. • 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 written and verbal communication and collaboration skills. Someone Who Will Thrive in This Role • Enjoys solving difficult technical challenges that span algorithms, software, and deployment. • Enjoys bridging the gap between research and production, finding practical engineering solutions that make advanced ML usable in real-world products. • Takes ownership and drives projects from concept through production. • Continuously explores new AI, ML, and agentic technologies. • Works effectively across multidisciplinary teams. • Balances research innovation with practical product delivery. • Builds side projects, experiments with emerging AI tools, or enjoys hands-on technical exploration. Bonus Points • Experience with radar, RF sensing, sensor fusion, computer vision, robotics, or autonomous systems. • Experience with LangChain, LangGraph, LlamaIndex, AutoGen, Semantic Kernel, or similar frameworks. • Experience optimizing models for edge deployment usingTensorRT, ONNX,OpenVINO, TVM, or similar tools. • Experience with embedded systems, GPUs, NPUs, FPGAs, or hardware acceleration. • Familiarity withMLOps, CI/CD, model monitoring, and large-scale production systems. At MatrixSpace, Machine Learning Engineering is where advanced AI research becomes real-world capability. This is an engineering-heavy ML role focused on productionizing algorithms created by Data Scientists, with some ownership of the ML infrastructure that helps those Data Scientists move faster.

Requirements

Must-haves

Help us bridge machine learning research and real-world deployment!
If you're technically curious, highly collaborative, and motivated by solving complex real-world problems, we'd love to talk.
Partner with Data Scientists to transform research algorithms into robust, production-quality software.
Implement machine learning algorithms in high-performance C++ and Python with a focus on maintainability, scalability, and real-time performance.
Build and improve machine learning infrastructure, tooling, and training pipelines that enable faster experimentation and more efficient model development.
Design and implement AI agents, agentic workflows, and LLM-powered applications.
Deploy and maintain AI workloads across edge, near-edge, and cloud environments.
Collaborate across engineering and research teams to transition prototypes into production systems.
This is NOT a fully remote position!
BS, MS, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Machine Learning, AI, Robotics, or a related field.
Strong hands-on programming experience in C++ and Python.
3-5 years of experience developing and deploying machine learning systems in production environments.
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 written and verbal communication and collaboration skills.
Someone Who Will Thrive in This Role
Enjoys solving difficult technical challenges that span algorithms, software, and deployment.
Enjoys bridging the gap between research and production, finding practical engineering solutions that make advanced ML usable in real-world products.
Takes ownership and drives projects from concept through production.

Nice-to-haves

Experience with radar, RF sensing, sensor fusion, computer vision, robotics, or autonomous systems.
Experience with LangChain, LangGraph, LlamaIndex, AutoGen, Semantic Kernel, or similar frameworks.
Experience optimizing models for edge deployment usingTensorRT, ONNX,OpenVINO, TVM, or similar tools.
Experience with embedded systems, GPUs, NPUs, FPGAs, or hardware acceleration.
Familiarity withMLOps, CI/CD, model monitoring, and large-scale production systems.

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machine learningaimlpythonllmci/cd

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