Senior Machine Learning Engineer

Our client is building the next generation of machine learning infrastructure by deploying ML models directly onto custom hardware. This is a rare opportunity to help define an entirely new technology stack from the ground up architecting solutions from first principles, influencing long-term research direction, and seeing your work deployed in one of the world's most demanding compute environments.

Key Responsibilities

• Co-design machine learning models alongside researchers, engineers, and domain experts while treating hardware constraints such as latency, resource utilization, and numerical precision as core design considerations.
• Help shape the roadmap for custom hardware platforms by translating ML workloads into hardware architecture decisions.
• Partner closely with hardware engineers to implement, validate, and deploy ML inference solutions from research prototypes through production.
• Evaluate emerging research across neural architecture search, quantization, ML systems, and hardware-aware optimization, identifying innovations that can deliver measurable performance improvements.
• Drive performance optimization across both hardware and software, balancing model accuracy with strict latency and throughput requirements.

Required Qualifications

• Strong understanding of hardware architecture and the trade-offs involved in mapping machine learning workloads to FPGAs, ASICs, or other specialized accelerators.
• Experience with hardware development through technologies such as VHDL, System Verilog, High-Level Synthesis (HLS), or hardware deployment frameworks including hls4ml, FINN, or Vitis AI.
• Solid understanding of machine learning fundamentals, including neural network architectures, inference optimization, quantization techniques, and frameworks such as PyTorch or Tensor Flow.
• Strong programming skills in Python, C++, or similar languages used for tooling, simulation, testing, and model development.
• Excellent communication skills with the ability to collaborate across multidisciplinary teams spanning hardware, software, and research.

Preferred Qualifications

• Experience with ML compiler technologies such as MLIR, TVM, XLA, or comparable compiler infrastructures.
• Background in performance-critical or resource-constrained systems, including high-frequency trading, real-time signal processing, particle physics, networking, telecommunications, or embedded systems.
• Familiarity with hardware verification methodologies such as UVM, Cocotb, or System Verilog verification environments.
• Master's or PhD in Electrical Engineering, Computer Science, Physics, or a related technical discipline, or equivalent industry experience.