OverviewThe AI Models and Data Science team at Keysight AI Labs is hiring a ML Performance Engineer to make our training and inference stacks as fast as the math allows. You'll own end-to-end performance: profiling training workloads on multi-GPU clusters, writing custom CUDA kernels and LibTorch C++ extensions for hot paths, and optimizing inference for embedding in production software where every millisecond matters.
This role sits at the intersection of ML, systems engineering, and HPC. You'll work directly with MLEs and data scientists driving the modeling work, and with the engineering teams shipping these models into Keysight products.
Responsibilities- Profile and optimize training workloads - multi-GPU scaling efficiency, throughput, memory footprint, mixed precision, gradient checkpointing tradeoffs
- Profile and optimize inference for low-latency, high-throughput deployment - quantization, graph optimization, kernel fusion, runtime selection
- Write custom CUDA kernels and LibTorch (PyTorch C++) extensions to accelerate hot paths in both training and inference
- Build and maintain serving infrastructure using ONNX Runtime, TensorRT, and similar - including C++ integration paths for embedding models inside production software
- Partner with MLEs and data scientists on perf-aware architecture choices; partner with product engineering on deployment, versioning, and monitoring
- Establish performance SLAs and regression tests so models stay fast as they evolve
Qualifications- 4+ years in ML engineering, performance engineering, or HPC, with substantial production ML experience
- Strong Python and C++ - including LibTorch / PyTorch C++ extensions in production
- Hands-on experience optimizing both training and inference workloads (not just one)
- CUDA experience required - comfortable profiling GPU code with Nsight and reasoning about occupancy, memory hierarchy, and kernel-level tradeoffs
- Production deployment experience with ONNX Runtime, TensorRT, or equivalent inference runtimes
- Solid software engineering fundamentals: testing, versioning, code review, monitoring
- Experience with Docker and container-based deployment
The level of role and salary will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.
California Pay Range: MIN $160,160- MAX $266,930
Note: For other locations, pay ranges will vary by region.
US Employees may be eligible for the following benefits:
- Medical, dental and vision
- Health Savings Account
- Health Care and Dependent Care Flexible Spending Accounts
- Life, Accident, Disability insurance
- Business Travel Accident and Business Travel Health
- 401(k) Plan
- Flexible Time Off, Paid Holidays
- Paid Family Leave
- Discounts, Perks
- Tuition Reimbursement
- Adoption Assistance
- ESPP (Employee Stock Purchase Plan)
