- Master’s degree or equivalent experience in Computer Science, Electrical Engineering, or a related field.
- Over 12 years working in the industry, including at least 8 years specializing in deep learning model optimization, inference engineering, or neural network compilation. Proficiency in understanding and reviewing model architectures at the operator/kernel level, not merely handling their operation, is required.
- Over 5 years of validated expertise in embedded/edge software, with experience delivering production inference solutions within power-limited, latency-sensitive deployment environments.
- Comprehensive knowledge of contemporary DL architectures: transformers, attention variants, vision encoders (ViT), multi-modal/vision-language model frameworks, as well as experience with diffusion models and/or state space models.
- Expert knowledge of GPU architecture fundamentals, CUDA, and low-level performance optimization using heterogeneous computing. Experience with TensorRT, compiler IRs, or equivalent inference optimization toolchains.
- Solid understanding of embedded operating system internals (QNX/Linux), memory management, C/C++, and embedded/system software concepts.
- Background in parallel programming (e.g., CUDA, OpenMP) and experience reasoning about memory hierarchies, data movement, and compute utilization.
- Demonstrated capability to collaborate directly with external partners and customers in a deep technical role. You solve their workload issues, identify performance problems, and provide solutions within production limitations.
Ways to Stand Out from the Crowd
- Experience with ML compiler frameworks (TVM, MLIR, XLA, Triton) or contributing to inference runtime development.
- Production deployment experience with autonomous vehicle perception or planning stacks, understanding the full pipeline from sensor input through trajectory output.
- Familiarity with the Physical AI model landscape: VLM + action expert architectures, end-to-end driving models, or robot foundation models.
- Contributions to MLPerf benchmarks and large-scale industry performance optimization efforts.
- Experience with automotive safety standards (ISO 26262, SOTIF) and their implications for inference system development.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 224,000 USD – 356,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
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