General MotorsRemote
Senior ML Engineer - Model Compression
United States of America
Full Time
4 weeks ago
$129,000 - $261,000 USD
H1B Sponsor Likely
Key skills
Model optimizationModel deploymentNeural network quantizationModel compressionEfficient inferencePyTorchGraph-level representationsNumerical linear algebraNumerical optimizationPost-training quantization (PTQ)Quantization-aware training (QAT)Compression frameworksAdvanced quantization algorithmsNumerics observability toolingSensitivity analysis toolingStructured pruningUnstructured pruningLow-rank decompositionKnowledge distillationPerception modelsTransformer-based modelsKernel performance optimizationSpecialized hardware acceleratorsAIMLMentoringCollaboration
About this role
General Motors is a leading automotive company focused on innovation in autonomous vehicles. The Senior ML Engineer will develop and implement model compression strategies to enhance the performance and safety of autonomous vehicle models.
Responsibilities:
- Developing and iterating on quantization and compression strategies for our AV models, considering model numerical properties, safety and latency constraints, and hardware performance, and partnering on deployment of quantized models to NVIDIA‑based AV hardware with our deployment, compiler, and kernel teams
- Advancing our numerical sensitivity analyses to recommend safe compression policies per op/layer/block, using AV-relevant metrics (perception, trajectory, etc.) to evaluate compressed models, and collaborating with Embodied AI to support compression-aware modeling
- Evolving sensitivity analysis, compression, and parity tooling into a connected, automated flow that makes low‑precision deployments repeatable, reliable, and low‑touch, with an emphasis on robust execution and maintainability
- Bridging the gap between state-of-the-art model compression research and safety-constrained deployment while making strong technical contributions in cross-functional projects and educating others on best practices
Requirements:
- Bachelor's degree in Computer Science, Electrical Engineering, Physics, Mathematics, Data Science / ML, or a closely related quantitative field (or equivalent experience)
- 3+ years of industry experience focused on model optimization and deployment, with significant hands‑on work in neural network quantization / model compression / efficient inference or relevant experience
- Strong proficiency in PyTorch and experience with graph‑level representations (e.g., PyTorch FX, ONNX) for capture and manipulation
- Background in numerical linear algebra and optimization (conditioning, spectral properties, Jacobians, Hessians) and how they relate to quantization robustness
- Master's or PhD degree in related quantitative fields
- Deep experience with PTQ and QAT, compression frameworks (e.g., PT2E, ModelOpt, torchao) and advanced quantization algorithms (e.g., GPTQ, AWQ, SmoothQuant, QuIP, SparseGPT), as well as with building or extending quantization toolchains
- Hands‑on experience designing numerics observability and sensitivity tooling integrated into training or evaluation pipelines (logging ranges, saturation, quant noise, etc.)
- A track record of collaboration, including leading cross-functional initiatives and mentoring others
- Experience with additional compression techniques such as structured/unstructured pruning, low‑rank decomposition, or knowledge distillation
- Experience with perception and/or transformer‑based models (e.g., multi‑view encoders, BEV backbones, detection/segmentation heads, trajectory or planning networks), ideally in AV / ADAS
- General understanding of kernel performance and optimization for reduced precision formats
- Direct experience with specialized hardware accelerators for edge deployment on tight latency and memory budgets (automotive SoCs, robotics platforms, or similar)
- Published research, open‑source contributions, or other notable, intellectually curious work in quantization, compression, or efficient inference