Senior ML Engineer - Model Compression

About the Team 

The Compression and Parity team in GM’s Autonomous Vehicle (AV) Organization enables repeatable, high-velocity model deployments through principled and automated model compression under strict safety guarantees. We partner closely with model developers and deployment and infra engineers to ship numerically robust, low-latency models to the car, blending rigorous analysis with state-of-the-art methods and our own innovations. 

About the Role 

Over time, you will help grow and evolve the Compression and Parity function through the following: 

•  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  

Your Skills & Abilities (Required Qualifications)      

•  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 

What Will Give You A Competitive Edge (Preferred Qualifications)   

•  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 

• 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 

Compensation:  The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.   

• The salary range for this role:  is $128,700 to $261,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.  

• Bonus Potential:  An incentive pay program offers payouts based on company performance, job level, and individual performance.  

• Benefits:  GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more. 

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