About the Team

The Compression and Parity team in GM’s Autonomous Vehicle (AV) Organization enables rep eatable, 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 innovation s .

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 , u sing AV-relevant metrics ( perception , trajectory, etc. ) to evaluate compressed models , and c ollaborating 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 e ducating 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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