Senior ML Engineer - Model Compression

• Job Description
• 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 quantizationand compressionstrategiesfor ourAV models,consideringmodel numerical properties, safety and latency constraints, andhardware performance, andpartnering on deployment of quantized modelsto NVIDIA‑based AV hardwarewith our deployment, compiler, and kernel teams + Advancing our numerical sensitivity analysesto recommend safecompressionpolicies per op/layer/block,using AV-relevant metrics(perception, trajectory,etc.

) to evaluate compressed models,andcollaborating withEmbodied AItosupportcompression-aware modeling + Evolvingsensitivity analysis, compression, and parity toolinginto a connected, automated flow that makes low‑precision deployments repeatable, reliable, and low‑touch,with an emphasis onrobust execution and maintainability + Bridging the gapbetweenstate-of-the-artmodel compressionresearch andsafety-constrained deploymentwhilemakingstrong technical contributions incross-functional projectsand educating others on best practices

• Your Skills & Abilities (Required Qualifications)
• + Bachelor'sdegreein Computer Science, Electrical Engineering, Physics, Mathematics, Data Science / ML, or a closely related quantitative field (or equivalent experience) + 3+ yearsof industry experience focused onmodel optimization and deployment, with significant hands‑on work inneural network quantization / model compression / efficient inference or relevant experience + StrongproficiencyinPyTorchandexperience withgraph‑level representations(e.

g.,PyTorch

FX, ONNX

) for capture and manipulation + Background innumerical 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'sor PhD degreein related quantitative fields + Deep experience withPTQ and QAT,compression frameworks(e.

g.,PT2E,ModelOpt,torchao) andadvanced quantization algorithms(e.g., GPTQ, AWQ,SmoothQuant,QuIP,SparseGPT),as well as with building or extendingquantization toolchains + Hands‑on experience designingnumericsobservability and sensitivity toolingintegrated into training or evaluation pipelines (logging ranges, saturation, quant noise, etc.) + A track recordof collaboration,includingleading cross-functional initiatives andmentoring others + Experience withadditionalcompression techniquessuch as structured/unstructuredpruning,low‑rank decomposition, orknowledge distillation + Experience withperceptionand/or transformer‑based models(e.g., multi‑view encoders, BEV backbones, detection/segmentation heads,trajectoryor planning networks), ideally in

AV / ADAS

+ General understanding ofkernel performance and optimizationfor reduced precision formats + Direct experience withspecialized hardware accelerators foredge deploymenton tight latency and memory budgets (automotive SoCs, robotics platforms, or similar) + Published research, open‑source contributions, orothernotable, intellectually curiousworkin quantization, compression, or efficient inference + 3+ yearsof industry experience focused onmodel optimization and deployment, with significant hands‑on work inneural 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. \#GM-AV-1 This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.The selected candidate will be required to travel