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.,PyTorchFX, 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 inAV / 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 <25% for this role.

This job may be eligible for relocation benefits.

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