Senior AI/ML Engineer

General Motors; Sunnyvale (CA); Hybrid; USD 171,000 - 261,000; Full time

Benefits offered by this job

Health and wellbeing benefits

Paid vacation & holidays

Job summary

General Motors seeks a mathematically rigorous engineer to own model numerics, measuring and bounding numerical behavior for deployment decisions in autonomous driving. You will connect tensor differences to behavior, build robustness tooling, and enable low-overhead observability inside production training loops.

The role emphasizes training dynamics, Jacobian/Hessian estimation, and actionable analysis for engineers across the validation and deployment path. Hybrid work with travel is expected.

Qualifications

• Formal degree in a quantitative field as listed.
• Strong grasp of numerical analysis and training dynamics.
• Proficiency in PyTorch and fast, production-grade tooling.
• Experience with distributed training and advanced parallelism.

Responsibilities

• Validate optimized implementations with rigorous equivalence criteria.
• Map tensor differences to downstream behavior using open-loop and closed-loop metrics.
• Build sensitivity analysis tooling for weight/input perturbations.
• Develop observability for training dynamics and diagnostics.
• Ensure tooling runs with negligible overhead in production training.

Skills

Numerical analysis

Matrix theory

Jacobian/Hessian estimation

Spectral properties

Floating-point error analysis

Neural network training intuition

Adversarial edge cases

PyTorch

Python

Production training tooling

Distributed training

Education

Bachelor's, Master's, or PhD in Applied Mathematics, Control, Physics, Computer Science, Data Science, or related field

Tools

PyTorch

DeepSpeed

Megatron-LM

3D parallelism

Job description

About the Team

The Compression and Parity team in GM's Autonomous Vehicle organization makes aggressive model optimization safe enough to ship repeatedly. We compress and quantize models headed for the car, and we own the analytical machinery that proves the compressed model still behaves like the original — not by assertion, but with bounded, quantified, auditable evidence. Every deployment decision our organization makes about a compressed model runs through the tooling this team builds.

Job Description

We are looking for a mathematically rigorous engineer to own model numerics: how we measure, bound, and reason about the numerical behavior of the models we ship — and how we turn that analysis into deployment decisions.

What You’ll Do

• Validate Optimized implementations. Optimized implementations are supposed to be equivalent to their references. Establishing that rigorously, rather than by spot check, means deciding what equivalence should mean for a given operation, and designing the inputs that would expose a violation if one existed.
• Connect tensor differences to behavioral disparity. Map low-level numerical differences from quantization, compilation, and precision reduction to downstream driving behavior, using both open-loop metrics (trajectory displacement error, perception IoU) and closed-loop outcomes — and identify the mechanism behind the mapping, not just the correlation.
• Build sensitivity and robustness analysis tooling. Use Jacobian/Hessian-based methods to characterize how model outputs respond to weight and input perturbation, extend the same machinery to out-of-distribution inputs, and turn it into tooling that runs repeatedly across checkpoints — by engineers who are not you.
• Build training dynamics observability. Design diagnostics that detect and root-cause training instabilities — gradient vanishing and explosion, loss spikes, silent divergence — including decompositions of gradient and update trajectories into loss-descent and oscillatory components under modern schedules such as WSD.
• Make it cheap enough to always be on. Metric computation, gradient decomposition, and diagnostic logging have to run inside real distributed training jobs with negligible throughput cost and no OOM risk. Observability nobody can afford to enable is observability that doesn't exist.

Requirements

• A working command of numerical analysis and matrix theory. Jacobian/Hessian estimation, spectral properties, conditioning, and floating-point error analysis should be tools you reach for by reflex, not topics you once studied. You should be able to say why an estimator's variance blows up, and when that matters.
• A real mental model of neural network training. Loss landscapes, gradient and error propagation, optimizer dynamics, and the mechanics by which training goes wrong. You should have opinions about what a gradient norm spike does and does not tell you.
• An adversarial instinct for numerical edge cases. Typical inputs rarely find anything. We are looking for someone who reaches for denormals, extreme dynamic range, catastrophic cancellation, degenerate shapes, and accumulation-order effects — someone whose first question about a passing test is what that test failed to exercise.
• The engineering to make the math run. High proficiency in PyTorch and Python, and a track record of building analytical tools that are both mathematically defensible and fast enough to be used in production training and evaluation loops.
• The judgment to make analysis actionable. Much of this role is turning a numerical result into something an engineer who will not read your derivation can act on: knowing which quantity actually answers the question being asked, tracing an anomalous number back to the operation that produced it, etc
• Bachelor's, Master's, or PhD in Applied Mathematics, Control, Physics, Computer Science, Data Science, or a closely related quantitative field.

What You’ll Learn Here

• The training recipes, the acceleration techniques in our stack
• The semantics of autonomous driving behavioral metrics and what actually constitutes meaningful behavioral drift for a vehicle.
• Model compression portfolio, the deployment path to the car, and the organizational context around a ship decision.
• Our parity validation workflow and where the quantization and compilation toolchains hide their sharp edges.

What Will Give You a Competitive Edge

• Hands‑on experience debugging large‑scale training runs: diagnosing loss spikes, resolving numerical divergence, and running deep investigations into training dynamics.
• Experience with distributed training beyond DDP — FSDP, Megatron‑LM, DeepSpeed, 3D parallelism — particularly designing low‑overhead observability over sharded parameter and gradient state.
• Experience in quantization, compiler toolchains, or inference‑time numerical parity.
• Experience building or scaling evaluation pipelines and its metrics formulation for AV or ADAS systems.
• Published or applied work in model robustness, OOD generalization, or adversarial/perturbation analysis.

Compensation

• Compensation: The expected base compensation for this role is: $170,600 - $261,300 Actual base compensation within the identified 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

#GM-AV-1

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}. The selected candidate will be required to travel <25% for this role. Relocation benefits are available for candidates who qualify under company policy.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.

Benefits Overview

From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.

Non-Discrimination and Equal Employment Opportunities (U.S.)

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Accommodations

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