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Senior AI/ML Engineer

Q: What is the Senior AI/ML Engineer job at General Motors?

A senior level role in technology (data, analytics and ai) listed 7 days ago. The full description is on this page and on The Muse.

Q: How do I apply?

Use the "View this job and apply on The Muse" button. AtiveSite does not receive applications.

Q: How much does it pay?

The listing does not publish pay, and we could not match the title to one official occupation.

Q: What is the cost of living there?

Median rent in San Francisco is $2,476 a month and median household income $140,970 (ACS 2020–2024).

Q: How old is this listing?

It was posted on 2026-11-01 (7 days ago). Jobs older than 90 days are not shown.

Q: Are there similar jobs?

Yes: browse data, analytics and ai jobs in California.

General Motors · San Francisco, CA · posted 7 days ago

In short: Senior AI/ML Engineer at General Motors in San Francisco, CA, posted 7 days ago.

By AtiveSite · Published 07/11/2026 · Updated 08/11/2026 · Every figure is linked to its source below.

General Motorsemployer

San Francisco, CAlocation

Data, analytics and AIsub-niche

7 days agoposted

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The job

LIVE Live feed (private-sector employer) Source: The Muse public API · Data: listed 2026-11-01

Employer	General Motors
Location	San Francisco, California
Field	Technology › Data, analytics and AI
Level	Senior Level
Source category	Data and Analytics
Posted	2026-11-01 (7 days ago)
Work arrangement	On-site at the location listed
Pay	Not published in the source listing

Job description

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.

About the Role

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.

The central question of this role is deceptively simple: given two numerically different versions of the same model, is the difference safe? Answering it well requires connecting things that are usually studied separately - floating-point drift and Hessian conditioning on one end, vehicle trajectory error on the other. You will build the tooling that makes that connection quantitative, and you will define the thresholds that turn it into a ship / no-ship decision.

This role is not: running an existing validation harness and reporting the numbers it produces. When a parity check fails, the expectation is that you can say which operation caused the divergence and why - not merely that a difference exceeded a threshold. The tooling exists to make that investigation fast; it does not replace the investigation itself.

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:

We are deliberately strict on a small number of things and flexible on everything else.

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

You are not expected to arrive with these. They are the parts of the job that are genuinely specific to this environment, and we expect them to take three to nine months:

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: 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

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.

Total Rewards | 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.)

General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.

All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.

We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.

Accommodations

General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us [email protected] or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.

Explore around this job

Field: Technology Sub-niche: Data, analytics and AI All jobs at General Motors Jobs in California Jobs in San Francisco, CA San Francisco city guide California state guide San Francisco County economy

View this job and apply on The Muse →

You apply on The Muse or on the employer's page; AtiveSite does not receive or process applications and is not affiliated with The Muse. The description above is shown as the source published it.

About the role

This is a Senior Level role in the Technology field (Data, analytics and AI) at General Motors. Senior roles usually expect substantial experience, ownership of results and often the ability to guide others.

We could not match this job title to one occupation in the official wage statistics, so no pay estimate is shown. Pay is not in the source listing; ask the employer for the range and compare in the 240 job niches.

Does the pay work here? (estimate)

Gross salary a year Filing State income tax rate %

Estimate for a wage earner: 2026 federal brackets and standard deduction (IRS), Social Security 6.2% up to $184,500, Medicare 1.45% (+0.9% above $200,000 single / $250,000 married), and a state rate you set; set your own state tax rate. It ignores credits, pre-tax deductions, local taxes and benefits. Sources: IRS: tax year 2026 inflation adjustments · SSA wage base per Federal Register notice 2025-19763.

How to prepare and behave

This is editorial guidance, not information from the employer.

Read the whole description and match your examples to its requirements; use the same words the posting uses where they are true for you.
Research the employer before the interview: what it does, who its customers are and recent news; prepare two or three questions about the work.
Arrive early (or log in early for a video call), dress one step above the daily dress code, and test your camera and audio beforehand.
Answer with short stories: the situation, what you did and the result. Give numbers when you have them and never invent them.
Be honest about gaps; say what you did to learn and how fast you can close them.
Ask about the team, the first 90 days, how success is measured and the schedule; do not open with salary and time off.
When pay comes up, know the market range for the occupation and your lowest acceptable offer before you answer; ask for the full package in writing.
Send a short thank-you message within a day, mentioning one thing you discussed.
Have code, data work or projects you can show, and be ready to think aloud: many technical interviews test how you reason, not only the answer.

Know your rights as a candidate and an employee:

The city

The job is in San Francisco, CA. Below is what the official statistics say about living and working there.

How San Francisco compares. Among the 145 largest places in California, San Francisco is #4 by population, #139 for homeownership (#1 = highest), #20 for remote work (#1 = most), #25 for college degrees (#1 = most). Its county, San Francisco County, is #3 of 58 counties for average pay ($174,103).

The place in numbers

San Francisco has 830,235 residents (ACS 2020–2024), about 2.1% of the population of California. It covers 47 square miles of land, which works out to roughly 17,784 people per square mile. The median age is 40.0, 6% older than the state median (40.0 against 37.9).

Housing

The median gross rent is $2,476 a month (rent plus utilities), 22% above the state median ($2,476 against $2,036). Over a year that is 21.1% of the median household income. US housing guidance treats housing as affordable when it takes 30% of income or less, so the typical household is under that line. The median home value is $1,394,500, which is 9.9 times the median household income ($140,970); a ratio above 5 usually means buying is out of reach without savings or a second income. 38.2% of households own their home, 32% below the state average (38.2% against 55.9%). A low ownership share points to a rental-heavy market with more apartments and shorter stays.

Work and pay

The San Francisco-Oakland-Fremont, CA metropolitan area has 2,373,920 jobs in the BLS wage survey, and the median pay across all occupations is $74,260 a year, 46% above the national median ($74,260 against $50,980). The biggest occupation groups are office and administrative support (226,320 jobs, median $62,120); management (223,100 jobs, median $176,340); business and financial operations (207,030 jobs, median $112,960). The area is most specialised in computer and mathematical (1.9 times) and life, physical, and social science (1.8 times), where the share of workers is well above the national share. The best-paying groups with at least 5,000 jobs are management ($176,340) and computer and mathematical ($167,770). In 2026M09 the unemployment rate of San Francisco County was 4.0%. 69.6% of residents aged 16 and over are in the labour force. The city lies in San Francisco County; its jobs, pay by industry and GDP are on the county page.

Getting around

Among workers, 28.6% drive alone, 18.7% use public transit, 9.3% walk, 31.0% work from home. 21.6% of workers commute 45 minutes or more, 11% above the state average (21.6% against 19.4%). Remote work is more common than in the state as a whole, which widens the set of employers a resident can work for without moving.

People and education

60.3% of adults aged 25 and over hold a bachelor's degree or higher, 63% above the state share (60.3% against 37.1%). 34.0% of residents were born outside the United States, 26% above the state share (34.0% against 27.0%). The poverty rate is 11.2%, 7% below the state rate (11.2% against 12.0%).

Climate

The warmest month is Sep, averaging 59°F (14.9°C), and the coolest is Apr at 53°F (11.6°C). About 515 mm (20 inches) of precipitation falls in a year; Dec is the wettest month (112 mm) and Jul the driest (2 mm). These values come from NASA satellite-based climatology on a grid of about 50 km, so they describe the wider area, not a weather station in the city.

Nearby cities to compare

Closest cities in this guide: Daly City (31 miles, rent 3% higher); Pacifica (31 miles, rent 25% higher); San Bruno (34 miles, rent 12% higher); Millbrae (36 miles, rent 32% higher). Rent differences are against San Francisco; use the Compare tab to put any two cities side by side.

Full guide to San Francisco →California guide All jobs in San Francisco

Official links for San Francisco

These links go to official sources and open in a new window. The first one is the government's or company's own site as listed in Wikidata (property "official website"); check it before relying on it.

Where to live

CITY Official statistics Source: U.S. Census Bureau, American Community Survey 2020–2024 5-year (via Census Reporter) · Data: ACS 2020–2024

Places within 50 miles (straight line) of San Francisco, CA that have a guide, with what living there costs. No pay estimate is shown because the title did not match one occupation.

Sort:

Place ⇅	Distance from the job city ⇅	Median rent ⇅	Median home value ⇅	Median household income ⇅	Commute 45+ min ⇅	Rent as % of occupation pay ⇅	Open jobs there ⇅
San Francisco, CA	0 mi	$2,476	$1,394,500	$140,970	21.6%	n/a	Jobs
Daly City, CA	31 mi	$2,551	$1,115,000	$123,547	19.6%	n/a	—
Pacifica, CA	31 mi	$3,098	$1,249,800	$160,523	15.5%	n/a	—
San Bruno, CA	34 mi	$2,773	$1,207,500	$141,272	12.2%	n/a	—
Millbrae, CA	36 mi	$3,272	$1,934,900	$163,476	21.8%	n/a	—
Brisbane, CA	36 mi	$2,574	$1,232,200	$156,169	29.8%	n/a	—
South San Francisco, CA	36 mi	$2,802	$1,188,800	$136,578	15.2%	n/a	—
Half Moon Bay, CA	37 mi	$2,433	$1,549,500	$157,884	26.7%	n/a	—
Burlingame, CA	38 mi	$2,785	$2,000,001	$174,000	23.1%	n/a	—
Richmond, CA	40 mi	$1,960	$668,200	$95,391	28.8%	n/a	—
San Mateo, CA	41 mi	$3,077	$1,618,700	$153,504	15.8%	n/a	—
Berkeley, CA	41 mi	$2,133	$1,413,900	$108,092	20.8%	n/a	Jobs
Alameda, CA	42 mi	$2,474	$1,235,700	$137,697	27.3%	n/a	Jobs
Belmont, CA	43 mi	$2,875	$2,000,001	$205,297	18.6%	n/a	Jobs
Oakland, CA	44 mi	$1,979	$929,900	$101,600	21.9%	n/a	Jobs
Foster City, CA	44 mi	$3,501	$1,838,200	$199,143	20.6%	n/a	Jobs
San Carlos, CA	45 mi	$2,854	$2,000,001	$237,470	14.9%	n/a	—
Redwood City, CA	47 mi	$2,968	$1,801,700	$157,814	10.2%	n/a	Jobs
San Leandro, CA	48 mi	$2,186	$844,200	$101,420	23.7%	n/a	—
Atherton, CA	49 mi	$3,501	$2,000,001	$250,001	7.0%	n/a	—

Career paths

We could not match this title to one occupation, so here are the other sub-niches in Technology, where your skills may carry over:

Cybersecurity (366) Software engineering (1880) Cloud, IT and infrastructure (504) Product management (412)

Distances

CITY Calculation (straight line) Source: U.S. Census Bureau Gazetteer (coordinates) · Data: 2024

How far San Francisco, CA is from the 20 largest US cities, in straight-line miles (not driving distance). Click a column to sort.

Sort:

From ⇅	Straight-line distance (miles) ⇅	Population of that city ⇅
San Francisco, CA	0 mi	830,235
San Jose, CA	73 mi	990,138
Los Angeles, CA	364 mi	3,857,263
San Diego, CA	475 mi	1,389,526
Phoenix, AZ	678 mi	1,642,323
Seattle, WA	684 mi	754,195
Denver, CO	986 mi	718,877
Oklahoma City, OK	1,420 mi	697,125
Fort Worth, TX	1,484 mi	963,194
Dallas, TX	1,515 mi	1,307,930
San Antonio, TX	1,516 mi	1,479,835
Austin, TX	1,530 mi	979,539
Houston, TX	1,673 mi	2,328,253
Chicago, IL	1,885 mi	2,711,226
Indianapolis, IN	1,979 mi	885,860
Columbus, OH	2,142 mi	914,802
Charlotte, NC	2,332 mi	903,844
Jacksonville, FL	2,403 mi	977,670
Philadelphia, PA	2,550 mi	1,579,706
New York, NY	2,602 mi	8,483,844

Nearby cities with open jobs (within 120 miles)

Sort:

City ⇅	Straight-line distance ⇅	Open jobs ⇅
Alameda, CA	42 mi	15
Belmont, CA	43 mi	2
Redwood City, CA	47 mi	27
Palo Alto, CA	54 mi	24
Sunnyvale, CA	60 mi	12
Fremont, CA	62 mi	2
Pleasanton, CA	63 mi	1
Milpitas, CA	66 mi	2
Campbell, CA	67 mi	7
San Jose, CA	73 mi	661
Capitola, CA	79 mi	1
Sacramento, CA	103 mi	38

The employer

General Motors is the employer in this listing. See all its open jobs here →

Company description (from The Muse): At GM, we pioneer the innovations that move and connect people to what matters. Our vision is a world with zero crashes, zero emissions and zero congestion. Our diverse team of over 165,000 employees brings their collective passion for engineering, technology and design to deliver on this ambitious future. And the bold commitments we’ve made are moving us closer to realizing this vision. We firmly believe that each of us has the power to drive meaningful change through our words, actions, and culture. At General Motors, we foster a culture where all ideas are taken into consideration, and where everyone can contribute to their fullest potential. Our foundation is built on respect, integrity, accountability, and equality.

size: Large Size; industries: Consumer Goods & Services, Engineering, Manufacturing, Technology; locations on The Muse: Detroit, MI.

It is among the 200 largest US-headquartered employers in our list, with about 156,000 employees worldwide (2025). Company page with headquarters, state economy and official links →

Official links for General Motors

The employer's profile on The Muse →

What it is like to work at a company (culture, schedules, benefits) is not in the source data, so we do not describe it. Read the description and ask the questions under "How to prepare".

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FAQ

What is the Senior AI/ML Engineer job at General Motors?

A senior level role in technology (data, analytics and ai) listed 7 days ago. The full description is on this page and on The Muse.

Where is this job?

San Francisco, CA.

How do I apply?

Use the "View this job and apply on The Muse" button. AtiveSite does not receive applications.

How much does it pay?

The listing does not publish pay, and we could not match the title to one official occupation.

What is the cost of living there?

Median rent in San Francisco is $2,476 a month and median household income $140,970 (ACS 2020–2024).

How old is this listing?

It was posted on 2026-11-01 (7 days ago). Jobs older than 90 days are not shown.

Are there similar jobs?

Yes: browse data, analytics and ai jobs in California.

Sources and terms

Job data: The Muse public API (listing dated 2026-11-01). The job disappears from this site when it disappears from the source.
Wages and projections: BLS OEWS (May 2025) and Employment Projections 2025–35.
City and county figures: Census Bureau ACS 2020–2024, BEA, BLS QCEW and LAUS, NASA POWER climatology; distances from Census Gazetteer coordinates.

AtiveSite is not affiliated with or endorsed by The Muse, the employer or any government agency, and does not process applications. Verify every detail with the employer before acting.

Sources

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