설명
Vacancy Status:
No: This posting is not for an existing vacancy within the organization and is open to new applications. (New Head Count)
AI Disclosure:
As part of the application process, Artificial Intelligence will be used in the hiring process for this role.
Hybrid - This role is categorized as hybrid. This means the successful candidate is expected to report to Markham three times per week, at minimum [or other frequency dictated by the business].
At General Motors, we're turning today's impossible into tomorrow's standard. Our vehicles already move millions of people every day, and we're building the autonomy that will drive them - L2 through L4, on real roads, at real scale. Making self-driving safe at that scale is one of the hardest AI problems there is.
The Data Scaling team owns the data flywheel for AV foundation model pre-training and SFT. We determine what data the AV needs in order to learn driving behaviors at scale, and we define what data quality means across the loop. The team delivers ML models that move the product up the data scaling curves, turning better data composition into measurably better driving behavior. We work with the very large datasets GM already has and we define the next generation of highest-value datasets GM collects. With each major release we aim to 10x the effective data behind our models: more scale, more diversity, and more value extracted from every example.
Why Join Us?
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Train on driving data almost nobody else has - real-world miles from GM's fleets, plus synthetic sim data - scaling into billions of examples. Then decide which ones are worth it: ten thousand near-identical highway miles teach the model less than one unprotected left turn in the rain. Mixture design, curation, mining, and evaluation are how you find out which is which, working alongside other MLEs and research scientists.
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Work on questions with no textbook answers yet. Scaling laws for language are well mapped by now; for embodied driving data - heavy-tailed, safety-constrained, closed-loop - they aren't. You'd be helping write them, and we support publishing what you find.
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See your results in the world rather than on a leaderboard. The models this team ships change how the vehicle behaves on real roads, and that behavior comes back as the evidence for your next iteration.
As a Senior AI/ML Engineer in the Embodied AI Data Foundations organization, you will be an individual contributor developing data-centric AI solutions that directly improve autonomous driving performance. You will design and run the data curation and model training recipes that produce models capable of safe, reliable behavior across diverse real-world scenarios, drawing on both real and synthetic data.
What You'll Do
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Design and run experiments that connect data composition to model behavior: dataset mixtures, sampling strategies, curricula, and scaling-law studies that tell us where to invest next.
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Apply methods such as self-supervised pre-training, imitation learning, reinforcement learning, and foundation-model fine-tuning to driving behavior, trajectory generation, and perception tasks.
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Develop data curation and mining methods - auto-labeling, deduplication, difficulty and uncertainty estimation, long-tail and out-of-distribution scenario discovery - to raise the value of every training example.
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Define offline metrics and evaluations that actually predict on-road behavior, and use them to make model and data decisions from evidence rather than intuition.
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Trace model failures back to their root cause in the data, then close the loop by specifying the data needed to fix them.
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Train models at scale across large multi-GPU/multi-node datasets, partnering with platform teams on the pipelines and tooling this requires.
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Collaborate with cross-functional teams to bring models into onboard driving systems, and document learnings and best practices along the way.
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Follow relevant literature and bring promising advances into our recipes and evaluations.
Your Skills and Abilities (Required Qualifications)
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Master's or PhD in Computer Science, Robotics, Machine Learning.
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Strong ML fundamentals: you can design a clean experiment, pick the right baseline, read an ablation, and tell signal from noise.
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Proficiency in Python and PyTorch, with experience training models on large datasets.
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Hands-on experience with data-centric ML: curation, sampling, labeling, or evaluation of large training sets.
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Working knowledge of large-scale foundation models and how they are pre-trained, fine-tuned, and aligned.
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Solid data analysis skills (NumPy, Pandas; SQL or Spark for large datasets).
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Demonstrated ability to deliver applied ML results under real-world constraints and timelines.
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Clear communication: you can explain a result and its limits to both engineers and non-experts.
Preferred:
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PhD, publications, or open-source contributions in representation learning, multimodal or vision-language models, generative models, RL, or data-centric ML.
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Experience with robotics, autonomous driving, or other embodied AI systems.
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Experience with synthetic and simulation data, including sim-to-real transfer.
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Familiarity with production ML deployment workflows.
Compensation:
The salary range for this role is $125,000 to $174,500. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP NOW OR IN THE FUTURE
Benefits:
The goal of the General Motors of Canada total rewards program is to support the health and well-being of you and your family. Our comprehensive compensation plan currently includes the following benefits, in addition to many others:
- Paid time off including vacation days, holidays, and supplemental benefits for pregnancy, parental and adoption leave.
- Healthcare, dental and vision benefits including health care spending account and wellness incentive.
- Life insurance plans to cover you and your family.
- Company and matching contributions to a Defined Contribution Pension plan to help you save for retirement.
- GM Vehicle Purchase Plan for you, your family, and friends.
다양성 정보
General Motors는 법적으로 금지된 차별을 배제하는 것은 물론 포용성과 소속감을 진정으로 장려하는 직장이 되기 위해 노력하고 있습니다. 당사는 다양성이 보장되는 환경에서 직원들이 역량을 발휘하고 우리 고객을 위한 더 좋은 제품을 개발할 수 있다고 믿습니다. 따라서 입사에 관심 있는 사람이 있다면 포지션별 주요 업무와 자격을 확인하고 본인이 보유한 기술과 능력에 부합하는 모든 포지션에 적극적으로 지원하기를 장려합니다. 지원자는 채용 과정에서 역할 관련 평가(해당하는 경우) 및/또는 채용 전 스크리닝을 통과해야 합니다. 자세한 정보는 GM 채용 과정 안내를 참고하십시오.
공평한 취업 기회 선언 (미국)
General Motors는 공평한 기회를 제공하는 고용주임을 자부합니다. 자격을 만족하는 지원자는 인종과 피부색, 성별, 성적 지향, 성별 정체성, 국적, 장애, 재향 군인 보호법 적용 여부와 상관없이 채용 후보로서 심사를 받습니다.
숙소 (미국 및 캐나다)
General Motors는 장애인을 포함한 모든 구직자들에게 취업 기회를 제공합니다. 구직이나 취업 지원에 도움이 되는 합리적인 숙소가 필요한 경우 [email protected]으로 이메일을 보내시거나 800-865-7580으로 전화주십시오. 이메일에, 귀하가 요청하는 특정한 숙소에 대한 설명과 귀하가 지원하는 직무와 채용 요청서 번호를 포함해주세요.