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AI/ML Engineer - Model Inference

  • 위치
    • Sunnyvale, California
  • 직무 유형 Full time
  • 게시됨
  • Job Requisition JR-202613719

설명

The Team

Cola is part of GM’s autonomous vehicle effort, focused on helping teams discover, understand, and curate high-value data from large-scale real-world sensor streams. The team sits at the intersection of machine learning, data infrastructure, and developer productivity, building systems that make it easier to search for important scenarios, prepare training-ready data, and support fast iteration across perception and evaluation workflows.

Our goal is to make world understanding scalable, practical, and cost efficient for embodied AI systems. We believe the next generation of autonomy and robotics depends not only on stronger models, but also on better infrastructure for turning massive volumes of multimodal data into reusable signals, searchable artifacts, and high-quality evaluation loops. That means building systems that can operate at industrial scale while preserving the flexibility to adapt quickly to new questions, new edge cases, and new model capabilities.

A core idea behind how we work is EMWU, or Efficient Multi-Tier World Understanding. At a high level, EMWU is a cost-aware approach that first performs the cheapest reusable work, such as detection, featurization, and retrieval, and then applies deeper reasoning only where it adds meaningful value. This operating model reflects how Cola engineers think: build durable intermediate artifacts, design for scale from the start, and balance quality, speed, and cost instead of optimizing any one of them in isolation.

The Role

We are looking for a hands-on machine learning engineer to help build the data processing, featurization, and inference foundations that power scalable world understanding. This role is ideal for someone who is equally comfortable working on machine learning systems, production infrastructure, and evaluation loops, and who enjoys turning ambiguous problems into practical, reliable solutions.

What You’ll Do

  • Design, build, and productionize data processing and featurization pipelines for large-scale multimodal data

  • Improve inference frameworks for computer vision and multimodal models, with a focus on reliability, extensibility, and operational simplicity

  • Drive scalability and cost efficiency across the end-to-end pipeline, including compute utilization, throughput, storage, and query performance

  • Work closely with partners across machine learning, infrastructure, and evaluation to deliver systems that support both rapid experimentation and production use

  • Develop and refine evaluation methods for model quality, retrieval quality, and system-level performance

  • Help shape technical direction through strong execution, thoughtful tradeoff analysis, and clear engineering judgment

  • Take ownership of ambiguous problem spaces, define practical paths forward, and move quickly from prototype to production

  • Operate with urgency and a strong bias toward execution velocity while maintaining a high bar for engineering quality

Your Skills & Abilities

  • BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related technical field, or equivalent practical experience

  • Experience building production data processing or machine learning pipelines at scale

  • Experience with featurization, embedding, inference, or retrieval systems for vision or multimodal workloads

  • Strong understanding of computer vision models and the practical challenges of deploying them in production environments

  • Experience evaluating machine learning systems using clear metrics, experiments, and regression safeguards

  • Proven ability to work hands-on in fast-moving environments with incomplete information

  • Strong ownership mindset, sound technical judgment, and the ability to drive execution through ambiguity

What Will Give You A Competitive Edge

  • Experience with world models or large-scale world understanding systems

  • Experience with simulation workflows or synthetic data systems

  • Experience with vector search, approximate nearest neighbor retrieval, or large-scale embedding infrastructure

  • Experience working on embodied AI, autonomous systems, or safety-critical machine learning applications

Compensation

  • The salary range for this role is $117,700 and $221,400. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position (along with level.) 

  • 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. 

이 직무는 재택 기반이지만, 선발된 지원자가 GM 허브에서 특정 거리 이내에 거주하는 경우 주 3회 {또는 관리자가 지정한 다른 빈도로} 출근해야 합니다.

이 직무는 리로케이션 혜택을 받을 수 있습니다.

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.