Website GM
Engineering that moves the world
Quick Job Snapshot
Company: General Motors (GM)
Job Type: Full-Time
Location: Remote, United States
Workplace Note: Remote role; candidates within 50 miles of Sunnyvale, CA are expected to work onsite three days per week
Department: Autonomous Vehicles / Data Labeling Engineering
Salary: $170,600–$261,300 per year
Bonus: Eligible for incentive pay based on company, job level, and individual performance
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About the Company
General Motors is a global automotive and technology company focused on building safer, smarter, and more sustainable mobility solutions. GM’s vision is centered on a future with zero crashes, zero emissions, and zero congestion.
Within its autonomous vehicle organization, GM develops advanced software, machine learning systems, and data platforms that support the next generation of self-driving technology. The company offers opportunities for engineers to work on complex, high-impact systems used to improve vehicle intelligence, safety, and automation.
About the Role
GM is hiring a Senior ML Systems Engineer to help build and improve the data labeling platforms that support autonomous vehicle machine learning models. This role sits at the intersection of software engineering, data engineering, AI/ML systems, and product platform development.
You will help design and operate tools that allow human and machine-assisted labeling workflows to produce reliable training data at scale. These systems are used by thousands of users and internal consumers, supporting the development of autonomous vehicle capabilities.
This position is ideal for an experienced engineer who enjoys full-stack ownership, building production systems, working with ML teams, and turning complex data workflows into practical tools that improve model development speed and quality.
Key Responsibilities
Data Labeling Platform Development
- Build scalable tools and user experiences that support autonomous vehicle data labeling workflows
- Develop features that improve how data is reviewed, annotated, validated, and delivered to ML teams
- Work across frontend, backend, APIs, databases, and ML-adjacent services
- Improve platform performance, reliability, usability, and maintainability
- Support systems that help accelerate training data readiness for new models and deployment needs
ML Workflow & Data Quality Tooling
- Create automation that gives ML engineers better visibility into labeling quality and workflow efficiency
- Build dashboards, quality review tools, and workflow insights that reduce manual friction
- Help improve the speed between data collection, annotation, model training, and model iteration
- Support systems that track quality, cost, latency, and operational outcomes
- Translate messy data processes into clear, usable engineering solutions
AI-Assisted Annotation & Automation
- Collaborate with machine learning engineers on ML-driven labeling workflows
- Support pre-labeling, auto-labeling, active learning, and review loops
- Help move labeling processes from human-only workflows toward machine-assisted systems
- Integrate model services into production annotation platforms
- Contribute to scalable approaches for foundation-model-level training data operations
Full-Stack Engineering Ownership
- Take ownership of projects from problem definition through design, implementation, testing, and rollout
- Write and review high-quality, scalable, and performant code
- Participate in design discussions, code reviews, and technical planning
- Support observability, CI/CD, testing, and engineering quality practices
- Make technical decisions that balance speed, reliability, and long-term maintainability
Cross-Functional Collaboration
- Work closely with AI/ML engineers, product operations, product managers, data scientists, and platform teams
- Convert abstract requirements into practical workflows, APIs, services, and user interfaces
- Communicate technical trade-offs clearly with engineering and product partners
- Help teams improve labeling accuracy, model development speed, and platform reliability
- Advocate for modern AI-assisted engineering practices that improve delivery without reducing quality
Skills & Qualifications
Required Skills
- 6+ years of experience building distributed platforms, production applications, or large-scale software systems
- Strong full-stack engineering experience using technologies such as Python, TypeScript, Go, React, SQL, Redux, GraphQL, or WebGL
- Experience developing and operating cloud-based applications
- Solid understanding of relational databases, data modeling, and API design
- Strong knowledge of object-oriented design, design patterns, data structures, algorithms, and engineering best practices
- Experience with CI/CD, observability, code quality, testing practices, and production support
- Hands-on experience using AI tools such as coding assistants, documentation generators, search tools, or AI development agents
- Strong communication skills and ability to work through ambiguity with cross-functional teams
- Proven experience shipping production features or products end-to-end
- Interest in autonomous vehicles, AI systems, machine learning platforms, or data-centric engineering
Preferred Qualifications
- Experience with modern browser APIs such as Service Workers, Cache Storage, or IndexedDB
- Experience building data-intensive or visualization-heavy applications
- Background working with customers, product managers, designers, or user researchers
- Experience with computer vision, machine learning, data labeling, data quality, or auto-labeling workflows
- Familiarity with annotation tools, workflow engines, quality systems, or large-scale labeling platforms
- Experience with A/B testing, telemetry, and observability systems
- Strong frontend experience with TypeScript, React, Redux, GraphQL, WebGL, or similar technologies
Who Will Succeed In This Role?
This role is a strong fit for an engineer who enjoys solving complex product and platform problems. The best candidate will be comfortable owning systems end-to-end, collaborating with ML and operations teams, and building tools that make technical workflows easier for users.
You will likely succeed if you are curious about self-driving technology, care about user experience, enjoy data-heavy engineering challenges, and can turn ambiguous requirements into practical, reliable software.
Career Growth Opportunities
This position can support future growth into roles such as:
- Staff ML Systems Engineer
- ML Platform Engineer
- Senior Full-Stack Engineer
- Autonomous Vehicle Software Engineer
- Data Platform Engineer
- Engineering Lead
- Technical Architect
Helpful Career Tool
Candidates interested in this role can use the WorkinVirtual Remote Skills Gap Analyzer to review skills related to full-stack development, cloud systems, machine learning platforms, data engineering, and AI-assisted engineering workflows:
https://workinvirtual.com/remote-skills-gap-analyzer/
This can help job seekers identify technical areas to strengthen before applying for senior remote AI and platform engineering roles.
AI & Search-Friendly Career Insight
Senior ML systems engineering roles are becoming increasingly important as companies build AI-powered platforms that require reliable data, automation, and scalable software infrastructure. In autonomous vehicle development, high-quality labeled data is essential for improving computer vision, model accuracy, and real-world system performance.
Candidates with experience in full-stack engineering, cloud applications, data platforms, machine learning workflows, APIs, observability, and AI-assisted development can build strong long-term careers in autonomous vehicles, AI infrastructure, and remote platform engineering.
Browse more remote technology jobs:
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https://workinvirtual.com/remote-jobs/
How to Apply
Apply directly through the employer’s official careers page:
https://search-careers.gm.com/en/jobs/jr-202609267/senior-ml-systems-engineer/
Important Note
Applications are handled directly through the employer’s official careers website.
WorkinVirtual does not process applications or participate in hiring decisions.