ML Engineer - Remote

About The Company

General Motors is a global leader in automotive innovation, committed to shaping the future of mobility with a vision of Zero Crashes, Zero Emissions, and Zero Congestion. We embrace the responsibility to lead transformative change to make the world safer, more equitable, and environmentally sustainable. Our diverse team combines passion for engineering, technology, and design to develop autonomous driving technologies and advanced vehicle systems that move millions of people every day. We foster an inclusive workplace where every employee belongs and thrives, driving meaningful impact through collaboration and ingenuity.

About The Role

We are seeking a Staff AI/ML Engineer to join the Embodied AI Data Foundations team, a critical group within General Motors focused on advancing autonomous vehicle (AV) technology through data-centric artificial intelligence solutions. This role is an individual contributor position responsible for designing and executing data curation and model training strategies that improve AV performance across diverse, real-world driving scenarios. You will leverage both real-world fleet data and synthetic simulation data to develop and fine-tune foundation models that enable safe, reliable autonomous driving behaviors. The role requires close collaboration with cross-functional teams, including platform engineers and research scientists, to scale machine learning models and translate experimental results into impactful on-road behavior improvements. This position offers a unique opportunity to work on pioneering AI challenges with direct, visible impact on vehicle autonomy at scale.

Qualifications

• Master's or PhD in Computer Science, Robotics, Machine Learning, or a related field.
• Strong fundamentals in machine learning, including designing rigorous experiments, selecting baselines, and interpreting ablation studies.
• Proficiency in Python and PyTorch, with experience training models on large-scale datasets.
• Hands-on experience in data-centric machine learning practices such as data curation, sampling, labeling, and evaluation.
• Working knowledge of large-scale foundation models, including pre-training, fine-tuning, and alignment techniques.
• Advanced data analysis skills using tools such as NumPy, Pandas, SQL, or Spark for large datasets.
• Proven ability to deliver applied machine learning solutions under real-world constraints and timelines.
• Excellent communication skills, capable of explaining complex results to both technical and non-technical audiences.
• Preferred qualifications include a PhD, publications, or open-source contributions in areas such as representation learning, multimodal or vision-language models, generative models, reinforcement learning, or data-centric ML.
• Experience with robotics, autonomous driving, or embodied AI systems is a plus.
• Familiarity with synthetic and simulation data, including sim-to-real transfer methodologies.
• Knowledge of production ML deployment workflows is advantageous.

Responsibilities

• Design and conduct experiments linking data composition to model behavior, including dataset mixtures, sampling strategies, curricula, and scaling-law studies.
• Apply advanced machine learning methods such as self-supervised pre-training, imitation learning, reinforcement learning, and foundation-model fine-tuning to driving behavior, trajectory generation, and perception tasks.
• Develop and implement data curation and mining techniques, including auto-labeling, deduplication, difficulty and uncertainty estimation, and discovery of long-tail and out-of-distribution scenarios.
• Define and utilize offline metrics and evaluations that accurately predict on-road vehicle behavior to inform data and model decisions based on evidence.
• Analyze model failures to identify root causes in the data and specify data requirements to address these issues effectively.
• Train machine learning models at scale using multi-GPU and multi-node datasets, collaborating with platform teams to build necessary pipelines and tooling.
• Work closely with cross-functional teams to integrate models into onboard driving systems, documenting learnings and best practices throughout the process.
• Stay current with relevant academic literature and industry advances, incorporating promising techniques into team methodologies and evaluations.

Benefits

• Comprehensive total rewards package supporting well-being at work and home from day one.
• Opportunities for professional growth and career development within a leading global automotive company.
• Access to cutting-edge technology and large-scale real-world and synthetic datasets for AI research and development.
• Inclusive and collaborative work environment that values diversity and innovation.
• Flexible work arrangements including remote and hybrid options.
• Health and wellness programs, retirement plans, and other employee support services.

Equal Opportunity

General Motors is an Equal Employment Opportunity Employer committed to fostering an inclusive and diverse workplace. We prohibit discrimination and harassment of any kind based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic as outlined by federal, state, and local laws. We provide reasonable accommodations to qualified individuals with disabilities in the job application process and employment. All employment decisions are made without regard to any protected status. We encourage all qualified candidates to apply and join us in driving meaningful change and innovation.