Senior ML Engineer (Embodied AI Scaling Foundations)

Location: Remote or hybrid, associated with Sunnyvale, California, United States

Salary: $159,300–$230,700 annually, plus bonus potential

Company

hirify.global develops vehicles and autonomous driving systems focused on zero crashes, zero emissions, and zero congestion.

What you will do

• Design experiments connecting dataset composition, sampling strategies, curricula, and scaling laws to autonomous driving model behavior.
• Apply self-supervised pre-training, imitation learning, reinforcement learning, and foundation-model fine-tuning to driving behavior, trajectory generation, and perception.
• Build data curation and mining methods, including auto-labeling, deduplication, uncertainty estimation, and long-tail scenario discovery.
• Define offline metrics that predict on-road behavior and use evaluation evidence to guide model and data decisions.
• Train models across large multi-GPU and multi-node datasets and collaborate on the required pipelines and tooling.
• Bring models into onboard driving systems, investigate failures, and document technical learnings and best practices.

Requirements

• Master's or PhD in Computer Science, Robotics, Machine Learning, or a related field.
• Strong machine learning fundamentals and the ability to design experiments, select baselines, interpret ablations, and distinguish signal from noise.
• Proficiency in Python and PyTorch, with experience training models on large datasets.
• Hands-on experience with data-centric machine learning, including curation, sampling, labeling, or evaluation of large training sets.
• Working knowledge of foundation-model pre-training, fine-tuning, and alignment, plus strong data analysis skills with NumPy, Pandas, SQL, or Spark.
• Ability to deliver applied machine learning results under real-world constraints and communicate findings clearly to technical and non-technical audiences.

Nice to have

• PhD, publications, or open-source contributions in representation learning, multimodal or vision-language models, generative models, reinforcement learning, or data-centric machine learning.
• Experience with robotics, autonomous driving, embodied AI, synthetic or simulation data, sim-to-real transfer, or production machine learning deployment.

Culture & Benefits

• Work with large-scale real-world fleet data and synthetic simulation data for autonomous driving.
• Opportunity to investigate open research questions in embodied driving data and scaling laws, with support for publishing findings.
• Health, dental, vision, HSA, FSA, retirement savings, life insurance, paid vacation and holidays, tuition assistance, and employee assistance programs.
• GM vehicle discounts and potential relocation benefits.

Hiring process

• A role-related assessment and pre-employment screening may be required where applicable.