Staff ML Engineer (Embodied AI)

Location: Seattle, Washington or Sunnyvale, California, United States

Company

hirify.global develops autonomous driving technology for L2–L4 vehicles, with a vision of Zero Crashes, Zero Emissions and Zero Congestion.

What you will do

• Design experiments connecting dataset composition, sampling, curricula and scaling laws to model behavior.
• Apply self-supervised pre-training, imitation learning, reinforcement learning and foundation-model fine-tuning to driving behavior, trajectory generation and perception.
• Develop data curation and mining methods including auto-labeling, deduplication, difficulty and uncertainty estimation, and long-tail and out-of-distribution scenario discovery.
• Define offline metrics that predict on-road behavior and use evidence to guide model and data decisions.
• Trace model failures to data root causes, specify corrective data, and train models across large multi-GPU and multi-node datasets.
• Collaborate with platform and cross-functional teams to bring models into onboard driving systems and document best practices.

Requirements

• Master’s or PhD in Computer Science, Robotics or Machine Learning.
• Strong machine learning fundamentals, including experiment design, baseline selection, ablation analysis and distinguishing signal from noise.
• Proficiency in Python and PyTorch, with experience training models on large datasets.
• Hands-on data-centric ML experience covering curation, sampling, labeling or evaluation of large training sets.
• Working knowledge of large-scale foundation-model pre-training, fine-tuning and alignment.
• Solid data analysis skills with NumPy and Pandas, plus SQL or Spark for large datasets.
• Ability to deliver applied ML results under real-world constraints and timelines.
• Clear communication with both engineers and non-experts.

Nice to have

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

Culture & Benefits

• Work with real-world fleet driving data and synthetic simulation data at the scale of billions of examples.
• Investigate open research questions in embodied driving data and scaling laws, with support for publishing findings.
• See model results reflected in vehicle behavior on real roads.
• Access total rewards and benefits supporting well-being at work and at home.
• Inclusive, non-discriminatory workplace with reasonable accommodations available to applicants.