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Senior Machine Learning EngineerHydrogen UK Ltd • San Francisco, California, United States
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Senior Machine Learning Engineer

Hydrogen UK Ltd
  • US
    San Francisco, California, United States
  • US
    San Francisco, California, United States
Postuler Maintenant

À propos

On behalf of my client, I am hiring for a Senior Machine Learning Engineer – End-to-End (E2E) to help develop and scale learning-based systems that connect multi-modal perception inputs to autonomous driving behavior. In this role, you will contribute to building safe, efficient, and human-like autonomy solutions for real-world freight operations.
You will work at the intersection of perception, prediction, and planning, contributing to unified learning pipelines that operate in closed-loop environments. This is a highly hands-on engineering role focused on execution, experimentation, and delivery.
Key Responsibilities Develop and deploy end-to-end machine learning models that map multi-modal sensor inputs—including camera, LiDAR, radar, and maps—to driving-related outputs such as trajectories, cost functions, or intermediate representations Train and evaluate models using large-scale datasets from fleet logs, simulation environments, and synthetic data Analyze model performance, identify failure modes, and drive data-informed improvements in robustness and generalization Design and optimize training pipelines, data workflows, and evaluation strategies to improve iteration speed and model quality Contribute to architecture decisions involving transformers, imitation learning, reinforcement learning, BEV models, diffusion models, and vision-language-action systems Collaborate cross-functionally with Perception, Prediction, Planning, and Simulation teams to align learning systems across the autonomy stack Support integration of machine learning models into simulation and on-vehicle systems for closed-loop validation Improve experimentation workflows, tooling, and reproducibility practices Mentor junior engineers and contribute to technical discussions and engineering best practices Qualifications Bachelor’s degree with 6+ years, Master’s degree with 4+ years, or PhD with 0–2 years of experience in Machine Learning, Robotics, Computer Science, or a related field Strong publication record in top-tier conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, or CoRL is preferred Experience developing and deploying machine learning models for autonomous systems, robotics, or complex decision-making environments Strong programming skills in Python and PyTorch, with the ability to write production-quality code Experience training and evaluating models on large-scale datasets using distributed compute environments Solid understanding of modern ML architectures used in end-to-end systems, including Transformers, BEV models, VLM/VLA systems, and diffusion models Proven ability to debug model behavior, analyze performance metrics, and improve model performance iteratively Experience influencing model architecture and training strategies Strong collaboration skills and experience integrating ML systems into broader autonomy pipelines Preferred Qualifications Experience building end-to-end or mid-to-end models for autonomous driving or robotics Familiarity with vision-language models (VLMs) and vision-language-action (VLA) systems Experience with closed-loop simulation and evaluation frameworks Background in reinforcement learning or imitation learning for real-world systems Experience using distributed training frameworks such as Ray Understanding of vehicle dynamics, motion planning, or multi-agent systems ...
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  • San Francisco, California, United States

Compétences linguistiques

  • English
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