Machine Learning Engineer, Driving Foundation Models

42dot • San Francisco, CA

1d • $175,000 - $250,000

About The Position

Develop driving and physical AI foundation models and support Aria VLA model development. The role covers large-scale data curation and pretraining, distributed model training, post-training alignment, inference optimization, and evaluation and safety guardrails.

Requirements

• Proficiency in Python, C++, and Golang for high-throughput systems.
• Experience with PyTorch, JAX, the Hugging Face ecosystem, and vLLM.
• Knowledge of Kubernetes, Docker, multi-node distributed training with tools such as Megatron-LM or DeepSpeed, and cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of linear algebra, calculus, probability, statistics, and deep learning architectures including Transformers and diffusion models.

Responsibilities

• Design and manage massive, multi-terabyte datasets, including data scraping, filtering, deduplication, tokenization, and synthetic data generation.
• Scale model training across large GPU/TPU clusters using parallel processing, custom data loaders, and framework optimization.
• Implement instruction tuning, Reinforcement Learning from Human Feedback (RLHF), or direct preference optimization to align models with safety and behavior goals.
• Compress and optimize models using techniques such as quantization, pruning, and speculative decoding for low-latency operation on cloud infrastructure or edge hardware.
• Build benchmarking harnesses, hallucination checkers, and red-teaming pipelines to test model accuracy and safety.
• Support development of the Aria VLA model.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed