Startups · AI

Machine Learning Engineer, Driving Foundation Models

42dot · Sunnyvale, United States · On-site

← All jobs
About 42dot

42dot is a technology company pioneering frictionless transportation services. 42dot develops technologies and platforms integrating autonomous driving, connected vehicles, and smart city infrastructure. Backed by Space Capital.

About the role

Develop driving and physical AI foundation models. Support Aria VLA model development. Data Curation & Pretraining: Design and manage massive, multi-terabyte datasets, including data scraping, filtering, deduplication, tokenization, and synthetic data generation. Found on 1752vc Careers, the job board for startup and VC roles.

What they're looking for

  • Languages: Python, C++, and Golang (for high-throughput systems)
  • Frameworks: PyTorch, JAX, Hugging Face ecosystem, vLLM
  • Infrastructure: Kubernetes, Docker, multi-node distributed training (Megatron-LM, DeepSpeed), and cloud platforms (AWS, Azure, Google Cloud)
  • Math & Theory: Linear algebra, calculus, probability, statistics, and deep learning architectures (Transformers, diffusion models)
More about this role

42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles.

We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.

Develop driving and physical AI foundation models. Support Aria VLA model development.

Data Curation & Pretraining: Design and manage massive, multi-terabyte datasets, including data scraping, filtering, deduplication, tokenization, and synthetic data generation.

Distributed Model Training: Scale training across massive GPU/TPU clusters using parallel processing, custom data loaders, and framework optimization (PyTorch, JAX, or TensorFlow).

Post-Training & Alignment: Implement instruction tuning, Reinforcement Learning from Human Feedback (RLHF), or direct preference optimization to align models with safety...

Read the full posting on 42dot's site ↗

ENGINEERING

Build your edge while you search

Free tools for founders and investors, plus VC Unfiltered, our take on startups, venture and the people who build them.