42dot Machine Learning Engineer to develop driving and physical AI foundation models and support Aria VLA model development. Responsibilities include data curation, distributed model training, post-training alignment, and model optimization.
Responsibilities
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 and behavior goals.
Inference & Optimization: Compress and optimize models (quantization, pruning, speculative decoding) to run at low latency on cloud infrastructure or edge hardware like smartphones and robotics.
Evaluation & Guardrails: Build benchmarking harnesses, hallucination checkers, and red-teaming pipelines to test model accuracy and safety.
Qualification
LanguagesFrameworksInfrastructureMath & TheoryInterview Process
Required
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).
