2 days ago
Base Salary
$175k - $250k/yr
Responsibilities
- Design and manage multi-terabyte datasets through scraping, filtering, deduplication, tokenization, and synthetic data generation.
- Scale distributed model training across GPU and TPU clusters using parallel processing, custom data loaders, and framework optimization.
- Implement instruction tuning, RLHF, and direct preference optimization for model alignment with safety and behavior goals.
- Compress and optimize models using quantization, pruning, and speculative decoding for low-latency cloud and edge inference.
- Build benchmarking harnesses, hallucination checkers, and red-teaming pipelines to evaluate model accuracy and safety.
- Develop driving and physical AI foundation models and support Aria VLA model development.
Requirements
- Proficiency in Python, C++, and Golang for high-throughput systems.
- Experience with PyTorch, JAX, the Hugging Face ecosystem, and vLLM.
- Experience with Kubernetes, Docker, multi-node distributed training, Megatron-LM, and DeepSpeed.
- Experience with AWS, Azure, or Google Cloud.
- Knowledge of linear algebra, calculus, probability, statistics, deep learning architectures, Transformers, and diffusion models.
Categories
About 42dot
42dot builds software-defined vehicle and autonomous mobility platforms for automakers and mobility service providers. Its products include AI-driven vehicle software, data-based update services, and core platform components such as API gateways and identity/access management to connect services securely. The privately held company is headquartered in Seongnam-si, Gyeonggi-do, and focuses on transportation and automotive markets.