ML Engineer - Austin, TX

Baasi is a stealth-mode startup building infrastructure for AI, seeking an ML Engineer to design and implement fine-tuning pipelines using LoRA/QLoRA with PyTorch and Hugging Face. The role focuses on productizing automated training pipelines for vendors to adapt language models. Responsibilities include implementing fine-tuning scripts, data preprocessing, and collaborating with backend engineers. The position offers flexible remote work with occasional in-office presence in Austin, TX.

• Implement and maintain LoRA/QLoRA fine-tuning pipelines using PyTorch + Hugging Face Transformers + PEFT.
• Develop logic for incremental training and adapter stacking, producing clean, versioned 'delta packs'.
• Automate data preprocessing (tokenization, formatting, filtering) for user-supplied datasets.
• Build training scripts/workflows that integrate with orchestration backends (Node.js, REST/gRPC, or job queues).
• Implement monitoring hooks (loss curves, checkpoints, eval metrics) to feed into dashboards.
• Collaborate with DevOps to ensure reproducible, portable training environments.
• Write tests to guarantee reproducibility and correctness of adapter outputs.

• Strong programming skills in Python.
• Hands-on experience with PyTorch and the Hugging Face ecosystem (Transformers, Datasets, PEFT).
• Familiarity with LoRA/QLoRA or parameter-efficient fine-tuning methods.
• Understanding of mixed precision training (FP16/BF16) and memory optimization techniques.
• Experience building training scripts that are production-ready (reproducibility, logging, error handling).
• Comfortable working in Linux GPU environments (CUDA, ROCm).
• Ability to collaborate with backend/frontend engineers who are not ML specialists.

• Experience with bitsandbytes, xformers, or flash-attention.
• Familiarity with distributed training (multi-GPU, NCCL, DeepSpeed, or Accelerate).
• Prior work in MLOps or packaging ML pipelines for deployment.
• Contributions to open-source ML libraries.