Principal Machine Learning Engineer

Requirements

• Strong background in deep learning and transformer-based architectures.
• Hands-on experience training, fine-tuning, or deploying large-scale ML models in production.
• Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly.
• Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, Ray).
• Strong software engineering fundamentals – you write robust, maintainable, production-grade systems.
• Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.
• Comfort owning ambiguous, zero-to-one ML systems end-to-end.
• A bias toward shipping, learning fast, and improving systems through iteration.

Ideal Experience

• Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.
• Contributions to open-source ML or systems libraries.
• Background in scientific computing, compilers, or GPU kernels.
• Experience with RLHF pipelines (PPO, DPO, ORPO).
• Experience training or deploying multimodal or diffusion models.
• Experience with large-scale data processing (Apache Arrow, Spark, Ray).