Principal Machine Learning Engineer

Job Title

Principal Machine Learning Engineer

Protingent Staffing is offering a direct‑hire Principal Machine Learning Engineer role that is fully remote. This role is a deep technical authority responsible for designing and evolving the most critical ML systems across training, inference, evaluation, and infrastructure.

• Architect and build large‑scale ML systems spanning data, training, evaluation, inference, and deployment.
• Design reproducible, high‑performance training pipelines across GPU infrastructure.
• Architect inference systems that balance latency, throughput, cost, and reliability at scale.
• Design and maintain data systems for high‑quality synthetic and real‑world training data.
• Implement evaluation pipelines for performance, robustness, safety, and bias in partnership with research leadership.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
• Make pragmatic trade‑offs and ship improvements quickly, learning from real usage.
• Work under real production constraints: latency, cost, reliability, and safety.
• Ensure ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets.
• Deploy models that achieve measurable quality improvements and meet user‑impact goals.
• Proactively monitor, debug, and resolve production issues with clear root‑cause analysis.
• Provide clear guidance, best practices, and scalable ML solutions for teammates and cross‑functional collaborators.
• Enable efficient, safe research‑to‑production cycles that continuously improve the product experience.

• 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 (Deep Speed, FSDP, Megatron, ZeRO, Ray).
• Strong software engineering fundamentals – 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.
• Bias toward shipping, learning fast, and improving systems through iteration.
• Must have experience with LLM inference frameworks such as vLLM, TensorRT‑LLM, or Faster Transformer.
• 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).

• Job Type: Direct Hire
• Pay Range:
  Market Rate
• Location:
  
  Fully Remote