Ginastechjobs

Ginastechjobs

Principal Machine Learning Engineer, Artificial Intelligence (AI)

San Francisco, CA, United States · Principal

Sponsorship not specifiedDetected 13 days ago
Machine LearningDeep LearningPyTorchSparkNLPLLMsResearch

About the role

  • The Principal Machine Learning Engineer will operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems.
  • This is a hands-on, high-impact role focused on depth.
  • Our IT recruiting agencies and staffing companies can help.

Responsibilities

  • Architect and build large-scale ML systems spanning data, training, evaluation, inference, and deployment.
  • Design reproducible, high-performance training pipelines across GPU infrastructure.
  • Design and maintain data systems for high-quality synthetic and real-world training data.
  • Implement evaluation pipelines covering 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.

Requirements

  • Artificial Intelligence (AI) experience required.
  • 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).
  • Experience with GPU optimization, including memory efficiency, quantization, and mixed precision.
  • Comfort owning ambiguous, zero-to-one ML systems end-to-end.
  • Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer.
  • 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).

Benefits

  • Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
  • Principal Machine Learning Engineer Outcomes:
  • Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions.

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