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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