Principal AI Engineer: Build Production-Grade ML Systems

Innovaccer Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Generous PTO

Parental Leave

Rewards & Recognition

Insurance Benefits

Job summary

Innovaccer Inc. is seeking a Principal AI Engineer to design and deliver production-grade AI systems at scale. You will take ideas from paper to prototype to deployment, building the model layer as part of an end-to-end product.

You will lead multi-GPU, multi-node training, decide model composition, and ensure product-level accuracy and latency targets while communicating with clinicians and operators.

Qualifications

• MS or PhD in Computer Science, Machine Learning, or a related quantitative field. Exceptional BS candidates with substantial research or open-source work will be considered.
• First-author publications at NeurIPS, ICML, ICLR, ACL, EMNLP, or similar; meaningful open-source ML contributions; a research internship at an AI lab; or models you trained and shipped that people actually used.
• Fine-tuned an open-weight model yourself and can explain parameter-efficient vs full fine-tuning.

Responsibilities

• Build production-grade AI systems that solve complex problems at scale.
• Design experiments with clear hypotheses and ablations.
• Explain work to non-AI stakeholders, including clinicians and operators.
• Lead multi-node training runs and data pipelines feeding large models.

Skills

Python

PyTorch

Distributed training

Model deployment

Research publications

Multi-GPU training

System design

Leadership

Mentoring

Communication with clinicians

Education

MS in Computer Science or related

PhD in CS/ML preferred

Exceptional BS with research

Tools

HuggingFace

FSDP/DeepSpeed

vLLM

Docker

Job description

Innovaccer Inc. is seeking a Principal AI Engineer to design and deliver production-grade AI systems at scale. You will take ideas from paper to prototype to deployment, building the model layer as part of an end-to-end product.

You will lead multi-GPU, multi-node training, decide model composition, and ensure product-level accuracy and latency targets while communicating with clinicians and operators.