AI Engineer - Build Production-Grade LLM Systems

Innovaccer Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Generous PTO

Parental Leave

Rewards & Recognition

Insurance Benefits

Job summary

Innovaccer is seeking an AI Engineer to join the growing AI team and build production-grade AI systems at scale. Collaborate with product, engineering, and data teams to design, develop, and deploy AI-powered applications including LLM-based solutions, AI agents, and RAG workflows.

The ideal candidate combines hands-on coding with a strong ML research mindset, comfortable from experimentation to deployment and optimization of end-to-end AI systems.

Qualifications

• MS or PhD in Computer Science, Machine Learning, or related quantitative field; exceptional BS candidates considered.
• First-author NeurIPS/ICML/ICLR/ACL/EMNLP-like publications or meaningful open-source ML contributions.

Responsibilities

• You take ideas from paper to prototype to production, building AI-powered applications at scale.
•  “You build the model layer of a real product, choosing models, composing systems, and meeting product-level accuracy goals.
•  “You write real code in Python and PyTorch and optimize training speed.
•  “You design experiments with hypotheses, ablations, and rigorous evaluation.
•  “You read current research and distinguish durable techniques from fleeting trends.
•  “You explain work to non-AI stakeholders, including clinicians and operators.

Skills

Python

PyTorch

Multi-GPU training

Open-weight model fine-tuning

HuggingFace/DeepSpeed/vLLM

Experiment design

Production deployment

Education

MS or PhD in Computer Science / ML

Tools

FSDP/DeepSpeed

vLLM

PyTorch distributed

Job description

Innovaccer is seeking an AI Engineer to join the growing AI team and build production-grade AI systems at scale. Collaborate with product, engineering, and data teams to design, develop, and deploy AI-powered applications including LLM-based solutions, AI agents, and RAG workflows.

The ideal candidate combines hands-on coding with a strong ML research mindset, comfortable from experimentation to deployment and optimization of end-to-end AI systems.