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Innovaccer is seeking a Senior AI Engineer to join its growing AI team and build production-grade AI systems that solve complex healthcare problems at scale. You will work across the full AI development lifecycle—from experimentation and prototyping through production deployment and optimization—collaborating closely with product, engineering, and data teams.
In this role, you will design, develop, and deploy AI-powered applications including LLM-based solutions, AI agents, retrieval-augmented generation (RAG), and intelligent automation workflows. You will take ideas from concept to prototype to production, building the model layer of real products while maintaining product-level accuracy bars. You will write production Python and PyTorch code with sufficient systems knowledge to optimize training performance, design rigorous experiments with clear hypotheses and ablations, and measure results skeptically—building evaluation frameworks before models.
You will read current research to distinguish techniques with lasting value from hype, explain technical work to non-AI stakeholders (including clinicians and operators), and demonstrate hands-on experience with the modern training and serving stack (HuggingFace, FSDP/DeepSpeed, vLLM/SGLang). You should have fine-tuned open-weight models, understand parameter-efficient versus full fine-tuning trade-offs, and have shipped models in production at scale (tens of GPUs minimum, models in the tens of billions of parameters or larger). You will own data pipelines feeding real training runs, including deduplication, filtering, decontamination, and format normalization. Mentoring and setting direction for other engineers is valued and will become increasingly important as the team grows.
The ideal candidate holds an MS or PhD in Computer Science, Machine Learning, or a related quantitative field (exceptional BS candidates with substantial research or open-source contributions will be considered). You should have first-author publications at top venues (NeurIPS, ICML, ICLR, ACL, EMNLP), meaningful open-source ML contributions, research internship experience at an AI lab, or shipped models with real user adoption. You must demonstrate the ability to scope ambiguous problems into concrete plans and distinguish research questions from engineering tasks.
