Senior Artificial Intelligence Engineer

Ovii's Interpretation of the Role

We seek a senior AI engineer to design, build, and ship production‑grade AI systems for healthcare. The role spans the full AI lifecycle—from research prototypes to large‑scale model training, serving, and real‑world evaluation.

Role Snapshot

• Senior AI Engineer
• Build production AI systems
• Work with LLMs, RAG, AI agents
• Cross‑functional collaboration
• Mentor engineers (optional)

Must-Have Requirements

• Python
• PyTorch
• Large‑scale multi‑node GPU training
• Model serving frameworks (HuggingFace, vLLM, SGLang)
• Distributed training tools (DeepSpeed, FSDP)
• Data pipeline engineering
• Experiment design & evaluation
• Production deployment & optimization
• hands‑on AI model development
• large‑scale training
• production deployment
• MS or PhD in Computer Science, Machine Learning, or related quantitative field

Nice-to-Have Signals

• Research publication record
• Open‑source ML contributions
• Mentoring or setting direction for other engineers
• research publications
• open‑source contributions
• BS with substantial research or open‑source work

Work Setup

• Location: San Francisco, United States
• Work mode: ONSITE
• Employment type: Full-Time

Eligibility Gates

• Visa sponsorship: unknown

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Education requirement specifics
• Certifications
• Relocation
• Notice period
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Design, develop, and deploy AI‑powered applications such as LLM‑based solutions, retrieval‑augmented generation, and AI agents
• Take ideas from research prototype to production, selecting model sizes, composing multiple models, and meeting accuracy and latency targets
• Write production‑grade Python code using PyTorch and related libraries
• Build and maintain large‑scale data pipelines for training (deduplication, filtering, decontamination, format normalization)
• Run experiments, formulate hypotheses, perform ablations, and evaluate results rigorously
• Optimize training and serving performance with tools like DeepSpeed/FSDP and vLLM/SGLang
• Communicate model behavior and results to clinicians, operators, and other non‑technical stakeholders
• Guide or mentor junior engineers as the team grows (optional)

Good Fit If You Have

• Strong research background with first‑author papers or notable open‑source work
• Hands‑on experience training multi‑node models with tens of billions of parameters
• Proven ability to move AI prototypes into production systems
• Curiosity‑driven learning and staying current with AI research