Artificial Intelligence Engineer

About the Role

We are looking for an AI Engineer to join our growing AI team and help build intelligent, production-grade AI systems that solve complex problems at scale. In this role, you will work closely with product, engineering, and data teams to design, develop, and deploy AI-powered applications, including LLM-based solutions, AI agents, retrieval-augmented generation (RAG), and intelligent automation workflows.

The ideal candidate is hands-on, highly curious, and comfortable working across the full AI development lifecycle—from experimentation and prototyping to production deployment and optimization.

A Day in the Life

• Take an idea from paper to prototype to production. If you have only ever done one of those three, this role will stretch you, and we are fine with that if the rest is strong.
• Build the model layer of a real product, not just a model. That means choosing model sizes, composing several models into a working system, and holding a product-level accuracy bar.
• Write real code: Python fluently, PyTorch fluently, and enough systems sense to know why your training run is slow.
• Design experiments: state the hypothesis, run the ablation, and report the result that disagrees with you.
• Measure things: be suspicious of results that look good, and build the eval before you build the model.
• Read current research and distinguish between techniques that will hold up and those that will not.
• Explain your work to non-AI engineers, including clinicians and operators who will tell you when your output is wrong.

Requirements

• 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.
• Depth beyond coursework: 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.
• Experience fine-tuning an open-weight model yourself, understanding the difference between parameter-efficient and full fine-tuning, and ability to explain why you chose one.
• Strong Python and PyTorch skills. Familiarity with the current training and serving stack (HuggingFace, FSDP or DeepSpeed, vLLM or SGLang, or equivalents).
• Exposure to multi-GPU training, even at lab scale. You should know what a sharding strategy is and why it matters.
• Evidence you can finish things.

Benefits

• Generous PTO Benefits: 20 days of PTO per year.
• Parental Leave: One of the industry's best parental leave policies.
• Rewards & Recognition: Monetary incentives and widespread recognition for dedication and outstanding performance.
• Insurance Benefits: Medical, dental, and vision benefits, along with 100% company-sponsored short- and long-term disability and basic life insurance. Legal aid and pet insurance available at a discounted rate.