Senior​/Applied AI Engineer

New York City, NY (In-office)

$250k–$320k Base + Equity

Storm2’s client is a claims intelligence startup using AI and data to uncover fraud across property and casualty insurance. They’re solving a difficult problem where the data is messy, fraudsters adapt, and false positives have real consequences.

Frontier models work, but they’re expensive and slow ller open-weight models solve that problem, but only if you can preserve the reasoning quality.

That trade-off is the job.

You’ll own which models the company builds on, fine-tune open-weight LLMs for specific workflows, put them inside production agents, and build the evals that decide what actually ships. This is a Staff IC role with genuine architectural ownership, while staying hands‑on in Python and PyTorch.

• Fine-tuning open-weight LLMs with LoRA, QLoRA, SFT and DPO.
• Building production agents with tool use, structured outputs and guardrails.
• Turning real investigator feedback and agent traces into training data.
• Benchmarking models on accuracy, hallucinations, false positives, latency and cost.
• Serving and optimising models through vLLM, TGI, Triton or similar.
• Setting the technical direction for applied AI across the business.

• You’ve actually fine-tuned LLMs and can explain what moved the numbers.
• You’ve shipped agents into production, not just built demos.
• You’re strong in Python, PyTorch and the Hugging Face ecosystem.
• You’ve made real trade-offs between model quality, latency and cost.
• You enjoy problems where the ground truth is messy and the answer isn’t sitting neatly in a research paper.
• Experience with quantisation, distillation, entity resolution, graph ML or highly imbalanced detection is a big plus.

You won’t be building another wrapper around a frontier API. You’ll decide which models run which workflows, how they’re trained, and whether they’re good enough to replace much larger models at production scale
. At $250k–$320k base + equity
, they’re paying for someone who can make those calls and then build the systems themselves.