You'll build the data pipelines that ingest legal sources, the model stack that produces structured evidence, the evaluation frameworks that measure accuracy, and the fine-tuning loops that improve performance. The unusual constraint: you need speed, scale, correctness, and robustness simultaneously , at millisecond latency, zero downtime, heading toward billions of transactions where a single error costs a customer $20K.

Within weeks:

• Lead development of new features aimed at increasing TRAM’s test-time accuracy
• Work on the underlying data and retrieval pipelines that help power our AI workflows
• Work directly with our internal tax experts to understand how TRAM can better reason like them

Within months:

• Own TRAM’s eval framework and workflows
• Work directly with leading frontier labs to reinforce fine tune models on our proprietary data

Requirements:

• Prior experience building AI enabled products, particularly RAG systems
• Experience fine tuning base models, ideally via RFT
• Willingness to dive into tax technical problems - if you aren’t willing to dive deep on how the model should reason through the tax research process you won’t be effective
• A strong understanding of how LLMs and reasoning models function

Nice to haves:

• Experience working with LLMs on legal applications
• Experience with RAG data pipelines and collecting/curating data for the pipeline

Who you are:

• You'll thrive here if: you're a dog, early stage is in your bones, you own it end to end, you believe speed and accuracy are both possible, and being in the room is a feature, not a cost.
• This won't be a fit if: you need structure handed to you or ambiguity feels draining rather than motivating, you want to manage people more than own hard problems, you're used to 'good enough' shipping, or being in the room five days a week feels like a cost instead of a benefit.

Compensation Range: $250K - $300K

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