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About Bespoke Labs

Bespoke Labs is an applied AI research lab pioneering data and RL environment curation for training and evaluating agents.

Recently, we curated Open Thoughts, one of the best open reasoning datasets used by multiple frontier labs, trained SOTA specialized models such as Bespoke-MiniChart-7B and Bespoke-MiniCheck, and taught agents to do multi-turn tool-calling with reinforcement learning.

Bespoke is uniquely positioned to capture a large market share of data and RL environment curation.

About the Role

This is a delivery-heavy role focused on standing up our enterprise post-training capability. Demand is inbound, and our goal is to ship custom, high-performing models to 2–3 paying enterprise customers by year-end. Over the first 6–12 months, you will do the execution work required to solve real-world enterprise problems while helping build the scalable product underneath.

You will not be running abstract experiments or training models solely for benchmarks. You will sit directly at the intersection of enterprise demand and applied post-training—curating data, building rigorous eval suites, fine-tuning models, and proving their value to enterprise stakeholders. We will measure you on the production impact, robustness, and delivery of models shipped to real users, not on published papers.

The thing we care about most is whether you have done this before. If you have post-trained an LLM, shipped it to production users, managed regression risks, and owned the evals from end-to-end, we want to talk.

What You'll Do

• Ship enterprise-grade models. Post-train, fine-tune, and align open-weight and proprietary base models for complex business domains, ensuring they perform reliably in production.

• Build bespoke evaluation suites. Define what "quality" means for subjective domain-specific tasks, create custom benchmarks, and calibrate LLM judges against human domain experts.

• Curate and filter high-impact datasets. Build and run production data flywheels combining real production traces, human labeling, and synthetic augmentation with strict filtering standards.

• Manage and mitigate regression risk. Rigorously track downstream performance to ensure fine-tuning for new behaviors doesn't silently degrade core capabilities or reasoning.

• Engage directly with stakeholders. Sit in front of product and enterprise customers to understand their requirements, translate vague domain preferences into technical eval metrics, and explain model behavior clearly.

• Deploy for cost and privacy. Fine-tune open-weight architectures (e.g., Llama, Qwen, Mistral, DeepSeek) to hit strict enterprise latency, cost, and privacy targets.