Help us train financial superintelligence.

Responsibilities

• Define and prioritize the training-gym roadmap based on real-world finance experience.
• Build end-to-end systems and processes from scratch to support $MMs in revenue this year.
• Own delivery and quality criteria for data and environments shipped to customers.
• Recruit and train our contractor finance staff.
• Create training materials for AI systems (modeling exercises, pitch decks, etc.).
• Partner with frontier engineering and research teams to implement and deliver gyms.

You should have

• Experience teaching and giving feedback on modeling, decks, or client deliverables.
• A track record of owning end-to-end deliverables for live deals.
• Experience leading large teams and owning deadlines for big projects.
• An ability to learn deeply technical concepts in ML, AI, data, and engineering.
• Comfort using coding tools (Cursor, Claude Code) to build platforms and speed up processes.

Nice to have

• Engineering background (e.g. CS).
• Experience with early-stage startups.

Responsibilities

• Build our RL environment training and inference infrastructure to support customer usage at scale.
• Research and develop next-generation RL environments — increasingly realistic, long-horizon, and difficult for frontier models.
• Build software that 10–100x's the quality and throughput of RL environment creation.
• Innovate on synthetic data pipelines to create realistic problems.
• Build platform analytics for environment cost, hours, bottlenecks, and SME management.
• Craft domain-specific verifiers (verifiable rewards for decks, Excel modeling, quantitative trading, and beyond).
• Establish engineering culture and practices from the ground up.

You should have

• Familiarity building evaluations, benchmarks, or RL for AI agents.
• Startup speed: iterate quick, ask quick, respond quick, make mistakes quick.
• Strong product and user ownership; ability to prioritize across a large roadmap.
• Client-facing comfort — you'll talk to users, customers, and SMEs.

Nice to have

• Research background (training models, publishing papers).
• Former founders or experience at early-stage startups.

Responsibilities

• Train open-source frontier models on our in-house environments to validate quality and build research insights.
• Lead our public-facing benchmark and leaderboard efforts for frontier models.
• Publish findings in blogs and papers to share with the broader research community.
• Contribute to core platform engineering as needed — AI tooling, data collection pipelines, partnering with environment and reward designers.
• Build our engineering team culture from the ground up.

You should have

• Experience in model post-training.
• Experience publishing papers and sharing open research.
• Familiarity building evaluations, benchmarks, or RL for AI agents.
• Startup speed: iterate quick, ask quick, respond quick.
• Strong product and user ownership.
• Client-facing comfort — you'll talk to users, customers, and SMEs.

Nice to have

• Former founders or experience at early-stage startups.