Research Scientist – RL Post-Training for Agents

Rnb Consultancy; San Francisco (CA); On-site; USD 250,000 - 500,000; Full time

Benefits offered by this job

Visa sponsorship

Job summary

Rnb Consultancy in San Francisco is hiring a brand-new AI lab focused on autonomous agents that pursue long-horizon goals. You will own ambitious bets from the first hypothesis and dataset through deployment, shaping the research agenda from day one.

The team values exceptional ML research and engineering talent with a track record of leading significant projects, and offers a chance to ship real-world results in a frontier research environment.

Qualifications

• Exceptional ML research and engineering skills.
• Depth in RL, LLM post-training, reasoning, agents, or long-horizon systems.
• Ability to build complete systems from hypothesis to production.
• Led a significant model, agent system, benchmark, paper, or open-source project.

Responsibilities

• Use RL and other methods to post-train LLM-based and multimodal agents.
• Develop long-horizon capabilities: planning, memory, error recovery, proactivity, self-improvement.
• Build environments where agents use computers and tools, plus data pipelines, benchmarks and evals.
• Enable agents to understand user intent and stay true to it over long runs.
• Design careful experiments and transfer results to production.

Skills

Reinforcement Learning

LLM post-training

Agents

Long-horizon systems

Memory & context

Eval harnesses

Production systems

Tools

None

Job description

Want your RL research to land in agents that run for days in the real world, not in a paper appendix?

A brand-new AI lab in San Francisco is building autonomous agents that pursue complex goals over very long horizons. The founding team comes from leading frontier AI labs, autonomous-driving and robotics AI, big-tech research and a top quant firm. This is a research seat that builds real systems: you own ambitious bets from the first hypothesis and dataset all the way to a deployed capability.

They’re hiring 4 research scientists.

What you’ll own

• Using RL, and whatever else works, to post-train LLM-based and multimodal agents
• Long-horizon capabilities: planning, memory, error recovery, proactivity, self-improvement, and knowing when to escape to a human
• Building the environments where agents use computers and tools, plus the data pipelines, benchmarks and evals around them
• Ways for agents to grasp what a user wants and stay true to it over long runs
• Careful experiment design, and taking what works all the way into production
• A real say in the research agenda from day one

What you bring

• Exceptional ML research and engineering skills
• Depth in at least one of these: RL, LLM post-training, reasoning, agents, computer use, long-horizon systems, memory and context, evals, or how humans and agents work together
• Good judgment on what to test and when to stop. You build complete systems and move easily between ideas, large-scale experiments and production.
• You’ve led something significant: a model, an agent system, a benchmark, a paper, an open-source project or a big research bet
• Roughly 3–6 years in; frontier-lab experience valued, exceptional outliers and senior leads welcome
• High agency and comfort with uncertain directions

Bonus points

• Hands-on RL post-training or computer-use work
• A strong publication, open-source or benchmark record
• Experience building environments and eval harnesses
• A spike: olympiad (IOI/IMO), quant, or world-class competitive achievement

What’s in it for you

• $250k–$500k base + 1–5% equity
• Your own research bets, end to end, at a lab where results ship
• Visa sponsorship available; if you’re outside the US, expect to go via an O-1

Good to know

• Full-time, in person in San Francisco, 9-9-6
• Process: informal talk with a founder → technical deep-dive on your research → paid 2–3 day work trial in person → offer