Job Description

Founding Research Scientist (Long-Horizon RL) at Lanturn

Location: San Francisco (preferred) / Remote (US)

Compensation: $300K base + 0.5–1% equity

Type: Full-time · Founding Team

At Lanturn, we are building the next generation of reinforcement learning systems for real-world agents. Our focus is on enabling AI systems to learn from behavioral data and long-horizon workflows, through:

• High-fidelity RL environments
• Synthetic data generation
• Closed-loop training systems

We are looking for a Founding RL Researcher to push the frontier of:

• Long-horizon RL
• Environment design
• Post-training for agents

About us:

Lanturn is building the end-to-end behavioural learning stack for AI systems. We believe current approaches to RL and post-training are limited by short-horizon optimisation, weak or proxy reward signals, and a lack of grounded environments. Our approach is to build closed-loop RL systems where environments, data, training, and evaluation are tightly integrated and based on real-world behavioral data.

The role:

As a Founding RL Researcher, you will lead efforts to develop novel reinforcement learning algorithms and environments for training autonomous agents. You will work across:

• Algorithm design
• Environment modelling
• Training systems
• Evaluation frameworks

This role sits at the intersection of:

• Frontier Labs-style RL research (environments + algorithms)
• Modern LLM post-training (RLHF, preference optimisation)

Key responsibilities:

• Design and implement RL systems for long-horizon tasks (10–100+ steps)
• Develop and extend modern post-training methods:
• PPO, DPO, ORPO
• GRPO / GRPO++ and ranking-based optimization methods
• Build RL environments grounded in real-world workflows
• Work on meta-RL and adaptive learning systems:
• Generalization across tasks
• Rapid adaptation to new environments
• Design reward systems for:
• Behavioural correctness
• Efficiency and robustness
• Develop evaluation frameworks aligned with real-world outcomes
• Collaborate with engineering teams to scale training systems

Ideal candidate:

You are a researcher with strong theoretical grounding and real-world system intuition, capable of working on open-ended problems in RL. You thrive in environments where:

• Problems are not well-defined
• Systems must be built from first principles
• Research directly translates into deployed systems

Minimum qualifications:

• Experience at a top-tier AI lab or company: OpenAI, DeepMind, Anthropic, FAIR, or equivalent
• Strong background in reinforcement learning and post-training systems
• Experience training large-scale models (LLMs or similar)
• Strong programming skills (Python, PyTorch/JAX)

Preferred qualifications:

• Experience with long-horizon RL or sequential decision-making systems
• Experience designing or working with RL environments
• Familiarity with: Preference optimization (DPO, ORPO), RLHF pipelines, and automated RL env generation
• Experience with meta-RL / adaptive learning systems
• Strong publication record in top-tier ML conferences

Core technical skills:

• Deep understanding of: Policy gradient methods (PPO and beyond), KL-regularized optimization, and credit assignment in long-horizon settings
• Experience with: Cascading RL pipelines (SFT → RL → evaluation), distributed training systems, and stability and scaling challenges
• Strong intuition for: Exploration vs exploitation, reward shaping vs reward learning, and trajectory-level optimization

What makes this role unique ?

• Focus on long-horizon behavioral learning, not short-form RLHF
• Treats environment design and generation as a first-class problem
• Opportunity to define GRPO++-style next-generation algorithms and publish to NeurIPS

Why join Lanturn ?

• Founding ownership (0.5–1% equity)
• Work on unsolved problems in RL and agent systems
• High autonomy and research freedom
• Direct impact on how real-world AI systems are trained
• Work with second time founders directly who have worked with various big tech companies and enterprises.

If you’ve worked on RL at a top lab or have had production RL experience and want to push beyond current paradigms into real-world, long-horizon intelligence, this is your opportunity.

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