Research Scientist, AI

Pramaana Labs • Palo Alto, CA

13h

About The Position

Pramaana Labs is a frontier AI lab building a verification layer for AI. It turns complex human knowledge—such as tax codes, legal rules, clinical guidelines, and security protocols—into formal representations that support inspectable, checkable reasoning. Its architecture pairs models trained to formalize and reason with a symbolic world model encoded in Lean 4. Founded in 2025 and headquartered in Palo Alto, Pramaana brings together researchers and formal methods experts from Google, DeepMind, and Glean.

The Research Scientist, AI will develop approaches to improve the accuracy, coverage, efficiency, and generalization of Pramaana’s domain formalizer, autoformalizer, and prover. The work spans model training, data quality, representations, and evaluation. The scientist will collaborate with an RL Scientist on reinforcement-learning approaches, Research Engineers on reliable experimentation, and Formal Methods experts on whether model outputs faithfully represent source knowledge.

The role leads research at the intersection of foundation models, formal reasoning, and verification, while remaining hands-on in technical work. It includes developing model training and post-training approaches, conducting research in reinforcement learning, synthetic data generation, reasoning, and agentic AI, leading end-to-end research initiatives, establishing benchmarks and evaluation methods, and partnering with engineering to translate research advances into production capabilities.

Requirements

• A demonstrated track record of high-impact research, including projects successfully deployed and materially advancing a product, core technology, or an organization's capabilities and direction.
• Deep hands-on experience in foundation model training and post-training, including one or more of large-scale pretraining, reinforcement learning, reasoning-focused training, or synthetic data generation.
• Strong technical depth in modern AI research, including frontier language models, agentic systems, multi-step reasoning, or structured prediction.
• Proficiency in Python and modern ML frameworks such as PyTorch or JAX, with experience implementing and scaling training experiments.
• Exceptional research judgment and experimental rigor, including the ability to identify promising directions, formulate hypotheses, design meaningful evaluations, and distinguish genuine advances from incremental gains.
• Demonstrated technical leadership and independence, including experience driving ambitious research initiatives, mentoring scientists, and collaborating across research and engineering while remaining a strong individual contributor.

Nice To Haves

• Direct experience training or post-training frontier-scale models, including distributed training, large-scale reinforcement learning, or inference-time scaling.
• Research experience in neuro-symbolic AI, autoformalization, automated theorem proving, program synthesis, or formal verification.
• A strong research record demonstrated through influential publications, deployed research breakthroughs, or substantial improvements to state-of-the-art model capabilities.

Responsibilities

• Lead research at the intersection of foundation models, formal reasoning, and verification.
• Develop novel model training and post-training approaches for translating complex domain knowledge into structured representations and enabling precise, verifiable reasoning.
• Conduct hands-on research in reinforcement learning, synthetic data generation, reasoning, and agentic AI to advance model capabilities, generalization, and efficiency.
• Own and lead end-to-end research initiatives, identify high-impact directions, set technical strategy, and guide teams of researchers and engineers while remaining deeply involved in technical work.
• Develop rigorous benchmarks and evaluation methodologies to measure reasoning capabilities, robustness, and generalization.
• Use experimental insights to drive fundamental model improvements.
• Partner with engineering to translate research breakthroughs into production capabilities and measurable improvements in performance and reliability.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed