Research Scientist, Frontier Capabilities

Research Scientist — Frontier capabilities Your impact at Lila:

We’re building a talent-dense, high-agency research team to develop the next generation of learning systems and reasoning algorithms for agentic LLMs. Our work sits at the intersection of large language models, post-training, and scientific reasoning, with the goal of enabling systems that learn from experience, reason effectively, and improve through interaction
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This role spans two complementary directions. Candidates are expected to bring deep expertise in one of the following areas:

• Agentic Systems & Continual Learning
• Inference time capabilities

Both tracks contribute to a shared goal: translating advances in reasoning, interaction, and structure into scalable training paradigms and real-world scientific capabilities.

Develop systems that learn continuously through interaction
, leveraging memory, feedback, and structured workflows to improve over time.

• Set research directions for continual and active learning in LLM-based systems
• Design mechanisms for learning from interaction (e.g., feedback loops, self-improvement, and adaptive data generation)
• Train or “in-context-learn” agentic systems at scale that exhibit robustness to distribution shift.
• Investigate temporal abstraction, planning, and self-critique in agentic systems
• Design and evaluate memory-augmented, hierarchical, or multi-agent workflows (e.g., supervisor + subagents)

Develop inference-time methods for reasoning and structured problem solving, and translate them into scalable learning algorithms.

• Set research directions on inference-time algorithms for reasoning, search, and structured problem solving
• Design and run evaluations across domains (math, coding, science etc)
• Implement and compare prompting strategies, search methods, and meta-learning approaches
• Translate inference-time improvements into training (e.g., synthetic data generation, distillation strategies)

• An advanced degree in computer science, machine learning, or a related field, or comparable experience
• Strong foundation in LLMs and empirical research
• Experience designing and executing rigorous ML experiments, including benchmarking and ablations
• Experience working with large-scale training or evaluation pipelines
• Ability to define and pursue research directions in open-ended, rapidly evolving spaces
• Strong collaboration and communication skills across research and engineering teams

• Experience with synthetic data generation, distillation, or self-improvement loops
• Familiarity with reinforcement learning (e.g., RLHF, on-policy methods)
• Experience with planning, search, or decision-making systems at scale
• Experience in building agentic systems with tool use, or multi-agent workflows
• Background in program synthesis, coding benchmarks, or long-horizon tasks
• Experience building evaluation frameworks or large-scale benchmarks

• You take a principled approach to experimentation, with careful baselines, ablations, and evaluation design
• You are motivated by understanding why systems work, not just improving metrics
• You prioritize clarity, reproducibility, and intellectual honesty in research
• You are comfortable working through long, nonlinear iteration cycles
• You operate effectively in ambiguous, fast-evolving research environments

Compensation

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD…