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.

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.

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Expertise Area 1: Agentic Systems & Continual Learning

Focus:

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

You will:

• 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)

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Expertise Area 2: Inference time capabilities

Focus:

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

You will:

• 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)

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What you’ll need to succeed:

• An advanced degree in computer science, machine learning, or a related field, or 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

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Bonus points for:

• 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

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Scientific rigor & persistence:

• 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