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.

Scientific domains present a distinct set of challenges that make this problem

uniquely hard. Feedback is sparse and delayed — experiments take days or weeks,

not milliseconds. Ground truth is expensive or contested. Distribution shift is

structural, as instruments, techniques, and knowledge bases evolve continuously.

The hypothesis space is vast and reward signal is thin. Existing benchmark do

not capture these nuances. The goal is to build systems that can operate

effectively in this scientific regime.

This role spans a few complementary directions. Candidates are expected to bring

deep expertise in one (ore more) of the following areas. In the event of

cross-track expertise, please select the one you align to the most. Our

interview process will be catered to verifying the chosen expertise area.

Expertise Area 1 — Agentic system building

Focus: Build systems that autonomously propose, execute, and verify scientific

hypotheses over long time horizons.

* Create and analyze long-running auto-research systems that propose and verify

hypotheses

* Design planning frameworks for agentic systems operating over long, sparse

feedback loops

* Design memory architectures that allow agents to build and retrieve

structured knowledge over time

* Explore algorithms in recursive self-improvement, multi-agent coordination,

and continual learning

Expertise Area 2: Distillation

Focus: Translate strong inference-time behaviors and reasoning traces into

efficient, trainable models.

* Develop distillation strategies from large or ensemble models into deployable

systems

* Research methods for self-improvement, including iterative self-distillation

and critique loops

* Investigate how to preserve generalization and reduce catastrophic forgetting

through the distillation process

Expertise Area 3 — Scalable experience generation

Focus: Develop inference-time algorithms and synthetic data pipelines that

generate high-quality training signal for scientific reasoning.

* Design and benchmark inference-time search, sampling, and verification

strategies

* Propose new techniques in synthetic environment creation and curriculum

learning

* Develop synthetic data generation strategies that capture high-quality

scientific reasoning for agentic model training

* Measure the end-to-end impact of inference-time improvements on real

scientific tasks

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

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

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

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 salary ranges apply

only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range

$176,000—$304,000 USD

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's

greatest challenges. We believe science is the most inspiring frontier for AI.

Rather than hard-coding expert knowledge into tools, LILA builds systems that

can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™

instruments into an operating system for science that executes the entire

scientific method autonomously, accelerating discovery at unprecedented speed,

scale, and impact across medicine, materials, and energy. Learn more at

www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we

move with startup speed while tackling problems of historic importance. If this

sounds like an environment you'd love to work in, even if you don't meet every

qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race,

color, ancestry, religion, sex, national origin, sexual orientation, age,

citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in

accordance with our Candidate Privacy Policy

[https://www.lila.ai/candidate-privacy-policy-notice].

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than

candidates. The submission of unsolicited resumes by recruitment or staffing

agencies to Lila Sciences or its employees is strictly prohibited unless

contacted directly by Lila Science’s internal Talent Acquisition team. Any

resume submitted by an agency in the absence of a signed agreement will

automatically become the property of Lila Sciences, and Lila Sciences will not

owe any referral or other fees with respect thereto.