---
title: 'Research Scientist, Photonic Materials Discovery at Lila Sciences'
canonical: 'https://feeny.ai/job/research-scientist-photonic-materials-discovery-lila-sciences-cambridge-m87b6zgatgmg'
type: 'job'
last_seen: '2026-09-04'
---

# Research Scientist, Photonic Materials Discovery at Lila Sciences

- **Company:** Lila Sciences
- **Location:** Cambridge, MA
- **Compensation:** $176k–$304k
- **Posted:** 2026-08-22
- **Last confirmed live:** 2026-09-04
- **Apply:** https://job-boards.greenhouse.io/lilasciences/jobs/4377093009

## Job description

## Your Impact at LILA

We are seeking a computational materials scientist to discover and optimize materials for electro-optic and photonic technologies. The role centers on understanding how composition, structure, defects, processing conditions, and operating environments influence optical and electro-optic behavior—and translating those insights into experimentally testable materials hypotheses.

You will develop first-principles and multiscale simulation workflows spanning electronic-structure calculations, lattice dynamics, atomistic modeling, and connections to electromagnetic or device-level models. These workflows will predict properties such as electronic structure, dielectric and optical response, polarization, phonons, and electro-optic coefficients. You will also integrate these capabilities into automated, agentic discovery systems that can plan studies, select and invoke tools, evaluate results, recover from failures, and iteratively refine computational hypotheses.

This is a hands-on scientific role at the intersection of condensed-matter physics, materials chemistry, photonics, and AI-enabled discovery. You will collaborate with experimental scientists, ML researchers, and software engineers to build validated workflows, establish structure–property–performance relationships, and prioritize candidates for experimental evaluation.

## What You'll Be Building

- Lead computational discovery efforts for materials relevant to electro-optic and integrated photonic applications.
- Develop and validate first-principles, atomistic, and multiscale workflows—including DFT and response-property calculations—to predict electronic, vibrational, dielectric, optical, and electro-optic behavior.
- Interpret material response across composition, structure, defects, interfaces, strain, and temperature; assess stability, synthesizability, and performance tradeoffs to prioritize candidates.
- Connect intrinsic material properties to device requirements such as optical loss, modulation efficiency, operating wavelength, and fabrication compatibility.
- Compare predictions with experimental measurements, investigate discrepancies, and build effective computational–experimental feedback loops.
- Build reproducible, automated workflows for high-throughput simulation, data provenance, validation, convergence testing, and uncertainty assessment.
- Develop agentic frameworks that orchestrate simulation codes, scientific databases, analysis tools, and surrogate models; partner with ML and software teams on planning, validation, failure recovery, and human review.
- Analyze simulation and experimental data to generate actionable materials hypotheses and communicate recommendations, assumptions, and limitations.

## What You'll Need to Succeed

- PhD or equivalent experience in Physics, Materials Science, Chemistry, Electrical Engineering, or a related field.
- Strong background in computational condensed-matter physics, materials science, physical chemistry, or a related discipline, with experience studying functional optical, dielectric, or electronic materials.
- Expertise in electronic-structure methods and calculating and interpreting dielectric, optical, vibrational, polarization, or related response properties using perturbative, finite-field, Berry-phase, or comparable methods.
- Working knowledge of crystallographic symmetry, electronic structure, lattice dynamics, light–matter interaction, and structure–property relationships.
- Experience with established electronic-structure packages and reproducible HPC or cloud workflows, including scheduling, data management, and automated analysis; strong Python and scientific software skills.
- Familiarity with agentic AI, tool-calling, or workflow orchestration and the design of reliable, auditable workflows across scientific tools.

Bonus Points For

- Experience with materials or device concepts relevant to electro-optics and integrated photonics, including ferroelectrics, semiconductors, oxides, nitrides, chalcogenides, or low-dimensional materials.
- Familiarity with advanced electronic-structure, excited-state, finite-temperature, or multiscale methods—such as hybrid-functional, many-body, molecular-dynamics, or effective-Hamiltonian approaches—when standard DFT is insufficient.
- Experience modeling defects, surfaces, interfaces, thin films, strain, or other non-ideal effects, and connecting atomistic predictions to electromagnetic, device, or process models.
- Experience building high-throughput workflows, materials data systems, surrogate models, or active-learning loops, including applications of AI/ML to computational materials science or physics-based simulation.
- Hands-on experience with agentic or tool-using systems and orchestration patterns for long-running scientific tasks, including branching, retries, checkpointing, and asynchronous execution.
- Experience designing evaluation, observability, error recovery, provenance, and human oversight for agent-driven workflows.
- Ability to communicate physical insight, uncertainty, and model limitations to cross-functional collaborators.

## 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.

## About Lila Sciences

## Company Overview
- **One-liner**: Lila Sciences is building the world’s first Scientific Superintelligence platform and autonomous lab, using AI to autonomously generate hypotheses, design and run experiments, and learn from results in real time across life sciences, chemistry, materials, energy, and defense.
- **Entity Type**: Private (Privately Held) – Series A, Seed, and Grant funding rounds
- **Headquarters**: Cambridge, Massachusetts, United States
- **Founded**: Not publicly available (first funding round was Seed in March 2025)
- **Founders**: Not publicly available (founded within Flagship Pioneering’s ecosystem)

## Core Business
- **Primary industry**: AI-powered scientific discovery and autonomous laboratory platforms
- **Target customers**: B2B – research organizations, biotech, pharmaceutical, energy, materials, aerospace, and defense companies
- **Mission**: “Scientific Superintelligence to solve humankind’s greatest challenges” – accelerating discovery across medicine, materials, energy, and defense

## Products & Services
- **LILA Platform (Scientific Superintelligence)**: An AI model and operating system that autonomously executes the entire scientific method – generating hypotheses, designing experiments, running them in physical labs, and learning from results in real time.
- **AI Science Factory™ Instruments**: Proprietary hardware and robotics that serve as the “body” of the platform, enabling automated experimentation at scale.
- **Domain-specific applications**: Tailored solutions for biotech (drug discovery, protein engineering), therapeutics (mRNA, antibodies, cell therapies), energy & environment (clean fuels, catalysis, critical minerals), advanced materials, chemicals, aerospace & defense, and oil & gas.

## Market Standing
- **Valuation**: Not disclosed
- **Key Metric**: Total funding – USD $550.7M (as of LinkedIn data)
  - Seed Round (March 2025): $200M led by Flagship Pioneering
  - Series A (September 2025): $235M led by Braidwell and Collective Global Management
  - Series A (November 2025): $115M led by NVentures (NVIDIA)
  - Grant (January 2026): $671,400 led by ARIA
- **Notable Investors/Partners**: Flagship Pioneering, NVIDIA (NVentures), Braidwell, Collective Global Management, ARIA
- **Growth Signals**: Named #25 on the 2026 CNBC Disruptor 50 List; headcount of 308 employees (monthly growth +10.6%); 122 active job postings; operates in 6 countries (US, UK, Canada, Poland, Spain, Germany)

## Competitive Advantages
- **Proprietary AI model** that consistently outperforms other models across scientific domains in complex analysis and reasoning.
- **Autonomous physical labs** that close the loop between AI hypothesis generation and real-world experimentation.
- **“Team of Teams” operating model** enabling startup speed at scale while maintaining radical transparency and high trust.
- **General platform approach** (inspired by Rich Sutton’s “Bitter Lesson”) rather than narrow domain-specific tools, allowing broad applicability.

## Strategic Focus
- Accelerating discovery across medicine, materials, energy, and defense
- Scaling the autonomous science platform to more industries and use cases
- Building “Scientific Superintelligence” that can tackle humanity’s greatest challenges
- Continued investment in AI research, robotics, and lab automation

## Why Work Here
- **Culture**: Emphasizes velocity, trust, curiosity, truth, and grit. “Think freely, prove precisely.” A high-trust, mission-driven environment where scientists and engineers work side by side.
- **Remote/Hybrid/Office**: Roles are listed in Cambridge, MA; San Francisco, CA; and London, UK. Physical lab presence suggests significant on-site work, but some roles may offer flexibility. Policy not explicitly stated.
- **Notable perks/engineering culture**: Opportunity to work at the frontier of AI and scientific discovery; collaboration with world-renowned experts; access to cutting-edge robotics and AI infrastructure; strong emphasis on learning and teaching (“generous teachers and eager learners”).

## Sources
1. [lila.ai](https://www.lila.ai/)
2. [lila.ai/about](https://www.lila.ai/about)
3. [lila.ai/open-roles](https://www.lila.ai/open-roles)
4. [LinkedIn - Lila Sciences](https://www.linkedin.com/company/lila-sciences)
5. [Greenhouse Job Board](https://job-boards.greenhouse.io/lilasciences/jobs/4246302009)

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- [Senior Data Engineer, Bioinformatics, Cheminformatics, Materials](https://feeny.ai/job/senior-data-engineer-bioinformatics-cheminformatics-materials-lila-sciences-san-kp5d4nbn38vz) — San Francisco, CA
- [Associate Director, App](https://feeny.ai/job/associate-director-app-lila-sciences-cambridge-46qgd5hp866e) — Cambridge, MA / San Francisco, CA
- [Maintenance Engineering Technician II](https://feeny.ai/job/maintenance-engineering-technician-ii-lila-sciences-cambridge-qgp4st6zk9gw) — Cambridge, MA
- [Research Scientist I/II, Computational Organic Electronics](https://feeny.ai/job/research-scientist-i-ii-computational-organic-electronics-lila-sciences-mh2tq43ahp5d) — Cambridge, MA
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