Scientist II, Silicon Photonics

Lilasciences; Cambridge; On-site; GBP 95,000 - 127,000; Full time

Job summary

Lila Sciences seeks a Scientist II, Silicon Photonics to join the Materials Science team. This role builds the measurement, analysis, and data-quality foundation for electro‑optic materials and photonic devices, turning material and device performance into trusted data for scientists.

You will own characterization from thin‑film properties to modulators at chip and wafer level, designing test setups and calibration procedures to ensure comparable results across variants and iterations.

Qualifications

• PhD in Electrical Engineering, Applied Physics, Materials Science, or related field.
• Hands-on experience characterizing photonic materials and devices at chip/wafer level.
• Experience with thin-film and electro-optic property measurements.

Responsibilities

• Own characterization across thin-film, passive photonics, and electro-optic modulators.
• Design, assemble, calibrate, and debug optical and optoelectronic test setups.
• Analyze data and feed results back to material, design, and fabrication decisions.

Skills

Characterization

Thin-film measurements

Electro-optic modulators

Optoelectronic measurement setups

Python

MATLAB

Data analysis

Test design

Education

PhD in Electrical Engineering/Applied Physics/Materials Science

Tools

Lumerical

COMSOL

Job description

Your Impact at LILA

Lila Sciences is seeking a Scientist II, Silicon Photonics to join the Materials Science team within the Autonomous Science Platform. This role builds the measurement, analysis, and data‑quality foundation for novel electro‑optic materials and the photonic devices built from them, turning material and device performance into trusted data for scientists and for the models that learn from it.

You will own characterization across the full stack, from thin‑film material properties through passive photonic structures to electro‑optic modulators, at both chip and wafer level. That means defining what to measure, designing and standing up the test setups, and establishing the calibration and quality‑control procedures that make results comparable across material variants, process conditions, and design iterations. This is hands‑on work: you will assemble and debug optical and optoelectronic measurement setups, not just operate them.

The role sits between materials, device design, fabrication, and test. You will interpret measured data in the context of material properties, process variation, and device physics, feed findings back into material and design decisions, and partner with our simulation and ML teams. You will also define measurement requirements for the automation team and prototype methods well enough to scale into reliable infrastructure.

The ideal candidate has followed photonic devices from material through fabrication to characterization, and has strong judgment about measurement quality: how to design a measurement that answers the question, and how to know when the data cannot be trusted.

What You’ll Be Building

• Own characterization spanning thin‑film material properties, passive photonic structures, and electro‑optic modulators, at chip and wafer level.
• Define measurement strategies that produce reliable, decision‑grade material and device performance data.
• Design, assemble, calibrate, and debug optical and optoelectronic test setups.
• Create measurement methods for novel thin film materials where no standard protocol exists, with calibration and quality‑control practices that make results comparable across material variants and process conditions.
• Develop wafer‑level measurement approaches that provide statistical insight into material and process variation and accelerate design iteration.
• Analyze data through material properties, device physics, process variation, and measurement uncertainty.
• Feed characterization results back into material, design, and fabrication decisions, and partner with simulation and ML teams.
• Prototype measurement methods for the automation team to scale into reliable infrastructure.
• Document procedures, data standards, and measurement limitations clearly for cross‑functional teams.

What You’ll Need to Succeed

• PhD in Electrical Engineering, Applied Physics, Materials Science, or a related field.
• Hands‑on experience characterizing photonic materials and devices at chip and wafer level, spanning thin‑film and material‑level measurements through passive and active device testing.
• Experience measuring thin‑film optical and electro‑optic properties, including extraction of electro‑optic coefficients.
• Experience testing passive photonic structures such as waveguides, resonators, and couplers, including loss and dispersion characterization.
• Experience with electro‑optic modulators or related active integrated photonic devices.
• Experience building and debugging optical and optoelectronic measurement setups, including alignment, calibration, and noise control.
• Experience developing measurement methods where no standard protocol exists, including defending measurement validity and data quality.
• Experience interpreting data through material properties, device physics, process variation, measurement uncertainty, and repeatability.
• Familiarity with high‑speed RF/electronics, fiber optics, and electro‑optic testing workflows.
• Proficiency in Python, MATLAB, or an equivalent language for automated workflow and data analysis, and collaboration with ML and automation teams.
• Ability to work across design, fabrication, simulation, and automation teams while owning the measurement‑quality standard.

Bonus Points For

• Experience characterizing novel, heterogeneous, or hybrid electro‑optic materials integrated on silicon photonics, such as χ² or Pockels‑effect thin films.
• Experience correlating film‑level material properties to device‑level performance.
• Hands‑on cleanroom fabrication experience.
• Experience with wafer‑level photonic probing, active alignment, foundry workflows.
• Familiarity with waveguide and electro‑optic modulator simulation tools such as Lumerical, COMSOL, or equivalent.
• Experience writing test automation, instrument control software, or measurement data pipelines.
• Prior work supplying measurement data to ML, modeling, or simulation teams.

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

$128,000 $170,000 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 iscommitted to equal employment opportunityregardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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