Machine Learning Scientist I / II, Protein Design
Company websitelila.ai (Lila Sciences, opens in new tab)
Base salary $176K to $304K
Employer-published annual base salary, USD. Advertised base range in United States
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationUnited States
- WorkplaceWorkplace not stated
- Status at last checkSource accepting applications when checked
- Last checked (3 h ago) · observed since
About this job
Your Impact at LILA
Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI, the AI for Protein Engineering team builds models and systems that take a biologic from design specification to wet-lab validated lead.
We're hiring a machine learning scientist to design molecules on real programs and turn what they learn into capabilities that generalize across programs. The ideal candidate is an exceptional builder with strong biological intuition: someone who can turn work on individual campaigns into reliable, extensible systems that improve how we design molecules across programs. You'll work closely with domain scientists, platform teams, and AI researchers to connect specialist protein design models to Lila's broader autonomous science platform.
What You'll Be Building
- Design molecules for active biologics programs, partnering with domain scientists to translate target, mechanism, and experimental constraints into actionable design hypotheses.
- Develop reasoning capabilities for drug discovery, including orchestrating design workflows.
- Design and maintain benchmarks and evaluation infrastructure that measure whether design workflows produce useful, generalizable decisions across biologics programs.
- Own operations around reproducibility, throughput, and inference cost of computational design workflows.
- Work with domain scientists to understand how designs are prioritized and turn that judgment into ML objectives and evaluation criteria.
What You'll Need to Succeed
- MS or PhD in computer science, machine learning, computational biology, biophysics, bioengineering, or a similar quantitative field.
- Strong software engineering and system design fundamentals.
- Rigor in evaluation and dataset design: how benchmarks leak, why a good validation number fails downstream, and how to measure whether an automated system is making good decisions.
- Strong cross-functional communication skills.
- Domain expertise in protein sequence, structure, and function.
Bonus Points For
- Experience building reasoning models, agents, planning systems, or multi-step ML orchestration.
- Exposure to designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins within design-test-learn loops.
- Experience in developing evaluation harnesses, model registries, or benchmark suites.
- Training or serving models at scale: distributed training, GPU efficiency, high-throughput inference.
- Publications, open-source contributions, or applied research outputs in AI for Science venues.
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.
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.
Source: Lila Sciences on Greenhouse · View original posting (opens in new tab)
Pay in context
This role's base floor ($176,000) is above 41% of the 1,730 Data & ML roles in California listed on this site; 4 others list the same floor. Their median floor is $184,000; the middle half runs from $154,150 to $224,000.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 7, 2026.
This listingMachine Learning Scientist I / II, Protein Design$176K to $304K
Research Scientist, Computational Condensed Matter Physics$176K to $304K
ML Scientist I/II, AI for Protein Engineering$176K to $304K
Principal Network Engineer$171,000 to $230,534
Senior II/ Staff Software Engineer, Platform Operations$180K to $288K
Senior Product Designer II / Staff Product Designer$180K to $256K
Senior / Staff Machine Learning Engineer, Applied AI$180K to $298K
Chart as a table
| Role | Published base |
|---|---|
| Machine Learning Scientist I / II, Protein Design (this listing) | $176K - $304K |
| Research Scientist, Computational Condensed Matter Physics | $176K - $304K |
| ML Scientist I/II, AI for Protein Engineering | $176K - $304K |
| Principal Network Engineer | $171,000 - $230,534 |
| Senior II/ Staff Software Engineer, Platform Operations | $180K - $288K |
| Senior Product Designer II / Staff Product Designer | $180K - $256K |
| Senior / Staff Machine Learning Engineer, Applied AI | $180K - $298K |
Observed changes
- Began meeting the published-salary criteria
- First observed on the employer's board
- Observed since
- Employer-stated posting date
- 2026-09-15
Where this pay comes from
Location as the employer wrote it: San Francisco, CA USA
USD 176,000 – 304,000 · interval unstated · pay_input_range · Expected Base Salary Range · base-labelled (R1: Base Salary)
- Method
- Structured pay field published by the employer
- Checked
- Rule set
- us-base-v2
The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
