Job description

Your Impact at LILA Lila Sciences is seeking an ML Scientist I/II, Nucleic Acid Design to advance RNA and DNA sequence design. This scientist will develop models and design strategies for understanding and engineering nucleic acid sequences, including problems such as 3’ UTR optimization, 5’ UTR optimization, CDS optimization, and promoter / enhancer design. You’ll work at the intersection of machine learning, sequence modeling, experimental design, and platform development. The work spans both applied design campaigns and building next-generation models that improve how Lila generates, evaluates, and learns from nucleic acid sequence-function data. The ideal candidate brings strong ML judgment, curiosity about biological mechanisms, and enthusiasm for areas such as regulatory genomics, RNA biology, and sequence-to-function modeling. You’ll collaborate with experimental scientists, ML researchers, and platform teams to build models that connect nucleic acid sequence design to biological function and make these capabilities usable across Lila’s autonomous science platform. What You'll Be Building Build ML models for RNA and DNA sequence design across regulatory and coding sequence contexts. Develop methods spanning de novo generation, sequence property prediction, diverse set selection for experimental validation, and active learning strategies. Deeply investigate the biological mechanisms of designed sequences and propose hypotheses about why they succeed or fail. Turn these insights into better models and future design principles. Partner with experimental scientists to propose informative assays, validation strategies, and learning loops. Collaborate with ML scientists and engineers across Lila to integrate nucleic acid design models into robust platforms and agent-driven frameworks. Stay current with research in nucleic acid biology, sequence design, and scientific ML, and share research findings externally through papers or blog posts. What You'll Need to Succeed PhD or equivalent experience in machine learning, computational biology, bioengineering, computer science, statistics, or a related quantitative field. Hands-on experience building, training, and evaluating ML models for DNA or RNA. Strong foundation in modern ML methods, with practical experience using frameworks such as PyTorch, JAX, or equivalent tools. Experience developing models for sequence design, sequence-function prediction, generative modeling, or active learning. Ability to reason about complex biological systems, scope ambiguous scientific problems, and formulate ML approaches that address difficult sequence-function challenges. Curiosity about nucleic acid biology, including RNA biology, regulatory genomics, or related sequence-to-function problems. Strong communication and collaboration skills, with a preference for team-based science and the ability to build shared technical direction across ML, engineering, platform, and experimental teams. Bonus Points For Experience with regulatory element design, sequence-to-expression DNA models, or models trained on genomic, MPRA, STARR-seq, or related functional genomics data. Experience with RNA sequence-function modeling, RNA secondary structure modeling, UTR design, or inverse design methods for RNA sequences. Experience with sequence design in applied therapeutic contexts. Experience collaborating with wet-lab teams to close the design-test-learn loop, including assay design, experimental prioritization, and interpretation of validation data. Familiarity with high-throughput experimental datasets, pooled screens, reporter assays, or other sequence-function measurements. Industry experience translating ML research into practical biological design workflows, experimental campaigns, or platform capabilities. 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.

Requirements

Must-haves

Your Impact at LILA
What You'll Be Building
Build ML models for RNA and DNA sequence design across regulatory and coding sequence contexts.
Develop methods spanning de novo generation, sequence property prediction, diverse set selection for experimental validation, and active learning strategies.
Deeply investigate the biological mechanisms of designed sequences and propose hypotheses about why they succeed or fail. Turn these insights into better models and future design principles.
Partner with experimental scientists to propose informative assays, validation strategies, and learning loops.
Collaborate with ML scientists and engineers across Lila to integrate nucleic acid design models into robust platforms and agent-driven frameworks.
Stay current with research in nucleic acid biology, sequence design, and scientific ML, and share research findings externally through papers or blog posts.
What You'll Need to Succeed
PhD or equivalent experience in machine learning, computational biology, bioengineering, computer science, statistics, or a related quantitative field.
Hands-on experience building, training, and evaluating ML models for DNA or RNA.
Strong foundation in modern ML methods, with practical experience using frameworks such as PyTorch, JAX, or equivalent tools.
Experience developing models for sequence design, sequence-function prediction, generative modeling, or active learning.
Ability to reason about complex biological systems, scope ambiguous scientific problems, and formulate ML approaches that address difficult sequence-function challenges.
Curiosity about nucleic acid biology, including RNA biology, regulatory genomics, or related sequence-to-function problems.
Strong communication and collaboration skills, with a preference for team-based science and the ability to build shared technical direction across ML, engineering, platform, and experimental teams.
Experience with regulatory element design, sequence-to-expression DNA models, or models trained on genomic, MPRA, STARR-seq, or related functional genomics data.
Experience with RNA sequence-function modeling, RNA secondary structure modeling, UTR design, or inverse design methods for RNA sequences.
Experience with sequence design in applied therapeutic contexts.
Experience collaborating with wet-lab teams to close the design-test-learn loop, including assay design, experimental prioritization, and interpretation of validation data.
Familiarity with high-throughput experimental datasets, pooled screens, reporter assays, or other sequence-function measurements.
Industry experience translating ML research into practical biological design workflows, experimental campaigns, or platform capabilities.

Nice-to-haves

Bonus Points For

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Keywords to mirror

mlmachine learningai

Platform: greenhouse
Effort: quick

Benefits

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Dental

Vision

Parental Leave