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Nucleic Acid Phd Scientist Jobs (NOW HIRING)

Pilgrim

Scientist

Redwood City, CA · On-site

As a Scientist at Pilgrim, you will develop and validate the biological workflows at the core of ... PhD) is a plus, but not required. * Hands‑on experience developing nucleic acid-based assays and ...

Pilgrim

Scientist

Redwood City, CA · On-site

As a Scientist at Pilgrim, you will develop and validate the biological workflows at the core of ... PhD) is a plus, but not required. * Hands-on experience developing nucleic acid-based assays and ...

Cantos Ventures

Scientist

Redwood City, CA · On-site

As a Scientist at Pilgrim, you will develop and validate the biological workflows at the core of ... PhD) is a plus, but not required. * Hands-on experience developing nucleic acid-based assays and ...

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Nucleic Acid Phd Scientist information

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$33K

$85.5K

$140.5K

How much do nucleic acid phd scientist jobs pay per year?

As of Sep 12, 2026, the average yearly pay for nucleic acid phd scientist in the United States is $85,539.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,500.00 and $111,500.00 per year, depending on experience, location, and employer.

What does a nucleic acid PhD scientist do?

A Nucleic Acid PhD Scientist researches the structure, function, and manipulation of nucleic acids such as DNA and RNA. Their work often involves designing and conducting experiments to understand genetic information, gene expression, and molecular interactions. They may also develop new techniques for sequencing, editing, or synthesizing nucleic acids, which can have applications in medicine, biotechnology, and forensic science. These scientists typically work in academic, government, or industry laboratories and collaborate with multidisciplinary teams.

What are the key skills and qualifications needed to thrive as a nucleic acid PhD scientist?

To thrive as a Nucleic Acid PhD Scientist, you need advanced knowledge of molecular biology, nucleic acid chemistry, and a doctorate in a relevant scientific discipline. Expertise with techniques such as PCR, qPCR, nucleic acid extraction, sequencing platforms, and data analysis software is typically required. Strong analytical thinking, attention to detail, and collaborative communication skills help drive innovation and effective teamwork. These competencies are essential for designing rigorous experiments, interpreting complex data, and advancing research in genetics or biotechnology.

What are some common challenges faced by nucleic acid PhD scientists when developing new assays or technologies?

Nucleic Acid PhD Scientists often encounter challenges such as optimizing assay sensitivity and specificity, troubleshooting unexpected results, and ensuring reproducibility across experiments. Additionally, they must stay current with rapidly evolving techniques and adapt methodologies to meet project goals or regulatory requirements. Collaborative problem-solving with interdisciplinary teams—such as bioinformaticians, chemists, and engineers—is also crucial for overcoming technical hurdles and driving successful innovation.

What are popular job titles related to Nucleic Acid Phd Scientist jobs?

For Nucleic Acid Phd Scientist jobs, the most frequently searched job titles are:

Infographic showing various Nucleic Acid Phd Scientist job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 85% Full Time, 12% Part Time, and 2% Contract. Highlights an 78% Physical, 4% Hybrid, and 18% Remote job distribution, with an average salary of $85,539 per year, or $41.1 per hour.

ML Scientist, Nucleic Acid Design

San Francisco, CA • On-site

Lila Sciences
See more jobs at Lila Sciencesexternal

Full-time

Re-posted 16 days ago

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.

Apply