1

Nucleic Acid Jobs (NOW HIRING)

Platform Lead, Nucleic Acids

Princeton, NJ · On-site

$130K - $200K/yr

Preclinical VP for the Nucleic Acid Platform Salary Range: $130,000 - $200,000. Exact compensation will vary based on experience Other: Vaccination against COVID-19 is a prerequisite for employment ...

Platform Lead, Nucleic Acids

Princeton, NJ · On-site

$130K - $200K/yr

Preclinical VP for the Nucleic Acid Platform Salary Range: $130,000 - $200,000. Exact compensation will vary based on experience Other: Vaccination against COVID-19 is a prerequisite for employment ...

next page

Showing results 1-20

Nucleic Acid information

See salary details

$66.5K

$110.5K

$164.5K

How much do nucleic acid jobs pay per year?

As of Aug 21, 2026, the average yearly pay for nucleic acid in the United States is $110,545.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,500.00 and $125,000.00 per year, depending on experience, location, and employer.

What does a nucleic acid professional do?

Nucleic acid professionals are scientists or technicians who specialize in the study and manipulation of nucleic acids, such as DNA and RNA. Their work often involves extracting, analyzing, synthesizing, or modifying these molecules for research, diagnostics, or therapeutic purposes. They may work in fields like molecular biology, genetics, biotechnology, or medical diagnostics, using techniques like PCR, sequencing, or gene editing. Their expertise is crucial for understanding genetic information, developing new medical treatments, and advancing scientific research.

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

To thrive as a Nucleic Acid Scientist, you need expertise in molecular biology, nucleic acid extraction, and quantitative analysis, typically backed by an advanced degree in biology, biochemistry, or a related field. Familiarity with PCR, qPCR, electrophoresis, and bioinformatics tools is crucial in this role. Strong analytical thinking, attention to detail, and effective teamwork skills help ensure robust experimental design and data interpretation. These abilities are vital to advance research, ensure data accuracy, and contribute meaningfully to scientific discovery and biotechnology innovation.

What are some common challenges faced by scientists working with nucleic acids in a laboratory setting?

Scientists working with nucleic acids often encounter challenges such as sample degradation, contamination, and maintaining RNase- and DNase-free environments. Precise pipetting and careful handling are critical, as even minor errors can impact the integrity and yield of nucleic acids. Collaboration with team members for troubleshooting, sharing best practices, and optimizing protocols is essential for overcoming these hurdles and ensuring successful experimental outcomes.

What is the difference between Nucleic Acid vs Molecular Biologist?

AspectNucleic AcidMolecular Biologist
Required CredentialsLaboratory training, molecular biology certificationsAdvanced degrees (BSc, MSc, PhD) in biology or related fields
Work EnvironmentResearch labs, biotech companies, healthcare settingsResearch institutions, universities, biotech firms
Industry UsageFocuses on nucleic acid extraction, analysis, and manipulationStudies genetic mechanisms, gene expression, and molecular processes

While Nucleic Acid specialists focus on working with DNA and RNA molecules, Molecular Biologists study broader genetic and molecular mechanisms. Both roles often overlap in research settings, but Nucleic Acid professionals typically specialize in laboratory techniques related to nucleic acids, whereas Molecular Biologists may engage in more comprehensive genetic research and analysis.

What cities are hiring for Nucleic Acid jobs?

Cities with the most Nucleic Acid job openings:

What are the most commonly searched types of Nucleic Acid jobs?

The most popular types of Nucleic Acid jobs are:

What states have the most Nucleic Acid jobs?

States with the most job openings for Nucleic Acid jobs include:

Infographic showing various Nucleic Acid job openings in the United States as of August 2026, with employment types broken down into 3% As Needed, 69% Full Time, 25% Part Time, 2% Contract, and 1% Nights. Highlights an 77% Physical, 1% Hybrid, and 22% Remote job distribution, with an average salary of $110,545 per year, or $53.1 per hour.

ML Scientist I/II, Nucleic Acid Design

Lila Sciences

San Francisco, CA

Full-time

Posted 25 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.