Contract Computational Biologist (Hybrid)

Form Bio•San Francisco, CA

4d•$120,000 - $140,000•Hybrid

About The Position

Form Bio provides award-winning AI and computational solutions for genetic medicine and synthetic biology leaders. By combining data, technology, expertise and lab-in-a-loop, Form's solutions accelerate timelines from discovery to clinic by providing drug developers with rapid in silico characterization and optimization of their therapeutics followed by the lab validation — enabling higher yields, better expression, enhanced safety and shorter, less-expensive development cycles. Form Bio's proprietary patent protected AI models are the first of their kind, purpose-built to solve the most complex challenges in genetic engineering and design for gene and nucleic acid-based therapies. With cross-disciplinary expertise spanning software engineering, biology, bioinformatics, AI, and data science, the Form Bio team collaborates closely with customers on their most pressing and strategic challenges and opportunities. We're looking for a highly skilled Computational Biologist to join our team. The ideal candidate will be an expert in biological data analysis and computational biology, with a passion for leveraging data to solve complex problems. You'll work closely with our R&D and engineering teams, translating biological questions into computational solutions and helping to build tools that will advance scientific discovery and product development. This role sits at the center of three areas we are actively building: gene editing workflows, computational RNA design, and computational protein design. You will help turn these from project work into productized, repeatable capabilities. Because Form Bio works shoulder-to-shoulder with drug developers and synbio partners, this is not a heads-down role. We expect roughly 10–15% of your time to be customer-facing, and we are looking for someone who sees that as a feature rather than a tax.

Requirements

Master's or Ph.D. in Bioinformatics, Computational Biology, Genomics, or a related life sciences field.
Proven experience in a bioinformatician role, including analysis of multiomics datasets (RNA-Seq, WGS, ChIP-Seq, etc.).
Expertise in scripting languages such as Python or R.
Experience with NGS analysis and writing workflows in Nextflow, Snakemake, or WDL.
Solid understanding of biological principles, including molecular biology and genetics.
Hands-on experience in at least one of: gene editing analysis, computational RNA design, or computational protein design.
Willingness to spend approximately 10–15% of your time in a customer-facing role.
Based in the San Francisco Bay Area.
Desire to work collaboratively across multi-functional departments, and to make decisions both collectively and independently.
Self-motivated, detail-oriented, and highly organized, with a drive to reach high-caliber milestones.
Roll-up-your-sleeves mentality; adaptable to start-up environments with competing deadlines and multiple deliverables.
Strong analytical, critical thinking, and problem-solving skills.
Strong verbal and written communication across mediums and audiences, with the poise to hold a scientific conversation with a customer and high emotional intelligence for building relationships at all levels, internally and externally.
Utmost integrity and understanding of confidentiality, as the position requires.
Comfortable in ambiguous situations and results focused; prior experience with fast-paced, early-stage, scaling organizations.

Nice To Haves

Experience across more than one of the three focus areas above.
Familiarity with long-read sequencing data (PacBio, Oxford Nanopore) and the analysis challenges specific to it.
For the wet-lab track: hands-on NGS library prep and sequencer operation experience.
Experience with cloud computing platforms (e.g. AWS, GCP).
Experience with complex data visualization and data engineering.
Exposure to cell and gene therapy, CRISPR, mRNA, or AAV programs in an industry setting.
Prior experience presenting technical work to customers, collaborators, or external scientific partners.

Responsibilities

Design and evaluate guide RNAs across editing modalities
Build and run on- and off-target validation analyses
Quantify editing outcomes from sequencing data
Design and optimize RNA sequences
Model secondary structure, thermodynamic stability, and folding to inform design decisions.
Screen candidate sequences for proper characteristics before they reach the bench.
Apply structure prediction and modeling to engineer therapeutic and tool proteins (e.g. editors, nucleases, capsids, regulatory elements).
Optimize variants for expression, stability, specificity, and reduced immunogenicity, and prioritize designs for experimental testing.
Work with ML/AI teammates to fold protein design models into Form Bio's platform.
Analyze large-scale biological datasets (e.g. genomics, transcriptomics, proteomics) to identify novel patterns and insights.
Design, implement, and maintain bioinformatics pipelines and workflows in Nextflow, Snakemake, or WDL for efficient, reproducible data processing.
Stay current with the latest bioinformatics algorithms, software, and databases to ensure our methods are cutting-edge.
Work with biologists, computational scientists, and software engineers to understand research needs and develop custom analytical tools.
Spend roughly 10–15% of your time in a customer-facing capacity — this is a requirement of the role, not an optional add-on.
Join scientific discussions with customer teams to scope problems, present results, and translate their questions into analyses we can run.
Clearly communicate complex results and findings to both technical and non-technical audiences, and represent Form Bio's science credibly to external partners.
Execute NGS and long-read library preparation and sequencing runs (Illumina short-read; PacBio HiFi and/or Oxford Nanopore long-read).
Support amplicon, targeted panel, and whole-genome or whole-transcriptome workflows used for editing and design validation.
Own run QC and troubleshooting, and feed practical assay knowledge back into pipeline and experiment design.
Help close the loop between in silico design and empirical validation — designing the assay you will later analyze.

Benefits

Award-winning AI and computational solutions
Accelerate timelines from discovery to clinic
Higher yields, better expression, enhanced safety
Shorter, less-expensive development cycles
Patent protected AI models
Cross-disciplinary expertise
Collaborates closely with customers
Customer-facing role as a feature
Commitment, colorful, passionate, intelligent, collaborative, creative, experienced, scientific, and world-class team
Culture of positivity, hope, happiness
Mutual respect
Equal employment opportunities
Diversity is paramount to success