Head of Computational Biology

Location: Boulder, CO

Reports To: Sr. Director, Applied Biology

Role Summary:

The Head of Computational Biology will be responsible for hands-on technical execution and building and leading a small team. They will integrate Alta’s design-build-test-learn (DBTL) data with in silico and bioinformatics approaches to improve predictions of protein expression, activity, specificity, and manufacturability.  A key requirement for this role is a demonstrated ability to translate computational models into experimentally validated candidates that perform in the lab, can be manufactured at scale, and advance toward commercialization, rather than optimizing model performance alone.

The individual will develop and implement computational approaches that connect protein design, experimental results, and predictive modeling to accelerate iterative learning. They will work closely within the Applied Biology group and with LIMS/software developers and Analytical Chemistry groups to define data requirements, experimental workflows, and automation systems that ensure data quality, provenance, and reliability. They will translate research needs into scalable workflows, integrating sample tracking, data capture, analysis, and model feedback.

The ideal candidate combines deep computational expertise with scientific rigor, strong cross-functional communication, and the ability to move between analytical problem-solving, computational development, and team leadership.

Key Responsibilities

• Lead a group to develop model classifications and scoring for protein candidates and chemistries using in silico and real-world data sources.
  

• Establish protein and peptide molecular dynamics and quantum mechanics workflows for in silico scoring.
• Execute workflows hands-on, personally contributing to model development, code, and analysis alongside the team.
• Define success metrics tied to company outcomes (experimentally confirmed hits, manufacturable leads, candidates advanced toward commercialization) rather than model-only benchmarks.
• Build pipelines from existing sequence databases and 3D modeling data.
• Work with a team to develop and implement model fine-tuning using high-throughput performance data.
• Leverage protein manufacturing data to enhance workflows and predict lead-candidates for scalable production.
• Deploy generative AI approaches for streamlining workflows.

• Collaborate with the data science team to implement genotype-to-phenotype relationship tracking to complete the learn cycle for model refinement.
  

• Work across teams to build out a world-class tracking system for proteins across the design, build, test, and scale workflows.
• Leverage sequencing data, production data, and performance tracking to build a state-of-the-art system for multiscale protein and genome engineering.
• Partner with the build and test teams to integrate computational workflows, ensuring designs account for real-world constraints such as throughput, cost, and turnaround time.

• People Leadership and Communication

• Hire, directly manage, mentor, and develop a small team of computational scientists; set clear goals and hold the team accountable for delivery.
• Balance hands-on individual contribution with team leadership as a true player/coach.
• Ensure excellent practices in documentation.
• Effectively communicate results with stakeholders across the company.
• Develop talent through team building and project execution, and foster a collaborative culture between the computational, build, and test teams.

Key Skills and Expertise Required

Required qualifications:

• Ph.D. in computational biology, bioinformatics, or related field with 5–10 of proven success in cross-functional leadership
• Demonstrated people leadership: directly managing, hiring, and developing a team of scientists while remaining a hands-on individual contributor (player/coach).
• Proven track record of delivering outcomes: has built design/test systems that delivered protein, peptide, antibody, or similar candidates that were commercially relevant and advanced toward commercialization.
• Expertise in machine learning and predictive models focused on protein engineering workflows.
• Expertise in bioinformatics pipelines and databases.
• Experience with generative AI models.
• Experience with molecular dynamics and quantum mechanics parameterization.
• Experience with LIMS and sample management systems.
• Demonstrated knowledge of experimental design, the scientific method, and statistical analysis.
• Demonstrated capabilities for building well-documented and version-controlled data pipelines.
• Strong written and verbal communication skills and ability to work within different teams.
• Strong organizational and documentation skills.

Desired qualifications:

• Experience working within or alongside the build and test functions, not only design and learn. 
• Experience integrating computational workflows with laboratory automation and high-throughput build/test platforms.
• Experience with multiomics data pipelines, flux balance analysis, and metabolic pathway modeling.
• Experience with antibody engineering simulation pipelines.
• Experience with Git, AWS, and standard computational tools.

Compensation & Benefits

The starting pay range for this position is $155,000 - $215,000, commensurate with educational background and work experience. 

Benefits include:

• 401(K)
• Medical, Dental, Vision plans (or equivalent) 
• Flexible Time Off
• Paid Parental Leave
• Paid Sick Leave
• Company Holidays