Computational Biologist

ABOUT US Edison Scientific builds and deploys AI scientist agents to accelerate science and the development of new medicines. We are an ambitious team run by scientists and engineers from leading institutions across biology, physics, chemistry, and AI. Role As a Computational Biologist, you will build and evaluate AI agent systems to automate biological discovery and clinical development. You'll be embedded directly with some of the world's leading R&D organizations, and will be responsible for ensuring Edison’s AI agents deliver. You’ll work with our partner to create benchmarks that measure the performance of our agents on complex tasks across the drug discovery and development pipeline, then work with our internal AI engineering team to improve the performance of our agents on those tasks. You'll need to deeply understand our partners’ biology, their research workflows, and their unmet needs, then determine where our platform can drive the most meaningful discovery. This isn't remote analysis or academic consulting. You'll be on-site and on the front lines, building relationships and credibility with scientists and research leaders, identifying the highest-leverage biological problems, and applying AI agents to generate and validate novel findings in fast cycles. You'll operate with a high degree of scientific independence and serve as the bridge between what our engineers and platform and what the science actually requires, feeding insights back to our product and applied AI teams to shape how our agents reason about biology. Responsibilities - Improving the ability of LLM agents to execute long, coherent data-driven discovery tasks within real-world scientific environments and customer data infrastructures.

• Designing benchmarks to evaluate LLM agent performance, acceptance criteria, and failure modes on complex scientific tasks.
• Partnering directly with scientists and research leaders at client organizations to apply AI agents and make novel discoveries in biology.
• Serving as the scientific authority on AI-generated findings, evaluating the accuracy of discoveries for biological validity, identifying failure modes, and determining what warrants further investigation.
• Contributing to internal knowledge artifacts, scientific playbooks, and publications that advance the field of AI-driven biological discovery.
• Partner with GTM and account executives to scope engagements, define success metrics, and support pre-sales technical evaluation.
• Build trusted relationships with research scientists and research leaders with customers, earning credibility as both a scientific peer and a knowledgeable AI practitioner.

• Familiarity with the regulatory and compliance landscape in pharma or biotech research.
• Experience analyzing one or more of the following types of complex biological data in a first-author publication: sc-omics data, high throughput screen data, proteomics or lipidomics data, human genetic data, imaging data, or protein structure data.
• Deep expertise in one area of mammalian biology and track record to rapidly develop working fluency in adjacent domains.
• Hands-on experience working with LLM and agentic AI tools in a biological research context.
• Strong critical thinking skills and ability to identify mistakes in LLM-generated analyses.
• Experience presenting scientific findings to both scientific and non-scientific audiences, across all levels, communicating biological complexity, AI limitations, and research outcomes.

• You enjoy quickly iterating on ideas through rapid prototyping.
• You operate with high independence, and like to execute complex tasks to completion in a high-stakes environment.
• You thrive when working on a diversity of projects in sprint-based formats, and have high comfort with uncertainty.
• You are proficient in experimental design, and could execute experiments yourself or via a CRO if necessary.
• Prior experience in an industry or client-facing research context, outside of academia.

• Showcase AI‑agent pipelines with measurable benchmarks – In your resume or a GitHub repo, include a project where you built an AI model (e.g., a reinforcement‑learning agent or a generative model) that automates a specific drug‑discovery step (target identification, hit‑to‑lead, etc.). Report concrete metrics (precision/recall, enrichment factor, time‑to‑candidate) and compare them against a baseline to demonstrate you can “create benchmarks that measure performance,” exactly what Edison’s interviewers will probe.
• Demonstrate mastery of GTM tools and data‑flow orchestration – Cite hands‑on experience with the GTM (Gene‑Target‑Mapping) platform (or its Python API) and any related workflow managers (e.g., Airflow, Prefect, Nextflow). In a brief “Technical Skills” section, list specific functions you’ve used (e.g., `gtm.load_expression()`, `gtm.run_pathway_enrichment()`) and include a 1‑page code snippet in your application portfolio that pipelines raw omics data into an AI‑agent input.
• Tailor your cover letter to Edison’s interdisciplinary culture – Reference at least one recent Edison publication or blog post about AI scientist agents, and explain how your background in both computational biology and software engineering (e.g., CI/CD, containerization with Docker/Singularity) will help you “work directly with leading R&D organizations” and accelerate their AI agents.
• Prepare a concise “impact story” for each interview round – Use the STAR format to describe a project where you identified a bottleneck in a biological workflow, built an AI solution, and quantified the downstream time or cost savings. Edison’s interviewers look for evidence that you can “ensure AI agents deliver” in real‑world partner settings, so quantify the outcome (e.g., “reduced lead‑optimization cycle by 30 %”).
• Create a remote‑ready portfolio site – Host a lightweight site (e.g., GitHub Pages) that showcases 2–3 relevant projects: (1) AI‑agent benchmark suite with reproducible notebooks, (2) GTM‑driven target validation pipeline, (3) Collaboration demo with a mock R&D partner (use a README to outline communication workflow). Include clear links in your resume; Edison’s hiring team will click through to verify both technical depth and communication clarity.
• Negotiate salary with data‑driven market comps – Research remote US salaries for Computational Biologists at AI‑driven biotech (e.g., 140–180 k base + 15–25 % RSU). Prepare a one‑page “Compensation Summary” that cites levels from levels.fyi, Glassdoor, and recent Edison job ads. When the offer comes, frame your ask around the high cost of living adjustments for remote work and the value of your proven AI‑agent benchmark expertise.