Home › Companies › Edison Scientific › Member of Technical Staff - Computational Biology
Member of Technical Staff - Computational Biology
Edison Scientific · San Francisco, CA · On Site · Active · Greenhouse
Job facts
| Field | Value |
|---|---|
| Company | Edison Scientific |
| Title | Member of Technical Staff - Computational Biology |
| Normalized title | - |
| Department / team | Science |
| Location | San Francisco, CA, United States |
| Work model | On Site |
| Employment type | - |
| Salary | - |
| Status | active |
| ATS provider | Greenhouse |
| Posted / first seen | 2026-02-13 / 2026-05-29 |
| Changed / last seen | 2026-05-29 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Edison Scientific. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Greenhouse. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in San Francisco. | Open |
| Department jobs | Active postings in Science. | Open |
| Work model jobs | Active On Site postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Edison Scientific |
| Source | 3e88a1f8-3904-45aa-8042-af6d399b3539 |
| ATS provider | Greenhouse |
Description
About
Edison Scientific builds and commercializes AI agents for science. Scientific discovery moves too slowly, and autonomous AI agents are how we intend to fix that. We're assembling a team of top researchers and engineers across AI and biology to build an AI scientist.
Role
As a Member of Technical Staff - Computational Biology, you'll build and evaluate AI agent systems to automate biological discovery. You'll focus on improving how LLMs execute complex scientific tasks, creating benchmarks to measure their performance, and collaborating with researchers to generate and validate novel findings in biology.
You'll be working alongside exceptionally smart, mission-driven people on problems that genuinely matter. Instead of optimizing ad clicks, you'll be advancing science and making breakthrough discoveries in basic research. We're well-resourced with a wet lab, extensive compute, and the tools you need to do your best work.
This role is on-site at our San Francisco office in the Dogpatch neighborhood. Our office is a converted warehouse with high ceilings, open space, and a team that genuinely believes in what they're building.
This position is part of the Discoveries team.
Responsibilities
Improving the ability of LLM agents to execute long, coherent data-driven discovery tasks
Building benchmarks to automatically evaluate the performance of LLM agents on a wide variety of scientific tasks
Working with collaborators to apply AI agents to make novel discoveries in biology
Evaluating the accuracy of AI-generated discoveries through independent human analysis
Leading or contributing to publications describing technical achievements
Requirements
PhD in biological science, broadly defined
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.
Expertise in one area of mammalian biology
Familiar with the advantages and limitations of chat-based LLM and agentic coding tools to perform data analysis tasks in biological research
Strong critical thinking skills and ability to identify mistakes in LLM-generated analyses
Excellent written and verbal communication skills
Good attention to detail
Bonus points for
You enjoy quickly iterating on ideas through rapid prototyping
You operate with high independence, and like to execute complex tasks to completion with minimal supervision
You thrive when working on a diversity of projects in sprint-based formats, and have high comfort with uncertainty
You enjoy trying out the newest AI tools
You are proficient in experimental design, and could execute experiments yourself or via a CRO if necessary
Salary
$160,000 - $250,000 • Offers equity
Why join us?
Competitive salary and equity
Full healthcare coverage — we pay 100% of premiums for you and your dependents
Support for growing families, including a yearly new parent stipend and fertility coverage through Carrot
401(k) company matching
$300 health and wellness benefit
Lunch is on us every day you're in the office, and dinner is on us when you're working late
Regular team offsites and company events
A fast-moving, mission-driven culture where smart people do their best work and actually enjoy doing it
Full job record
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| Org ID | 44a0f242-a76e-4a4e-8d1a-c717646ec362 |
| Source ID | 3e88a1f8-3904-45aa-8042-af6d399b3539 |
| Board ID | 3e88a1f8-3904-45aa-8042-af6d399b3539 |
| Provider | greenhouse |
| Provider Job Key | 5051959007 |
| Title | Member of Technical Staff - Computational Biology |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | San Francisco, CA |
| Department | Science |
| Team | — |
| Employment Type | — |
| Workplace Type | on_site |
| Remote Policy | — |
| Country | United States |
| Region | CA |
| City | San Francisco |
| Salary Raw | — |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://job-boards.greenhouse.io/edisonscientific/jobs/5051959007 |
| Apply URL | https://job-boards.greenhouse.io/edisonscientific/jobs/5051959007 |
| First Seen At | 2026-05-29 22:57:52Z |
| Last Seen At | 2026-06-06 19:59:50Z |
| Last Checked At | 2026-06-06 19:59:50Z |
| Last Changed At | 2026-05-29 22:57:52Z |
| Inactive At | — |
| Source Posted At | 2026-02-13 02:28:36Z |
| Source Updated At | 2026-03-06 22:57:42Z |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=greenhouse/board=edisonscientific/date=2026-06-06/2026-06-06T19-59-49-960Z-3234da87baadfff782f702c01020910074e5dcd09ef15c7cc11da837e2de3af0.json |
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