Founding Computational Scientist, Agentic Drug Discovery

United States Digital Space LLC; United States; On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive compensation

Meaningful equity

Job summary

United States Digital Space LLC seeks a PhD‑level scientific AI product lead to deliver decision‑ready science to pharma R&D teams. You will run engagements end‑to‑end, present findings to leadership, and own the quality of what ships.

Hit the ground running with LLM prompts, eval loops, and tools that extend the company's capabilities. This role blends science, software, and product ownership in a fast‑moving environment.

Qualifications

• PhD in life sciences required or equivalent.
• Experience delivering scientific findings to R&D leadership.
• Hands-on with LLM workflows and prompt/eval loops.

Responsibilities

• Deliver the company's science to pharma R&D teams and present findings to leadership.
• Run the company end-to-end on live engagements and ensure quality of outputs.
• Write and evaluate prompts daily and iterate on learnings.
• Ship learnings back into the product to extend capabilities.

Skills

PhD in life sciences

Computational fluency

Git

Python

LLM workflows

Prompt evaluation

Education

PhD in life sciences

Tools

Git

Python

Job description

About iolloiollo was founded by Daniel Gomari (PhD Computational Biology, Stanford) and Prof. Mike Snyder (Stanford Genetics, 900+ publications) after watching pharma companies spend millions and months making R&D decisions that could be computed in days. We built the company to fix that. the company is an AI scientist that runs autonomous scientific workflows and delivers high-stakes R&D decisions to Fortune 500 pharma companies.

The roleDeliver the company's science directly to pharma R&D teams and ship what you learn back into the product.

the company delivers decision-ready science to pharma partners — target validation, translational strategy, trial design, competitive intelligence, and more. Each partner engagement starts with a hard R&D question and ends with a decision package their leadership can act on. The challenge: run the company and turn every deployment into product improvement. You are the bridge between the AI and the pharma teams that use it.

What you'll do

• Work alongside the company to deliver for pharma partners and present findings to their R&D leadership
• Run the company and own the quality of what ships
• Write and evaluate LLM prompts daily
• Ship what you learn back into the company to extend its capabilities

What you'll need

• A PhD in a life sciences discipline (biology, chemistry, pharmacology, or related) with computational fluency
• 3+ years in pharma R&D with exposure to multiple stages — not just one silo
• Delivered scientific findings directly to R&D leadership or external partners
• Shipped tools, pipelines, or outputs that other people actually used for decisions
• Hands‑on comfort with Git, Python, LLM workflows, and prompt/eval loops
• A self-directed approach — you figure out what needs to happen and do it

You’ll stand out if you

• Understand drug development from target to clinic, not just your specialty
• Built something real with LLMs and can explain what worked and what didn’t
• Have written decision memos, not just papers

You might be exactly right if you're one of these

• An ex‑biotech computational scientist who became a product person or operator
• A scientific AI product engineer — hands‑on with LLM workflows, thinks in product outcomes
• A technical PM from scientific software who uses the tools and inspects outputs directly

Tech stackPython, Git, LLM prompt/eval workflows, scientific data analysis. Pharma R&D domain knowledge across discovery, translational, and clinical stages.

First 90 days

First 30 days:Deliver for a live pharma partner. Run the company end-to-end on a real engagement and present findings to R&D leadership. Prove you can operate independently from day one.First 60 days:Own the delivery playbook. Define how partner engagements run and how deployment learnings feed back into the company. Ship prompt and eval improvements based on real partner feedback.First 90 days:You're defining how the company delivers science, not just executing engagements. The team defers to you on partner delivery.

Why join us

• Your work directly enables scientific decisions that change how drugs get made in the world
• Shape systems that Fortune 500 pharma depends on
• Competitive compensation with meaningful equity

You'd be the company's scientific voice at the partner table. the company finds things human teams miss — you make the call and deliver to partners in days, not quarters. If you want to define how AI gets used in drug discovery, let's talk.