# Machine Learning Infrastructure Engineer at Genesis Therapeutics

- Company: Genesis Therapeutics
- What the company does: Genesis pairs frontier AI with world-class drug hunters to discover small molecule medicines. Built on Pearl and GEMS. Partnered with Gilead and Incyte. Backed by a16z.
- Company website: https://www.genesis.ml
- Type: Startups (AI role)
- Level: Mid level
- Location: San Mateo, CA
- Work setup: Remote
- Posted: 2025-11-24
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/genesis-molecular-ai/388e7964-e9a3-4620-89ae-ff21fe6444cc
- Page: https://www.1752.vc/careers/jobs/genesis-therapeutics-machine-learning-infrastructure-engineer/

## About the role

We are looking for a Machine Learning Infrastructure Engineer to build the data and orchestration systems that power molecular AI at Genesis. Drug discovery creates unusual infrastructure challenges: our workflows combine computational chemistry, structural biology, machine learning, and large-scale data processing in ways that don't map neatly onto traditional ETL systems. To meet these requirements, we have built our own pipeline orchestration framework—and we want an engineer excited to push it much further.

## What they're looking for

- Passionate about DAGs. You naturally think about computation as graphs of dependencies and care deeply about how work is scheduled, parallelized, cached, retried, and recomputed
- Impatient about latency. When a pipeline takes hours, your instinct is to understand exactly where the time went and systematically make it faster
- An evangelist for lazy execution and caching. You dislike unnecessary work and look for principled ways to avoid recomputation, move less data, and reuse intermediate results
- A strong systems engineer. You are comfortable reasoning across APIs, distributed systems, storage, serialization, concurrency, resource scheduling, and performance
- Hands-on with data-intensive systems. You have built production pipelines or infrastructure that processes large datasets reliably and efficiently
- Comfortable moving between framework and application layers. You're as interested in designing the orchestration primitive as you are in optimizing the pipeline built with it

Tags: AI