> Markdown version of [/jobs/ext/2007840-machine-learning-researcher](https://www.wearedevelopers.com/jobs/ext/2007840-machine-learning-researcher). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Researcher

- **Company:** Capable LLC
- **Location:** San Francisco, CA, United States
- **Salary:** $150,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Machine Learning, Model Validation, Data Analytics, Build Tools, Machine Learning Operations
- **Published:** August 9, 2026
- **Apply:** https://www.adzuna.com/details/5834488392

## About the Role

* You have, or are eager to develop, strong research judgment in biomolecular modeling.
 * You have, or are eager to develop, strong judgment about bottlenecks in end-to-end drug development.
 * You care about wet-lab reality and are excited to work closely with wet-lab scientists.
 * You take ownership of outcomes end to end: you can prioritize important problems, scope the necessary experiments, develop strong solutions, and present clear results.
 * You are curious and data-driven; you enjoy developing and testing hypotheses, and you update your views based on empirical evidence.
 * You are motivated to accelerate drug development.
 * Bonus: Experience or a strong project history in active learning, biological modeling in data-constrained regimes, or multimodal models spanning omics, imaging, and phenotypic data.
 * Bonus: Experience or a strong project history building production-scale agent platforms.

## Description

We are a vibrant and intensely mission-driven team in San Francisco, comprising members from MIT, Harvard Medical School, Roche, ETH, and Dana-Farber.

We value speed and rigor, coupled with excitement, drive, and a strong work ethic. In an early-stage environment, we value people who can bring clarity to open-ended problems, take ownership of the next steps, and follow through with energy.

Capable Labs is a place for ambitious, high-integrity people who want to become dramatically better. You will be surrounded by people who care intensely about the work, get close feedback from the people making scientific and company-defining decisions, and have room to own increasingly important problems.

We believe excellent work should be met with meaningful reward, ownership, and trust. Responsibility is earned through contribution, not title alone: anyone who demonstrates the judgment, rigor, and follow-through to move important work forward can earn meaningful scope.

What You'll Do

 * Identify high-impact bottlenecks across our drug discovery pipeline and develop targeted ML approaches to address them.
 * Build systems that support experiment planning, literature triage, protocol drafting, and in silico candidate screening.
 * Develop tools to automate work across the preclinical and clinical stack in close collaboration with wet-lab scientists and operators.
 * Fine-tune and apply protein and structure models, including ESM, AlphaFold-family models, RFdiffusion, and ProteinMPNN, on in-house data to generate and optimize candidates across programs.
 * Develop and implement methods for drug design, including candidate generation, filtering, in silico modeling, and predictive analysis. Relevant approaches may include molecular dynamics, post-training biomolecular models, and probe-based model evaluation.
 * Close the loop between in silico predictions and in vivo results using active learning methods.
 * Build internal evaluations that measure whether ML systems are accelerating drug development in practice.


## Related Videos

- [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering)
- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery)
- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career)
- [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)