As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.
Until is a moonshot company building a “pause button” for biology. Our near-term focus is organ-scale reversible cryopreservation: preserving donated organs at subzero temperatures without ice formation, then rewarming them uniformly for transplant. By solving this grand challenge, we’re laying the foundation for whole-body reversible cryopreservation, giving patients a bridge to future cures.
To achieve our goal, we are assembling an interdisciplinary team to develop perfusion systems, cryoprotectant formulations, and vitrification and rewarming hardware. We are also building out our medical hibernation team to tackle the challenges of whole-body cryopreservation, beginning with rodent models.
We envision a future where no transplantable organ is lost to logistics, and no terminal diagnosis is final because patients can safely wait for future medicine to arrive.
About the Role
As a Machine Learning Engineer / Scientist at Until, you will be an early member of the computational team defining how experimental data becomes insight and drives the next round of scientific discovery. You’ll build high-leverage ML systems that help develop new cryoprotectant formulations, engineer biologically-inspired antifreeze proteins, and understand the physics of vitrification and rewarming. You will own projects end-to-end including shaping data collection and designing data pipelines, training and evaluating models, and deploying tooling that scientists use daily.
About You
- Degree in Computer Science or a related field (Applied Mathematics, Statistics, Data Science, Computational Biology).
- Excellent foundations in the mathematics that underlies machine learning, including linear algebra, probability, statistics, and calculus.
- Strong experience in modern machine learning approaches, such as representation learning, generative modeling, active learning, and bayesian optimization.
- Track record of developing ML approaches for scientific discovery, as evidenced by a strong publication record, substantial open source contributions, or deployment of a machine learning system in an industry role.
- Demonstrated ability to write modular, maintainable, and performant code in Python.
- Fluency with the Python data science and ML stack, including PyTorch, NumPy, SciPy, Pandas/Polars, Matplotlib/Plotly.
- Proficient with developer tooling, including Linux command line, Git, and shell scripting.
- Ability to think from first principles and tackle complex, cross-disciplinary problems with other scientists and engineers.
Preferred Qualifications
- 3+ years of relevant professional or research experience, or a PhD in a computational field.
- Strong understanding of computer science fundamentals, including algorithms, operating systems, and concurrency.
- Experience with cloud infrastructure (AWS, GCP) and SQL databases.
Benefits
- Opportunity for outsized impact creating the future as an early team member
- Generous medical, dental and vision insurance coverage
- Flexible time off and paid holidays
- Competitive compensation package, including salary and equity
- 401(k) retirement savings plan
- FSA and commuter benefits
- Subsidized lunch daily
As an equal opportunity employer, Until is committed to providing employment opportunities to all individuals. All applicants for positions at Until will be treated without regard to race, color, ethnicity, religion, sex, gender, gender identity and expression, sexual orientation, national origin, disability, age, marital status, veteran status, pregnancy, or any other basis prohibited by applicable law.
Until Labs San Francisco, California, USA Office
San Francisco, California, United States
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