Machine Learning Engineer, Model Experimentation

About Aether Aether engineers enzymes and uses them to make materials that are hard or impossible to produce any other way. The goal is not a better product line. It is to reach the point where more or less anything can be manufactured quickly and cheaply, anywhere, by borrowing a few billion years of evolution rather than inventing the chemistry from scratch.

That work already pays for itself: Aether's print materials are shipping into defense and unmanned systems today. The company is Series A, with a bit over $60M raised.

On the AI side, Aether is generating more protein-material binding data than any other group we know of, aimed at a model where you describe the material you want and it tells you which proteins can make it. About the role This role exists to test modeling ideas quickly and tell the team, with evidence, which ones deserve more time. The ML team is small and senior; this person is its fastest experimental loop. A typical cycle looks like this: take an idea (a new architecture, a different representation, a change to the training objective), set up a baseline, run it, and come back with a clear call to continue, change course, or stop. Most ideas will not work, and that is expected. Success in this role is measured by how quickly and clearly you learn which ideas hold up, not by how many of them do. You will work day to day with senior members of the ML team, who will help set priorities and review your calls. The data you work with comes from real lab measurements, so results can be checked against what actually happened at the bench. What you'll do

• Design and run ML experiments end to end: baseline, model changes, training runs and evaluation.
• Make a clear call on each experiment and write it up in a page or less, including the ones that failed.
• Build evaluations you would trust even when they go against the idea being tested.
• Bring ideas from the wider field, such as world models, latent-prediction objectives or diffusion, and test them against Aether's own data.
• Work within the team's ML infrastructure, and improve it where it slows experiments down.

What we're looking for

• Hands-on experience training and evaluating models yourself, whether in research, a job, competitions or serious personal projects. The number of experiments you have run matters more than years of experience.
• Strong experimental habits: baselines first, 1 change at a time, ablations, and honest evaluation.
• Speed. You are comfortable writing code that runs today and cleaning it up only once an idea earns it.
• A track record of stopping things. You can describe an experiment you abandoned, what told you to stop, and how quickly you made the call.
• Clear written communication of results, especially negative ones.
• Fluency in Python and a modern deep learning framework such as PyTorch or JAX.

Nice to have, and what we'll teach

Nice to have:

• Experience with world models or latent-prediction methods (JEPA, DreamerV3 or similar)
• Experience with diffusion models, in any domain
• Competition results, open-source contributions, or a project you built and maintained yourself

Note: No chemistry or biology background is required. We will teach the molecular and protein side, along with Aether's data and tooling. We would rather hire a strong experimenter and teach the domain than the reverse.

The pay range for this role is $150,000 to $200,000 per year plus equity, with final compensation based on skills and experience.

Aether is committed to building an inclusive and diverse workplace. We welcome applicants from all backgrounds and do not discriminate on the basis of age, disability, gender identity or expression, marital or civil-partner status, pregnancy or maternity, race, religion or belief, sex, or sexual orientation. All employment decisions are based on qualifications, merit, and business needs.

We are committed to providing reasonable accommodations for candidates throughout the recruitment process. If you require any adjustments to participate fully in interviews or assessments, please let us know and we will work with you to support your needs.