Senior Computational Chemist

Reinvent chemistry. Decarbonize industry. Electrify the future.

Today, most chemicals are still made using heat from burning fossil fuels in energy-intensive, polluting processes. Adiabatic Materials has developed a fundamentally new approach: an electrochemical synthesis platform that uses clean electricity instead of heat. The platform grew out of 15 years of Harvard research and has been demonstrated for a broad range of chemicals. Adiabatic recently spun out of Harvard and is scaling its operations in the Boston area.

We’re a small, early-stage team working on hard, meaningful problems. If you get energy from making things actually work and want your contributions to directly shape a company’s trajectory, this could be a great fit.

• You’ll be our first dedicated computational chemist. You’ll own the computational side of our chemistry, reporting to our CEO and working closely with our Chief Scientific Officer.
• Direct strategic impact. Your data and ideas will help decide which products and targets we pursue, and they will feed directly into our conversations with partners and investors.
• Computation close to the lab. You’ll work side by side with the chemists who run the experiments, and see your proposals tested at the bench.
• Chemistry that is genuinely new. Electrochemical reactions with high functional group tolerance, opening shorter routes to pharmaceutical and fine chemical targets.
• Equity and upside. You’ll share in the value you help create, alongside a mission-driven team that cares about science and sustainability.

We’re looking for a Senior Computational Chemist who provides well-founded data and new ideas for our product strategy decisions, and builds out our ability to access new routes efficiently. A big part of the job is code for retrosynthetic analysis and Bayesian optimization: adapting open-source tools such as ASKCOS, extending them with our own reactions, and developing new proprietary code, together with the data and parameters behind it.

The goal is route planning and process optimization that is commercially useful, e.g. which targets benefit most from our reactions and what a product would cost along a proposed route at different scales.

You’re a cheminformatics and machine learning expert with a strong interest in chemistry. You enjoy a balance of chemistry discussions and time with your tools and code, and you understand enough chemistry to judge the output of your tools and to answer simple chemistry questions on your own.

Synthesis Planning: Review the available open-source tools, incl. ASKCOS and Retro Agent, recommend the best fit for our use cases and set it up. Then adapt its code, data and parameters so it proposes the new routes our reactions enable.

Target and Route Screening: Screen promising pharmaceutical and fine chemical targets for places where our reactions shorten a route or solve a selectivity problem, estimate what a product would cost along a proposed route, and back each opportunity with data. The goal is a ranked shortlist of targets for experimental route validation. You’ll also help judge which reactions in development are worth pursuing by their possible economic value.

Reaction Data and Experiment Design: Set up structured capture of reaction and process data that fits the lab team’s daily work, and apply design of experiments or Bayesian optimization to live optimization campaigns.

Working with the Lab: Spend time with the chemists on site, hand them experiment designs for the most promising proposals, and feed the lab results back into the models.

New Materials: Get up to speed on structure/property prediction for new materials we may develop, and rank novel structures for…