Senior Computational Chemist

Adiabatic Materials | Somerville, MA

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.

Why Adiabatic might be great for you

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.

The Role

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.

What You'll Do

Synthesis Planning: Review the available open-source tools, incl. ASKCOS and RetroAgent, 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 synthesis.

AI Tools and Code: Lead our in-house AI tools for route planning, optimization and cost prediction, built on third-party or open-weight LLMs, and be our go-to-person for AI tools more broadly. Write code quickly with AI coding tools, and keep it clean and documented so the team can use it.

What We're Looking For

Many strong candidates don’t fit a standard blueprint. If you bring relevant experience and are excited about what we’re building, we encourage you to apply even if you don’t check every box.

Required:

• PhD in cheminformatics, computational chemistry, chemical engineering, computer science or a closely related field, with a focus on cheminformatics or machine learning for chemistry, or a master's degree plus two years of relevant experience
• Strong programming skills in Python and efficient use of AI coding tools, with a track record of code that others use, e.g. a tool, a package, a public repository or a publication with code
• Hands-on cheminformatics and machine learning for chemistry: RDKit or similar toolkits, molecular and reaction representations, and reaction and molecular databases
• At least an undergraduate-level understanding of organic chemistry, enough to judge whether a proposed route is plausible and what a reaction template represents
• Hands-on experience in at least one of: synthesis planning and retrosynthesis, reaction outcome or condition prediction, reaction data curation, Bayesian optimization or design of experiments, automated or high-throughput experimentation

Nice to Have:

• Experience with a retrosynthesis planner such as ASKCOS or CAS SciFinder (a strong plus)
• Hands-on synthetic lab experience, e.g. a bachelor thesis in a synthetic lab
• Pharmaceutical process chemistry, API supply chains, techno-economic analysis or quantum chemistry (e.g. DFT)
• Industry or startup experience is a plus, not a requirement
• Electrochemistry experience is not required

Working Style & Character:

• You like building tools people use: you ship a working first version within weeks and improve it step by step
• You seek out the scientists and engineers, ask what they need, and enjoy working with them on site
• You are clear about what a model or a dataset can and can't tell us, and you know when computation or AI is the right tool and when it isn't
• You can explain a model's answer and its uncertainty to non-specialists, e.g. for a partner or investor conversation
• You pick up adjacent work when the company needs it, e.g. a techno-economic check or a look at market data
• You take high-level direction, run with it and manage your own time against milestones, you're comfortable with less structure than in a big company, and you care about building a great team culture as we grow

Logistics

• Location: Greentown Labs, Somerville, MA. On site four days a week
• Work style: Flexible, family-friendly schedule
• Compensation: $130K to $185K/year, plus meaningful equity and benefits (negotiable). The range is deliberately broad because it spans a strong recent PhD through to about five years of experience

How to Apply

Please submit your resume and include a few sentences about why you're excited about Adiabatic and how this role aligns with your experience. This is also an opportunity to highlight experiences that you feel are particularly relevant for the role. This doesn't need to be a formal cover letter; we just want to understand what motivates you.