Organic Chemist

San Francisco, CA

Full-time

Visa Sponsorship

About the role

At Yoneda Labs, we use state-of-the-art lab automation to run hundreds of organic chemical reactions a week. Using this experimental dataset, we plan to create an AI model that will be able to predict high-yielding reaction conditions for organic reactions without any wet-lab experimentation.

We are looking for an organic chemist who would:

Ensure the day-to-day running of our wet lab

Prepare powders and stock solutions to feed to our robots

Maintain the lab equipment

Analyze HPLC-MS and NMR results using our semi-automatic pipeline

Troubleshot unexpected challenges that show up in our wet lab (e.g. purify impure compounds that we receive from suppliers)

Help set up a bigger wet lab

Help shape the overall chemistry strategy of Yoneda

About us & What we offer

We are an early-stage startup — currently a team of 3 friends in their 20s with backgrounds in Chemistry and Computer Science — and we hope to build datasets and models that will revolutionize how organic synthesis is done.

Apart from a fast-paced startup environment, we offer an interesting job at the intersection of Chemistry and Data-Science in a well-funded lab. As we are a small team without a strongly defined structure, you will be able to work on what you’re best at without any unnecessary internal meetings or reports, helping to define the direction and success of the company.

Skills & Qualifications

As a candidate, you should:

Have experience working in an organic chemistry lab

Be able to work independently, and proactively solve any (unexpected) experimental problems

Bachelor’s or higher degree in chemistry

About Yoneda Labs

Yoneda Labs provides software to help chemists optimise reactions. When chemists make a drug or a material, we help them figure out the best reaction parameters such as temperature, concentration and catalyst.

When Jan was working at chemical labs, he experienced the struggle of spending weeks guessing reaction conditions. We then started experimenting with ML to speed up the process.

Now, as a team of three friends from the University of Cambridge, we’ve spent the last month combining our domain expertise in Computer Science, Machine Learning and Chemistry to develop state of the art models for reaction optimisation.

Although ML is becoming well established in other fields, current chemical models generalise poorly and require lots of programming experience. We make our models easily accessible to chemists in the lab.

Finding the right conditions quickly allows pharmaceutical companies to test more drugs, and finding better optima makes manufacturing process cheaper and more environmentally friendly.