Machine Learning Researcher
- Boston, MA, United States
- $80k - $150k
- 0.5% - 1.3%
This role will be focused on developing machine learning algorithms to design molecular structures, forecast chemical properties and predict the outcomes of organic synthesis. You'll also be collaborating with some of the world's top pharma to co-develop state-of-the-art models in these areas to be applied to drug discovery projects.
You will work closely with domain experts in drug discovery to curate datasets, benchmark algorithms, analyze biotech assay results, deploy models and data pipelines at-scale, and relentlessly get to the truth about what works and what doesn’t in tools for drug discovery. Your favorite tool is whichever one helps the world cure more diseases faster, full stop.
We envisage that some of the work will be showcased at top conferences/journals -- giving back to the scientific community is a core value of PostEra.
- At least 2-3 years industry experience applying ML to 'real-world' problems.
- Comfort with modern development and deployment tools (Git, Linux, AWS).
- Proven competence in modern deep learning architectures.
- Experience in applying ML to chemistry or drug design problems.
PostEra is a Y Combinator-backed company using machine learning, applied to medicinal chemistry, in order to expedite the discovery of new drugs and get new cures to patients faster.
What are we working on?
Partnerships: We partner with drug hunters and help them get cures to patients faster. We're currently collaborating with Pfizer and several small biotechs, using all of PostEra's ML-powered software to help their drug discovery.
Manifold: The first-of-its-kind SaaS platform for scientists to get molecules made.
COVID Moonshot: PostEra is helping lead the world's largest open-source drug discovery effort. Moonshot is an international team of scientists working on an antiviral cure for COVID and in 1 year we've gone from ideas on a website to potent compounds which should soon be ready for human trials preparation.
Medicines come before models. We're all nerds at heart, either for science, ML or engineering but ultimately the mission of getting new cures to patients is more important than the technology we use. As such, everyone is very focused on innovation only so far as it helps our mission.
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