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Machine Learning Scientist - Synthesis Planning and Optimization

Genentech -
Basel, BS

Jetzt bewerben

Details zur Stelle

100% | Pensum: 90-100%
CHF 147’600 - CHF 310’800 pro Jahr
vor 7 Stunden

Leistungen

Unterkunft

Qualifikationen

Doktorat
GitHub
GitLab
Python

Vollständige Stellenbeschreibung

The Position

Join the small-molecule team within AI for Drug Discovery (AI4DD), formerly Prescient Design, at Roche and Genentech’s Computational Sciences Center of Excellence as a Machine Learning Scientist / Senior Machine Learning Scientist in Synthesis Planning and Optimization. You will build ML methods that design molecules we can actually make — closing the loop between generative design and automated synthesis.

The Opportunity:

Develop and advance machine learning methods for synthesis-aware molecular design across retrosynthesis, synthesis planning, molecular generation, and search in synthesizable chemical spaces.
Integrate proprietary reaction and biochemical data to design the next generation of synthesis-aware models and workflows for hit finding and optimisation.
Build robust, scalable pipelines for active-learning loops that interface directly with automated and high-throughput synthesis platforms.
Design novel batch synthesis-planning algorithms that maximise chemical-space coverage, information gain and experimental efficiency.
Drive scientific impact through publications, open-source releases, and conference talks.
Collaborate widely with computational and experimental researchers at Roche and with academic partners.

Who you are:

You bring deep machine-learning expertise with a strong foundation in linear algebra, probability and optimization, and hands-on experience in modern machine learning approaches such as graph-neural networks, sequence/language models and reinforcement learning.
You are familiar with chemistry concepts relevant to synthesis planning and molecular optimisation as well as small molecule data and cheminformatics toolkits such as RDKit or Openeye.
You are fluent in Python and have experience with modern ML frameworks like PyTorch or JAX as well as scientific software development.
You hold a PhD or equivalent research depth in machine learning, computational chemistry, chemical engineering or a related quantitative field such as physics or statistics.
You have a record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab)..

Preferred:

Experience with retrosynthesis or synthesis-planning models.
Experience with automated/high-throughput synthesis.

If designing molecules that move from screen to synthesis to patients excites you, apply now and help build self-driving discovery at Roche.

The expected salary range for this position based on the primary location of California for the Machine Learning Engineer is $147,600, - $274,000, and the Senior Machine Learning Engineer for California is $167,400 - $310,800. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

#ComputationCoE

#tech4lifeComputationalScience

#tech4lifeAI

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.

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