Senior Machine Learning Scientist – Synthesis Planning and Optimization, AI for Drug Discovery

Roche

. Develop and advance machine learning methods for synthesis-aware molecular design across retrosynthesis, synthesis planning, molecular generation, and search in synthesizable chemical spaces .

Posted 10/5/2026full-timeUnited StatesSenior💰 $147,600 - $274,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in machine learning and its application to molecular design, with proficiency in Python and modern ML frameworks. Capable of integrating chemical data and developing scalable algorithms for synthesis planning and optimization.

Highest-signal resume keywords

Deep Machine Learning ExpertiseGraph-Neural NetworksFluency In PythonExperience With RDKitPhD In Machine Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningSynthesis PlanningMolecular OptimizationLinear AlgebraProbabilityOptimizationReinforcement LearningCheminformaticsBatch Synthesis-Planning AlgorithmsActive-Learning Loops

Soft Skills

CollaborationScientific Communication

Tools & Technologies

PyTorchJAXRDKitOpeneyeAutomated Synthesis Platforms

Industry Keywords

Computational ChemistryChemical EngineeringRetrosynthesisHigh-Throughput SynthesisScientific Publications

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

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 synthesis-aware models and workflows for hit finding and optimisation
Build robust, scalable pipelines for active-learning loops interfacing with automated and high-throughput synthesis platforms
Design 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 with computational and experimental researchers at Roche and with academic partners
Build ML methods that design molecules that can be synthesized, connecting generative design with automated synthesis

Requirements

What you’ll need

Deep machine-learning expertise with a strong foundation in linear algebra, probability and optimization
Hands-on experience with graph-neural networks, sequence/language models and reinforcement learning
Familiarity with chemistry concepts relevant to synthesis planning and molecular optimisation
Experience with small molecule data and cheminformatics toolkits such as RDKit or Openeye
Fluency in Python
Experience with modern ML frameworks such as PyTorch or JAX
Experience with scientific software development
PhD or equivalent research depth in machine learning, computational chemistry, chemical engineering or a related quantitative field such as physics or statistics
Up to 2 years of industry research experience for Scientist or 2+ years for Senior Scientist
Record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects
Preferred: Experience with retrosynthesis or synthesis-planning models
Preferred: Experience with automated/high-throughput synthesis

Benefits

Comp & perks

A discretionary annual bonus may be available based on individual and Company performance
Benefits detailed at the provided link