Roche
Roche
Roche este o companie globală din domeniul sănătății, care combină cercetarea și dezvoltarea farmaceutică cu testarea diagnostică și sănătatea digitală. Activitatea sa se concentrează pe îmbunătățirea rezultatelor pentru pacienți prin depistare mai timpurie, tratamente mai bine direcționate și inovație bazată pe știință, inclusiv instrumente care utilizează inteligența artificială. Compania dezvoltă soluții pentru boli complexe, precum cancerul și afecțiunile neurologice, colaborând cu sisteme de sănătate, parteneri de cercetare și alte părți interesate din întreaga lume.

Senior Machine Learning Scientist, Synthesis Planning and Optimization

Develop machine learning models for synthesis-aware molecular design, planning, generation, and optimization. Build automated synthesis workflows supporting Roche’s AI-driven drug discovery research.

Descriere

  • Advance machine learning methods for synthesis-aware molecular design, including retrosynthesis, synthesis planning, molecular generation, and search across synthesizable chemical spaces.
  • Combine proprietary reaction and biochemical data to create synthesis-aware models and workflows for hit discovery and optimization.
  • Develop reliable, scalable active-learning pipelines that connect with automated and high-throughput synthesis platforms.
  • Create batch synthesis-planning algorithms that improve chemical-space coverage, information gain, and experimental efficiency.
  • Contribute to scientific progress through publications, open-source projects, and conference presentations.
  • Partner with computational and experimental scientists at Roche and with external academic collaborators.
  • Develop machine learning approaches that generate synthesizable molecules and link generative design with automated synthesis.

Cerințe

  • Advanced machine learning expertise grounded in linear algebra, probability, and optimization.
  • Practical experience with graph neural networks, sequence or language models, and reinforcement learning.
  • Working knowledge of chemistry concepts used in synthesis planning and molecular optimization.
  • Experience handling small-molecule datasets and using cheminformatics tools such as RDKit or OpenEye.
  • Proficiency in Python.
  • Experience with contemporary machine learning frameworks, including PyTorch or JAX.
  • Background in scientific software development.
  • PhD or comparable research experience in machine learning, computational chemistry, chemical engineering, physics, statistics, or a related quantitative discipline.
  • Up to two years of industry research experience for Scientist level, or more than two years for Senior Scientist level.
  • Evidence of scientific excellence through journal or conference publications, or a public portfolio of relevant work.
  • Preferred: Experience developing retrosynthesis or synthesis-planning models.
  • Preferred: Experience with automated or high-throughput synthesis.

Beneficii

  • A discretionary annual bonus may be offered based on individual and Company performance.
  • Benefits are described at the provided link.

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