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Leveraging AQ - the powerful compound effects of AI + Quantum technology

ML Research Scientist, Co-Folding and Affinity

Research ScientistResearch ScientistFull TimeRemoteSeniorTeam 51-200Since 2021H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

11 days ago

Salary

$112K - $210K / year

Seniority

Senior

Job Description

ML Research Scientist, Co-Folding and Affinity

SandboxAQ

• Implement, experiment with, and refine deep learning models for protein-ligand co-folding and structure prediction • Design and execute systematic evaluation pipelines to measure model performance against state-of-the-art methods and internal benchmarks • Collaborate with senior scientists and engineers to integrate validated models into production-ready drug discovery workflows • Employ computational and data analysis techniques to generate insights from structural and sequence datasets • Present research progress through internal scientific talks, technical write-ups, and contributions to peer-reviewed publications • Work closely with multidisciplinary teams to prototype and scale impactful solutions

Job Requirements

  • Ph.D. in Computational Biology, Biophysics, Computer Science, Computational Chemistry, or a related field
  • Direct experience with protein structure prediction or protein-ligand co-folding methods (e.g., AlphaFold2/3, RoseTTAFold, Chai-1, Boltz, or comparable systems)
  • Experience developing, training, and validating deep learning models
  • Strong proficiency in Python and modern ML frameworks (PyTorch and/or JAX)
  • Demonstrated ability to design controlled experiments, interpret results critically, and iterate effectively on model development
  • Strong written and verbal communication skills; ability to work collaboratively in a fast-paced, multidisciplinary research environment
  • Active or recently completed postdoctoral research in co-folding, structure-based drug design, or closely related computational domain is highly desired.
  • Familiarity with binding affinity prediction methods
  • Authorship of publications or preprints in relevant venues (e.g., NeurIPS, ICML, Nature Methods, PLOS Computational Biology, bioRxiv).
  • Experience deploying ML workflows on public cloud infrastructure (e.g., GCP, AWS, or Azure) is a plus.
  • Exposure to generative models for protein/ligand design, active learning for data generation, foundation models for biomolecules, or QSAR/property prediction is a plus.
  • Familiarity with drug discovery workflows.

Benefits

  • Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
  • Retirement savings with company matching
  • Paid parental leave
  • Inclusive family-building benefits
  • Flexible paid time off
  • Company-wide seasonal breaks
  • Support for flexible work arrangements that enable sustainable performance
  • Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs

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