ML Research Scientist

Role Description

The AI Sim R&D team creates leading edge ML and physics-based models ("LQMs") to advance drug and materials discovery. We are a flexible, creative, and impact driven team of multidisciplinary scientists and engineers, whose products dramatically accelerate the creation of molecules and medicines.

As a Research Scientist focusing on Co-Folding & Affinity, you will contribute to building SandboxAQ's next generation of structure prediction and binding affinity models. Working alongside a high-performing team of scientists and engineers, you will help advance the state-of-the-art in protein-ligand co-folding, translating cutting-edge research into scalable workflows that power our drug discovery software. This is an opportunity to do frontier science with real-world impact — developing models that redefine what's possible in computational drug discovery.

Key Responsibilities

• Develop and Iterate on Co-Folding Models:
  • Implement, experiment with, and refine deep learning models for protein-ligand co-folding and structure prediction, building on the latest research from the field.
• Drive Rigorous Benchmarking:
  • Design and execute systematic evaluation pipelines to measure model performance against state-of-the-art methods and internal benchmarks.
• Contribute to Research-to-Product Pipelines:
  • Collaborate with senior scientists and engineers to integrate validated models into production-ready drug discovery workflows.
• Apply Data-Driven Methods:
  • Employ computational and data analysis techniques to generate insights from structural and sequence datasets, informing model development decisions.
• Communicate Findings:
  • Present research progress through internal scientific talks, technical write-ups, and contributions to peer-reviewed publications.
• Collaborate Across Teams:
  • Work closely with multidisciplinary teams — including ML engineers, structural biologists, and software engineers — to prototype and scale impactful solutions.

Qualifications

• Ph.D. in Computational Biology, Biophysics, Computer Science, Computational Chemistry, or a related field, with a research focus on protein structure prediction, co-folding, or closely related areas.
• Direct experience with protein structure prediction or protein-ligand co-folding methods (e.g., AlphaFold2/3, RoseTTAFold, Chai-1, Boltz, or comparable systems), developed through graduate or postdoctoral research.
• Experience developing, training, and validating deep learning models, including familiarity with architectures relevant to structural biology (e.g., transformers, equivariant neural networks, diffusion 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.

Requirements

• Active or recently completed postdoctoral research in co-folding, structure-based drug design, or a closely related computational domain.
• Familiarity with binding affinity prediction methods, including structure-based or physics-informed approaches.
• 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).
• Exposure to one or more of the following: generative models for protein/ligand design, active learning for data generation, foundation models for biomolecules, or QSAR/property prediction.
• Familiarity with drug discovery workflows, including hit identification, lead optimization, or structure-based drug design (SBDD).
• Familiarity with agentic coding tools (e.g., Claude Code, Codex) to accelerate research prototyping.

Benefits

• Competitive base salary, performance-based incentives or bonuses (where applicable), and equity participation.
• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions.
• Retirement savings with company matching.
• Paid parental leave, and inclusive family-building benefits.
• Flexible paid time off, company-wide seasonal breaks, and 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.