ML Research Scientist, Co-Folding and Affinity

SandboxAQ

12d•$112,000 - $210,000

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.

Benefits

Competitive base salary
performance-based incentives or bonuses (where applicable)
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
inclusive family-building benefits
Flexible paid time off
company-wide seasonal breaks
support for flexible work arrangements
Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.