SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.
We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.
At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.
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.
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.
Domain Foundation: 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.
Hands-On Co-Folding Experience: 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.
ML Model Development: Experience developing, training, and validating deep learning models, including familiarity with architectures relevant to structural biology (e.g., transformers, equivariant neural networks, diffusion models).
Programming Proficiency: Strong proficiency in Python and modern ML frameworks (PyTorch and/or JAX).
Scientific Rigor: Demonstrated ability to design controlled experiments, interpret results critically, and iterate effectively on model development.
Communication and Collaboration: Strong written and verbal communication skills; ability to work collaboratively in a fast-paced, multidisciplinary research environment.
Postdoctoral Experience: Active or recently completed postdoctoral research in co-folding, structure-based drug design, or a closely related computational domain.
Affinity Prediction Exposure: Familiarity with binding affinity prediction methods, including structure-based or physics-informed approaches.
Research Output: Authorship of publications or preprints in relevant venues (e.g., NeurIPS, ICML, Nature Methods, PLOS Computational Biology, bioRxiv).
Cloud Computing: Experience deploying ML workflows on public cloud infrastructure (e.g., GCP, AWS, or Azure).
ML Techniques for Structural Biology: 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.
Biopharma Context: Familiarity with drug discovery workflows, including hit identification, lead optimization, or structure-based drug design (SBDD).
Agentic Coding Tools: Familiarity with agentic coding tools (e.g., Claude Code, Codex) to accelerate research prototyping.
We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.
Compensation: Competitive base salary, performance-based incentives or bonuses (where applicable), and equity participation.
Benefits: 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.
Work-Life Balance: Flexible paid time off, company-wide seasonal breaks, and support for flexible work arrangements that enable sustainable performance.
Career Development: Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.
We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges.
Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.
Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles. If you need such an accommodation, please let a member of our Recruiting team know.
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