Research Scientist (AI)

TL;DR

Research Scientist (AI): Developing next-generation structure prediction and binding affinity models for drug discovery with an accent on protein-ligand co-folding and deep learning. Focus on translating cutting-edge research into scalable workflows and redefining computational drug discovery.

Location: Remote (United States)

Salary: $112,000 – $210,000

Company

hirify.global is a high-growth company delivering AI solutions to address challenges in life sciences, financial services, navigation, and cybersecurity.

What you will do

• Develop and iterate on deep learning models for protein-ligand co-folding and structure prediction based on latest research.
• Design and execute systematic evaluation pipelines to benchmark model performance against state-of-the-art methods.
• Collaborate with senior scientists and engineers to integrate validated models into production-ready drug discovery workflows.
• Employ computational and data analysis techniques on structural and sequence datasets to inform model development.
• Present research progress through internal scientific talks, technical write-ups, and peer-reviewed publications.
• Partner with multidisciplinary teams, including ML engineers and structural biologists, to scale impactful solutions.

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).
• Experience developing, training, and validating deep learning models, specifically Transformers, equivariant neural networks, or diffusion models.
• Strong proficiency in Python and modern ML frameworks such as PyTorch and/or JAX.
• Demonstrated ability to design controlled experiments and interpret results critically.
• Must be based in the United States.

Nice to have

• Active or recently completed postdoctoral research in co-folding or structure-based drug design.
• Familiarity with binding affinity prediction methods, including physics-informed approaches.
• Authorship of publications in venues such as NeurIPS, ICML, Nature Methods, or bioRxiv.
• Experience deploying ML workflows on public cloud infrastructure (GCP, AWS, or Azure).
• Familiarity with biopharma drug discovery workflows, including hit identification and lead optimization.
• Experience with agentic coding tools (e.g., Claude Code, Codex) for research prototyping.

Culture & Benefits

• Competitive base salary, performance-based incentives, and equity participation.
• Comprehensive medical, dental, and vision coverage for employees and dependents.
• Retirement savings with company matching and paid parental leave.
• Flexible paid time off and company-wide seasonal breaks.
• Support for flexible work arrangements and access to internal learning and development programs.