Note: The job is a remote job and is open to candidates in USA. Randstad Digital Americas is seeking a contractor to support computational drug discovery initiatives involving AI and machine learning model development. The role focuses on fine-tuning, benchmarking, and automating co-folder, 3D structure generation, and affinity prediction workflows across multiple lead optimization projects.
Responsibilities
- Start from a pre-trained foundation model such as Boltz-2 or AISB and fine-tune it on AbbVie data
- Compile AbbVie datasets from multiple advanced LO projects including both experimental protein-ligand complex structures and potency/affinity data
- Evaluate dataset variation across projects to challenge model applicability domains, ranging from small sets with a few structures and ~100 affinity data points to larger sets with 10+ structures and thousands of affinity data points
- Benchmark model performance against established methods and tools, including DeepAutoQSAR, GatorAffinity, MPNN, MM-GBSA, FEP, and related approaches
- For 3D workflows, compare pose generation using co-folder approaches against docking-based methods and assess performance
- Use project-specific thresholds to evaluate recall and precision as primary metrics
- Using confidence and domain of applicability, develop model ranking across affinity prediction model types
- Determine whether a single fine-tuned co-folder model trained across selected projects is sufficient, or whether project-specific models are required
- Design the workflow with automation in mind, ideally enabling an end-to-end process from protein and ligand sequence to predicted 3D structure and affinity
- Optimize workflow for efficiency that single predictions can be generated in less than one minute on GPU or CPU resources
Skills
- Strong experience in machine learning, computational chemistry, cheminformatics, or related scientific data science domains
- Experience in training and fine-tuning models on domain-specific scientific datasets
- Working knowledge of benchmarking methodologies and model performance evaluation
- Experience with 3D modeling workflows, pose generation, or docking-based comparison studies
- Strong scripting and automation skills, preferably in Python
- Ability to design reproducible model workflows with attention to runtime and scalability
- Strong collaboration and communication skills for working across scientific and technical teams
- 5+ years of experience
- Experience supporting drug discovery or lead optimization projects
- Familiarity with co-folder models, Boltz-2, AISB, DeepAutoQSAR, GatorAffinity, MPNN, MM-GBSA, or FEP-related methods
- Experience designing automated scientific workflows from sequence input to structure and affinity output
- Exposure to building model benchmarks using project-specific thresholds and precision/recall metrics
- Ability to work independently and deliver high-quality technical solutions in a contractor setting
Benefits
- Medical insurance offerings, based on eligibility
- Prescription insurance offerings, based on eligibility
- Dental insurance offerings, based on eligibility
- Vision insurance offerings, based on eligibility
- AD&D insurance offerings, based on eligibility
- Life insurance offerings, based on eligibility
- Short-term disability, based on eligibility
- A 401K plan, based on eligibility
- Telecommute
Company Overview
Company H1B Sponsorship