About Precision Federal
Precision Federal is a small business building AI, data and software systems for government agencies. The company’s founder loves God, and the company is built on Christian faith: every number we publish is real, every claim we make is verifiable, and the work is done with excellence as service to God. Faith is welcome here and openly shared.
How this role works
- You join the bench. You apply with your weekly hours and start time. We set your hourly rate from your résumé, and we may name you on proposals that match this role. If we don't win, you owe nothing.
- A contract is awarded. The award funds the team named in the proposal. Your engagement begins at the hourly rate we agreed on.
- You work remote, paid from the contract. ~80–480 hrs per funded award — and if no award ever funds, you owe nothing and lose nothing.
What you'll do
- Design and build models that represent protein structure and molecular surfaces in ways that carry real chemical and geometric meaning
- Develop and test binding-site, interface and docking prediction methods against experimental structures and binding data
- Tell us where an approach breaks on real proteins: flexible regions, missing residues, low-confidence predicted structures or data leakage between train and test sets
- Check our claims about accuracy, generalization and novelty before they reach the Government
- Point us at the dataset, benchmark, structure source or method we should be using
Required qualifications
- PhD in computational structural biology, biophysics, computational chemistry, bioinformatics or a closely related field
- Hands-on experience modeling protein structure and molecular surface representations, such as meshes, point clouds, graphs or learned geometric embeddings
- Direct work on binding-site, interface or docking prediction, with results you can describe and defend
- U.S. citizen or lawful permanent resident (U.S. person)
- Willing to be named on a federal proposal with a one-page resume, and to sign a short letter of commitment
Preferred qualifications
- Geometric deep learning or equivariant networks applied to proteins, such as MaSIF, dMaSIF, GVP, EGNN or similar
- Working use of AlphaFold, RoseTTAFold, ESM or comparable structure and language models
- Docking and physics-based tools such as AutoDock Vina, Rosetta, HADDOCK, PyMOL or ChimeraX
- Peer-reviewed publications in protein structure, docking or protein machine learning
- Experience on federally funded research, such as SBIR, STTR, NIH, DOE or DoD awards
Compensation & structure
1099 consulting engagement. $30–$70 per hour; we set your rate from your résumé and experience. Hours are funded by federal contract awards — this seat is scoped at roughly 80–480 hours per funded award, fully remote. Work and pay begin only if the contract is awarded — no award, no hours, no obligation.