About Mind MovesMind Moves is a women-owned Washington, D.C.-based firm helping businesses and governments to embrace, build and deploy ‘human in the loop’ AI solutions to elevate mission and value. We are pioneers with deep expertise in the field of machine learning and AI (both predictive and generative). We have delivered responsibly developed products and services that impact millions of users worldwide, while generating millions of dollars in revenue. We have worked with Fortune 500 corporations and large government entities in health care, supply chain management, financial services and life sciences. We apply a structured methodology and holistic approach to AI transformation and a broad spectrum of technical services centered in the harnessing of people, science and technology.What you’ll be doingDesigning and building synthetic data generation pipelines that produce high-fidelity, statistically representative datasets modeled on sensitive, controlled-access data sources — without exposing real participant-level information. Your synthetic datasets will let cross-functional teams prototype, test, and red-team AI/agentic systems against realistic data conditions before those systems ever touch production, controlled-access repositories.Working with Mind MovesYou’ll collaborate with the Principal Investigator/Technical Lead, Biomedical Privacy & Gateway Engineer, Red-Team/Adversarial Security Lead, Cybersecurity Engineer, and the broader Mind Moves project team — while working closely with institutional stakeholders and Data Access Committees at NIH. This role offers the chance to:Make a measurable impact on how safely AI systems handling sensitive genomic and health data can be developed and tested.Gain hands-on experience with privacy-preserving data generation for frontier AI/ML pipelines.Operate at the intersection of data engineering, privacy, and federal data governance in a creative, mission-driven environment.The contract will include:Design and build synthetic data generation pipelines (statistical modeling, generative models, or agent-based simulation) that replicate the structure and statistical properties of controlled-access datasetsGenerate realistic synthetic test/dev datasets (e.g., genomic sequence-style data modeled on dbGaP) to support prototyping, MVP development, and red-team testing without exposing real participant-level dataApply and tune privacy-preserving techniques, including differential privacy noise injection, to bound re-identification and membership-inference riskValidate synthetic data fidelity — statistical distribution matching, utility-preservation testing, and downstream model performance parity against real data benchmarksAssess and document residual privacy risk (e.g., re-identification, linkage, membership inference) for each synthetic dataset releaseCollaborate with the Red-Team/Adversarial Security Lead and Biomedical Privacy & Gateway Engineer to supply adversarial and edge-case synthetic scenarios for testingContribute methodology documentation and data provenance records for review by institutional Data Access Committees (DACs)Collaborate with cross-functional teams across federal agencies and supporting partnersContractor has the following skills/background:Demonstrated experience building synthetic data generation pipelines using statistical modeling, generative models (e.g., GANs, VAEs, diffusion), or agent-based simulationExperience engineering and transforming large-scale genomic formats (e.g., multi-sample VCFs, PLINK binary sets, Hail MatrixTables) alongside complex, longitudinal clinical and phenotypic data.Advanced proficiency in Python and SQL, with hands-on experience using synthetic data, privacy, and scientific libraries (e.g., Opacus, Diffprivlib, PyTorch, Hail, Polars, or Spark).Data engineering and ETL skills — pipeline design, schema modeling, API integrationKnowledge of AI/data governance frameworks such as NIST AI RMF and federal (NIH/HHS) compliance requirements preferredBackground or experience in biomedical, bioinformatics, or life sciences preferredExperience taking synthetic data pipelines from proof of concept through to production-grade, reusable toolingStrong communication skills; ability to document methodology and risk assessments for non-technical and institutional stakeholdersU.S. work authorization for 1099 contracting; Mind Moves cannot offer sponsorship for this contractMust be eligible to obtain and maintain a federal public trust credential / government badge.Proposal DetailsCompensation range: $100-130/hrEstimated contract duration: September - DecemberContract hours: 20-25 hours/weekWork locale: Remote in the US (EST time zone preferred)Read more about Mind Moves:
https://mindmoves.co/Timeline and ProcessProposals are accepted on a rolling basis until the position is filled. The contract bid process will include 3 interviews and the invitation to complete a technical exercise.
2 days ago