Clinically-validated Bayesian neural networks with full uncertainty quantification for life-critical medical decisions. Building AI systems that physicians trust and patients deserve.
Healthcare AI | Hiring Planned
Opening Selectively
We are actively building this system. Senior-level positions will open as deployments scale and clinical validation studies progress. We seek world-class talent to build AI systems that physicians trust and patients deserve.
Senior Role Profiles
Machine Learning Engineering
Principal Machine Learning Engineer | Healthcare AI Systems
Architect and deploy production-grade Bayesian neural networks for life-critical clinical decision support. Lead technical initiatives in uncertainty quantification, conformal prediction, and HIPAA-compliant machine learning infrastructure. Drive research contributions to top-tier conferences and medical journals while mentoring engineering teams.
Core Responsibilities
Design Bayesian deep learning architectures for diagnostic imaging and clinical workflows
Develop calibrated uncertainty quantification frameworks using conformal prediction and variational inference
Build reproducible, auditable ML pipelines meeting regulatory and clinical validation standards
Partner with clinical research teams to ensure model outputs align with physician decision-making
Author technical publications demonstrating clinical efficacy and safety profiles
Establish engineering best practices for medical AI systems in regulated environments
Required Qualifications
PhD in Computer Science, Statistics, Biomedical Engineering, or equivalent with ML specialization
5+ years production ML experience; healthcare or regulated domains strongly preferred
Expert-level proficiency in PyTorch or TensorFlow, Bayesian methods, uncertainty quantification
Proven track record deploying ML systems in high-stakes, safety-critical environments
First-author publications in NeurIPS, ICML, ICLR, or leading medical AI journals
Demonstrated ability to translate research innovations into production systems
Preferred Expertise
Medical imaging standards: DICOM, HL7 FHIR, clinical workflow integration
Regulatory pathways: FDA premarket submissions, CE marking for SaMD
Open-source contributions to major ML frameworks or healthcare AI libraries
Cloud infrastructure expertise: AWS, Azure, GCP with HIPAA compliance architecture
Clinical Research & Validation
Senior Clinical AI Research Scientist | Evidence Generation
Lead clinical validation programs bridging machine learning research and medical evidence standards. Design rigorous protocols, manage multi-site studies, and generate the statistical evidence required for regulatory approval and clinical adoption. Interface with IRBs, hospital systems, and regulatory bodies to demonstrate safety and efficacy.
Core Responsibilities
Design and execute clinical validation protocols meeting FDA, EMA, and institutional standards
Lead multi-center clinical studies with hospital partners and institutional review boards
Perform biostatistical analysis comparing AI performance to clinical ground truth and physician benchmarks
Author regulatory submissions, clinical study reports, and peer-reviewed manuscripts
Establish post-market surveillance frameworks and real-world evidence generation programs
Collaborate with KOLs and clinical advisory boards to guide research priorities
Required Qualifications
PhD in Biomedical Informatics, Clinical Epidemiology, Health Data Science, or related field
7+ years clinical research experience with AI/ML medical device validation
Deep expertise in clinical trial design, biostatistics, and regulatory science
First-author publications in NEJM, JAMA, Lancet Digital Health, or equivalent clinical AI journals
Proven success navigating IRB approvals and multi-institutional collaborations
Fluency in statistical software: R, SAS, Python for clinical data analysis
Preferred Expertise
Clinical training: MD, DO, or advanced clinical degree with patient care experience
Define strategic vision and execution roadmap for enterprise Healthcare AI products. Translate clinical needs into technical requirements, manage go-to-market strategies for hospital and health system buyers, and ensure products deliver measurable improvements in patient outcomes. Own P&L responsibility and cross-functional alignment across engineering, clinical, regulatory, and commercial teams.
Core Responsibilities
Define product vision, strategy, and multi-year roadmap based on clinical needs and market analysis
Gather requirements from key stakeholders: physicians, hospital IT, payers, and regulatory advisors
Translate clinical workflows and EHR integration requirements into technical specifications
Lead go-to-market strategy including pricing, positioning, and customer success frameworks
Why Join TeraSystemsAI Healthcare AI
Build AI systems that directly impact patient outcomes and save lives. Collaborate with world-class researchers and clinicians. Publish in top-tier venues. Deploy technology that physicians trust, regulators approve, and patients deserve. Work at the intersection of cutting-edge ML research and transformative healthcare delivery.
Hiring timelines are contingent upon clinical partnerships, regulatory milestones, and capital deployment. This page will be updated as positions open.