Healthcare AI Platform

Healthcare AI Diagnostics

Bayesian neural networks with calibrated uncertainty quantification for clinical decision support. Designed for regulated deployment where human authority over diagnostic decisions is non-negotiable.

Profile A: Decision Support Human-in-the-Loop HIPAA-Aligned Audit Trail

Platform Capabilities

This system is designed exclusively for clinical decision support. It does not make autonomous diagnostic decisions. All outputs include uncertainty quantification, and low-confidence predictions are escalated for human review.

Uncertainty Quantification

Uncertainty Quantification

Every prediction includes explicit confidence estimates using Bayesian inference and conformal prediction methods. Clinicians see not just a result, but how confident the system is in that result.

Escalation Workflow

Escalation Workflow

Low-confidence outputs are automatically flagged for human review. Configurable thresholds allow clinical teams to set escalation sensitivity appropriate to their use case and risk tolerance.

Audit Trail

Audit Trail

Every prediction, escalation, and human override is logged with full provenance. Decision records are retained for regulatory review, legal defensibility, and quality improvement programs.

Human Override

Human Override

Licensed clinicians retain full authority to accept, modify, or reject any system output. Override events are logged but never restricted. The system advises; humans decide.

✓ Uncertainty Quantification ✓ Human Override ✓ Escalation Workflow ✓ Full Audit Trail ✓ HIPAA-Aligned Architecture ✓ Clinical Governance

This system is not intended for autonomous diagnosis. Final diagnostic decisions remain with licensed clinicians at all times.

Deployment Profile A: Decision Support (Lowest Risk)

Human-in-the-loop required. AI provides recommendations only. Uncertainty explicitly surfaced. Full audit logs retained.

View Deployment Profile

Technical Architecture

Built for clinical-grade reliability and regulatory readiness.

Technical Architecture
  • Bayesian Deep Learning : Probabilistic inference with variational methods for calibrated uncertainty
  • Conformal Prediction : Distribution-free coverage guarantees for prediction intervals
  • Medical Imaging Pipeline : DICOM-native processing with sub-50ms inference latency
  • Interoperability : HL7 FHIR integration for EHR connectivity and clinical workflow embedding
  • Privacy Architecture : HIPAA-aligned data handling, access controls, and encryption at rest and in transit
  • Reproducibility : Versioned models, deterministic inference, and reproducible audit records

Healthcare AI Team | Selective Hiring (Planned)

Opening Selectively

This system is operational. 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 ML infrastructure.

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
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
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
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.

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
  • Author regulatory submissions, clinical study reports, and peer-reviewed manuscripts
  • Establish post-market surveillance frameworks and real-world evidence generation programs
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
  • Proven success navigating IRB approvals and multi-institutional collaborations
Product Strategy & Commercialization

Director of Product | Healthcare AI Platforms

Define strategic vision and execution roadmap for enterprise Healthcare AI products. Translate clinical needs into technical requirements and manage go-to-market strategies for hospital and health system buyers.

Core Responsibilities
  • Define product vision, strategy, and multi-year roadmap based on clinical needs and market analysis
  • Translate clinical workflows and EHR integration requirements into technical specifications
  • Lead go-to-market strategy including pricing, positioning, and customer success frameworks
  • Establish KPIs: diagnostic accuracy, time-to-diagnosis, physician adoption, patient outcome metrics
  • Manage regulatory strategy and evidence generation to support marketing claims
Required Qualifications
  • 8+ years product management in healthcare technology, medical devices, or health IT
  • Proven track record launching B2B healthcare products to hospital and IDN buyers
  • Deep understanding of clinical workflows, EHR systems, and interoperability standards
  • Experience managing AI/ML products or Software as a Medical Device (SaMD)
  • Demonstrated P&L ownership and commercial success in regulated healthcare markets

Why Join TeraSystemsAI Healthcare AI

Build AI systems that directly impact patient outcomes. 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.

Location

Remote-first, US-based preferred

Compensation

Competitive salary, equity participation, comprehensive benefits

Culture

Research-driven, publication-encouraged, high-autonomy environment

Express Interest in Roles

Hiring timelines are contingent upon clinical partnerships, regulatory milestones, and capital deployment. This page will be updated as positions open.