Staff Software Engineer - Clinical AI Platform

About OrthoAI OrthoAI is building AI-driven decision intelligence for dentistry. Our initial focus is turning multimodal clinical information into structured, explainable support for high-friction authorization workflows. We are building from Abu Dhabi for the UAE and wider GCC, with clinical validity, traceability, and user safety designed into the product from the start. The role Own the production contracts and controls between multimodal data ingestion, model services, structured clinical outputs, reasoning, human review, external integration, and field feedback. You will ship hands-on while raising the engineering bar through reusable systems, reviews, debugging practice, and mentoring. What success looks like • Within 60 days, map the first workflow slice and define service/data contracts, security boundaries, lineage, tests, observability, and deployment design. • Within 120 days, ship the controlled end-to-end slice with versioned models, traceable structured output, human review, and a dependable integration surface. • Within eight months, harden reliability, access control, audit logging, rollback, incident response, performance, and cost visibility. • Within 12 months, generalize the platform for additional workflows and show a measurable lift in team delivery and defect prevention. Key responsibilities • Design and build backend services, APIs, asynchronous workflows, and data contracts for the clinical AI platform. • Integrate versioned ML and reasoning components into observable, testable production workflows. • Build for provenance, secure access, human review, auditability, rollback, and predictable failure. • Own production operability and lead evidence-based debugging of complex incidents. • Partner with research, clinical quality, product, and commercial teams to ship end-to-end outcomes. • Mentor engineers and improve technical decisions, code review, testing, and reusable platform patterns. Required qualifications Education or equivalent evidence MSc/Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent professional evidence. A degree is not required when shipped systems demonstrate the necessary depth. Experience expected • Typically 8+ years building production software, including substantial backend, platform, or distributed-systems ownership. • At least 3 years owning architecture or technical direction across a meaningful product or platform surface. • Experience integrating ML models or computational services into reliable, observable production workflows. • Experience mentoring engineers and improving team-level quality, delivery, debugging, or system design. Technical and functional capability • Designed, shipped, and operated production backend or platform systems under meaningful reliability and data constraints. • Strong system design and implementation in a modern stack; demonstrated depth matters more than a specific language. • Integrated versioned ML models or other computational services into observable production workflows. • Depth across APIs, data modeling, asynchronous systems, access control, testing, CI/CD, observability, and debugging. • Raised engineering standards through reusable systems, reviews, mentoring, and written decisions. Preferred qualifications • Healthcare, insurance, medical AI, imaging, or another regulated/high-consequence domain. • MLOps, model registry, lineage, drift monitoring, GPU inference, or multimodal pipelines. • Workflow/rules engines, structured document generation, enterprise integration, or multi-tenant cloud security. Evidence that will stand out • A production system you designed and operated, including trade-offs, failure modes, and later design changes. • A debugging or incident example covering signals, containment, root cause, and verified prevention. • A reusable platform capability that reduced repeated integration work or improved delivery reliability. How we work at OrthoAI • Outcome ownership: one accountable owner, clear interfaces, and visible follow-through. • Believability by domain: ideas are weighted by relevant demonstrated success and causal reasoning, not titles. • Written learning: important decisions, misses, root causes, and machine changes are recorded. • Cross-disciplinary rigor: clinical, scientific, engineering, commercial, and operational evidence must connect. • Human accountability: AI supports decisions; accountable people make and own them. Our values are a gate Results do not compensate for a values breach. We look for demonstrated behavior consistent with: • Truth over comfort: surface inconvenient facts and change your view when evidence changes. • Ownership without excuse: take outcomes end to end, including dependencies and recovery. • Evidence over opinion: distinguish observation, inference, and hypothesis. • Patient and user safety is inviolable: stop and escalate when a safety boundary is at risk. • Speed with rigor: shorten feedback loops without removing the controls the risk requires. What we offer

8+ years building production software, including substantial backend, platform, or distributed-systems ownership; at least 3 years owning architecture or technical direction across a meaningful product or platform surface; experience integrating ML models or computational services into reliable, observable production workflows; experience mentoring engineers and improving team-level quality, delivery, debugging, or system design; MSc/Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent evidence (a degree is not required if shipped systems demonstrate the necessary depth).

Design and build backend services, APIs, asynchronous workflows, and data contracts for the clinical AI platform; integrate versioned ML and reasoning components into observable, testable production workflows; build for provenance, secure access, human review, auditability, rollback, and predictable failure; own production operability and lead evidence-based debugging of complex incidents; partner with research, clinical quality, product, and commercial teams to ship end-to-end outcomes; mentor engineers and improve technical decisions, code review, testing, and reusable platform patterns.