Top Tech Jobs & Startup Jobs

Lead architecture and technical strategy for a fullstack digital pathology platform. Design and deliver complex cross-team initiatives, write production code, uphold engineering standards, and improve testing, observability, security, and performance. Collaborate with product, design, machine learning, infrastructure, QA, and regulatory teams in a medical device environment. Mentor engineers, influence technical decisions, and communicate with customers and nontechnical stakeholders.

Implement digital diagnostics solutions for pathology labs, academic medical centers, hospital systems, and clinical trial customers. Serve as the primary technical contact during implementation, coordinate with customer IT and internal development teams, manage schedules and risks, resolve issues, and provide application expertise through on-call support. The role requires SaaS implementation experience, strong customer communication, analytical rigor, and familiarity with HL7 and laboratory information systems.

Develop and maintain full-stack features for a SaaS platform supporting clinical trial workflows. Responsibilities include Python-based application development, frontend collaboration, architecture, testing, code review, refactoring, reliability, data integrity, and regulatory compliance. The engineer will collaborate with product, design, machine learning, infrastructure, clinical, QA, and regulatory teams while contributing to a distributed engineering organization.

Lead the MLOps engineering team and roadmap supporting large-scale machine learning training and inference. Oversee cloud infrastructure, Kubernetes, distributed systems, observability, data pipelines, and inference modernization. Manage engineers, budgets, and cloud costs while partnering across machine learning, data science, product engineering, and infrastructure. Drive platform scalability, build-versus-buy assessments, stack refreshes, production reliability, and adoption across the organization.

Leads the strategic vision, roadmap, and end-to-end lifecycle for AI/ML algorithm products. Builds business cases, defines requirements, oversees prototypes and deployments, and scales product portfolios. Partners with engineering, data science, pathology, clinical, regulatory, quality, design, and business teams. Supports validation strategies, clinical trials, regulatory pathways, and commercialization of healthcare AI products.

Lead complex product development programs through planning, execution, and delivery. Manage project plans, budgets, timelines, risks, actions, documentation, stakeholder reporting, and cross-functional meetings. Resolve issues, drive accountability and decisions, coordinate technical and product teams, and recommend process improvements focused on efficiency and delivery.

Design, build, and maintain full‑stack features for a clinical SaaS platform. Drive architecture, collaborate with product, ML, infra, and regulatory teams, uphold engineering standards, mentor peers, and ensure reliability, data integrity, and compliance for clinical trial workflows.

Design and scale machine learning infrastructure across AWS and on-premises environments. Lead MLOps architecture, automation, CI/CD, deployment, monitoring, and reproducible ML workflows using Kubernetes, Airflow, and related tools. Collaborate with engineering and data science teams, evaluate MLOps technologies, ensure security and compliance, conduct design reviews, enforce engineering standards, and mentor junior engineers.

Build and maintain automated UI, API, and integration test suites; contribute to test strategies, code reviews, debugging, manual testing, CI/CD pipelines, and documentation. Help manage AWS and Kubernetes test infrastructure, improve test reliability, and investigate failures across software, data, and infrastructure layers. Collaborate with engineering, product, QA, and business teams in a distributed healthcare technology environment.

Designs, builds, and maintains backend APIs and data pipelines supporting clinical workflows and real-time data exchange. Drives platform architecture, code quality, testing, reliability, data integrity, compliance, and inspection readiness. Collaborates with product, design, machine learning, infrastructure, clinical, QA, and regulatory teams while mentoring engineers and contributing to a SaaS healthcare platform.