Top Tech Jobs & Startup Jobs in NYC, NY

Designs, builds, and evaluates agentic AI systems for healthcare workflows such as documentation, coding, denial management, and revenue cycle automation. Develops long-horizon agent behavior, evaluation frameworks, benchmarks, feedback loops, monitoring, and production safeguards. Partners with research, ML engineering, clinical, product, and engineering teams to prototype and deploy reliable, explainable, auditable AI systems using LLMs, machine learning, and agentic technologies.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Build and deliver AI-powered healthcare products using React, Node.js, Python, and modern full-stack technologies. Partner with customers, designers, researchers, and product teams to understand workflow needs, design intuitive AI experiences, and own features from ideation through deployment, optimization, and scaling. The role requires strong product instincts, end-to-end software engineering ownership, and experience building production-grade applications.

Build and scale AI-native healthcare products through full-stack applications, backend services, data pipelines, and AI systems. Design, test, deploy, and operate production-ready software, run A/B tests, automate manual workflows, and collaborate with product, operations, and AI/ML teams. The role requires end-to-end ownership of scalable, secure, reliable systems and features, with a focus on performance, data integrity, clean code, and measurable business outcomes.

Conduct applied AI research focused on explainability, reinforcement learning, retrieval-augmented generation, and long-context retrieval. Design, train, and evaluate machine learning models using large-scale healthcare data and distributed infrastructure. Build research prototypes and experimentation frameworks, validate measurable impact, and collaborate with ML Engineering, MLOps, and Product teams to transition innovations into production.

Build and operate scalable data pipelines, backend services, APIs, and infrastructure powering AI-driven healthcare applications. Design distributed data systems, ETL workflows, streaming platforms, and cloud-native architectures while partnering with AI, machine learning, and product teams. Own production reliability, observability, security, automation, and on-call support for business-critical systems.

Leads AI/ML, data, and full-stack engineering teams building scalable AI-native healthcare products. Responsibilities include hiring and mentoring engineers, defining team structures, partnering with product and AI leaders on roadmaps, driving 0-to-1 innovation, implementing modern AI/ML technologies, and championing agile, DevOps, automated testing, and continuous improvement practices.

Build and maintain large-scale healthcare data platform software using HL7/FHIR, Apache Spark, Python, Scala, Databricks, and cloud technologies. Shape technical roadmaps, architecture, and implementation approaches; collaborate with product and business stakeholders; maintain CI/CD and staging environments; and mentor junior engineers. The role requires deep healthcare data expertise, technical leadership, and experience with modern software engineering and integration practices.

Own the production runtime for Phare’s machine learning stack by deploying, serving, and scaling models across GPU inference endpoints and batch or streaming workflows. Build progressive delivery pipelines, automated rollbacks, observability, alerting, and incident response processes. Manage model registries, retraining lifecycles, versioning, data lineage, security controls, and reproducible releases using Kubernetes, Terraform, Docker, and CI/CD.

Build and operate agentic AI systems for healthcare workflows including clinical documentation, coding, denial management, appeals, and revenue cycle automation. The role owns agent design, context construction, retrieval, tool use, evaluation, failure analysis, deployment, monitoring, and continuous improvement. It requires turning complex clinical workflows into reliable, auditable production systems while partnering with research and ML engineering teams on model selection, fine-tuning, benchmarks, and AI infrastructure.