ExaCare AI

Full-stack Engineer

• Own product features end-to-end, from workflow problem through technical design, implementation, verification, production rollout, and operational readiness
• Reason across software architecture, data models, integrations, and reusable product patterns, grounded in customer, operator, and implementation-team experience
• Design and deliver reliable full-stack features supporting complex healthcare workflows at scale using TypeScript, PostgreSQL, and AWS cloud-native tooling
• Maintain quality across testing, observability, deployment safety, and incident response
• Identify and create reusable abstractions, frameworks, and AI-powered patterns
• Collaborate with product, operations, platform, ML, design, clinical, customer-facing, and engineering teams
• Shape technical direction across multiple product areas and influence architectural decisions
• Raise engineering standards and improve engineering velocity and product consistency

Staff Software Engineer

Platform Engineer

• Build and scale core platform infrastructure
• Design and maintain reliable backend services, cloud infrastructure, CI/CD pipelines, and internal platform systems
• Develop and operate highly available distributed systems using TypeScript, PostgreSQL, AWS, and modern infrastructure tooling
• Ensure systems are secure, observable, fault-tolerant, and performant
• Create internal tooling, deployment automation, developer workflows, feature flag systems, and platform abstractions
• Establish best practices around monitoring, incident response, infrastructure-as-code, testing, deployment safety, and system resiliency
• Participate in production incident triage and continuous reliability improvements
• Design APIs, background processing systems, event-driven workflows, queues, and integrations supporting complex healthcare operations at scale
• Evaluate and implement modern infrastructure patterns, AI-powered developer workflows, and scalable cloud-native technologies
• Collaborate with product, engineering, and operations teams to translate business needs into robust technical systems and platform capabilities

Senior Platform Engineer

Software Engineer

• Take ownership of well-defined features and improvements from understanding requirements through implementation, testing, rollout, and support
• Learn how software architecture, data models, APIs, and integrations connect to real healthcare workflows
• Develop full-stack features using TypeScript, PostgreSQL, and modern cloud tooling on AWS
• Write tests, participate in code reviews, and follow observability and deployment practices
• Use shared components and established patterns, improve existing systems, and simplify code and workflows
• Collaborate with engineering, product, operations, platform, and ML teammates to solve customer problems
• Contribute ideas, participate in technical discussions, and build skills through feedback and mentorship

• Own product features end-to-end, from understanding workflow problems through technical design, implementation, testing, production rollout, and ongoing support
• Connect software architecture, data models, APIs, and integrations to customer, operator, and implementation-team workflows
• Design and deliver reliable full-stack features supporting complex healthcare workflows at scale
• Use TypeScript, PostgreSQL, and modern cloud tooling on AWS
• Build testing, observability, and safe deployment practices into engineering work
• Proactively address rollout risks and help resolve production issues
• Create shared components, simplify common workflows, and improve integrations or AI-powered capabilities
• Collaborate with product, operations, platform, ML, and engineering partners
• Contribute to architectural decisions and technical discussions
• Support fellow engineers through code reviews, collaboration, and mentorship

Senior Software Engineer

Machine Learning Engineer

• Build and maintain workflows and infrastructure supporting the end-to-end ML lifecycle
• Partner with researchers and ML practitioners to productionize models and enable faster iteration
• Design, build, and improve data and training pipelines
• Improve data processing, annotation workflows, and ML system efficiency
• Deploy and maintain background systems supporting model training and inference
• Build tooling and processes for monitoring model performance, system reliability, and operational health
• Improve scalability, observability, and reproducibility of ML systems
• Optimize ML infrastructure for speed, reliability, and cost-efficiency
• Identify bottlenecks and automate or streamline manual ML workflow processes
• Establish best practices around ML operations, deployment, and system performance