Engineering Manager, Platform

• Lead, manage, and develop a team of platform and backend engineers
• Own execution and delivery across Platform projects
• Set priorities, break down ambiguous projects, and move projects from design through production
• Run technical planning, design reviews, and engineering reviews
• Coach senior and junior engineers and raise the team’s technical bar
• Partner with the Head of Engineering on platform strategy, architecture, hiring, and organizational priorities
• Identify and resolve execution risks
• Design and build production backend and platform systems
• Take ownership of technically difficult or high-priority projects
• Review architecture, code, infrastructure changes, and technical designs
• Debug complex production and distributed-system issues
• Make architectural decisions balancing speed, reliability, scalability, and maintainability
• Establish reusable technical patterns and standards
• Own backend platform reliability, scalability, and performance
• Design systems supporting growing clinical workloads
• Improve observability, monitoring, alerting, and incident response
• Own cloud infrastructure and improve security, cost efficiency, and operational resilience
• Build shared services and infrastructure for product engineers
• Improve developer experience, deployment workflows, testing, and engineering productivity
• Eliminate technical bottlenecks
• Partner with ML engineers and researchers to product ionize AI capabilities
• Build infrastructure for model inference, evaluation, experimentation, and data processing
• Support applied ML initiatives and scalable foundations for the ML organization
• Balance AI quality with latency, reliability, scalability, and cost
• Report directly to the Head of Engineering

• 7+ years of professional software engineering experience
• Previous experience directly managing a software engineering team
• Strong hands-on backend engineering experience and willingness to continue writing production code
• Deep experience designing and operating distributed backend systems
• Strong system design and software architecture skills
• Experience building production systems on AWS or another major cloud platform
• Experience owning production systems with meaningful scale, reliability, and availability requirements
• Strong understanding of databases, APIs, asynchronous systems, queues, caching, and distributed architectures
• Experience with infrastructure, observability, deployment systems, and production operations
• Demonstrated ability to lead complex technical projects from ambiguity through production
• Ability to effectively manage both senior and junior engineers
• Strong communication skills and ability to work across engineering, product, ML, and business teams
• Experience with Python and modern backend frameworks
• Experience with AWS services such as Lambda, ECS/EKS, SQS, RDS, DynamoDB, S3, Cloud Watch, or related technologies
• Experience operating event-driven or asynchronous distributed systems
• Experience building systems with strict latency and reliability requirements
• Experience with containers, infrastructure-as-code, and modern CI/CD systems
• Experience building data infrastructure or large-scale processing pipelines
• Experience supporting ML systems, model inference, LLM applications, or AI infrastructure
• Experience in healthcare technology or other regulated environments
• Experience with HIPAA-compliant systems and healthcare data
• Experience working in fast-moving startup environments where engineering leaders remain deeply involved in execution
• Legally authorized to work in the US
• Must answer whether visa sponsorship or transfer will be required

Demonstrates extensive experience in backend engineering, team leadership, and system design, with a strong focus on building scalable and reliable production systems. Proficient in managing complex technical projects and collaborating across engineering, product, and ML teams.

• Backend Engineering
• Team Management
• AWS Cloud Services
• System Design
• Distributed Systems

• Python
• Backend Frameworks
• Database Management
• APIs
• Asynchronous Systems
• Infrastructure-as-Code
• CI/CD Systems
• Data Processing Pipelines
• Model Inference
• Event-Driven Systems

• Strong Communication
• Team Coaching
• Project Leadership

• Healthcare Technology
• HIPAA Compliance
• Regulated Environments

• AWS Lambda
• ECS/EKS
• SQS
• RDS
• DynamoDB
• S3
• Cloud Watch