Backend Engineer

Cadence ABA builds AI-native clinical software for ABA therapy teams — capturing session data by voice, structuring it against clinical targets, and drafting billable session notes so clinicians can stay present with the child instead of the tablet. We're looking for a Backend Engineer to help build and scale the systems that power this: real-time data capture, agentic processing pipelines, and the compliant infrastructure that a clinical record demands.

What you'll do

• Design, build, and maintain backend services and APIs that support real-time voice capture, trial data processing, and note generation
• Work with clinical and product teams to model ABA data structures (trials, targets, mastery criteria, phase changes) accurately and reliably
• Build and maintain data pipelines that connect captured session data to structured records, graphs, and drafted documentation
• Implement and uphold strong security, access control, and audit-logging practices appropriate for a HIPAA-compliant, BAA-backed system
• Optimize backend performance and reliability as usage scales across clinics and providers
• Collaborate with frontend, agent/ML, and clinical teams to ship features end to end

What we're looking for

• Professional experience building and operating backend services in a production environment
• Strong proficiency in at least one backend language (e.g., Python, Go, Java, Node.js, or similar)
• Solid understanding of relational databases, API design, and distributed systems fundamentals
• Experience building systems with strict data integrity, security, or compliance requirements
• Comfort working in a small, fast-moving team where you own features from design through deployment
• Clear communication skills, especially when translating clinical or domain requirements into technical design

Nice to have

• Experience in healthcare tech, especially systems handling PHI or HIPAA-regulated data
• Experience with event-driven architectures, audit logging, or role-based access control systems
• Familiarity with voice/speech processing pipelines or applied ML systems in production