Staff Backend Engineer (Ambient AI)

Join our team as a Staff Backend Engineer, where you'll play a crucial role in building and operating our next-generation ambient AI product suite for healthcare. You'll work on a distributed system responsible for processing audio, orchestrating AI models, and producing clinical outputs. This role is ideal for engineers who enjoy making complex systems dependable and have a strong understanding of distributed systems concepts.

- Design, build, and operate backend systems that power Ambient AI products across mobile and web, including inference workflows and orchestration systems. - Improve the reliability and scalability of services operating under variable, compute-intensive workloads, and define and maintain service-level objectives for critical workflows. - Lead incident response and post-incident analysis for important production failures, turning operational lessons into lasting improvements.

- A strong understanding of distributed-systems concepts such as idempotency, consistency, concurrency, backpressure, retries, failure isolation, and eventual completion - Experience operating services in a cloud environment, including deployment, monitoring, scaling, and incident response - An ownership mindset and bias for action. You identify important risks, create clarity, and drive ambiguous infrastructure work through completion - Experience designing, building, and operating production distributed systems - 8+ years of professional backend or infrastructure engineering experience - Experience designing APIs, service boundaries, and data models for complex product workflows - Experience with asynchronous processing, message queues, event-driven systems, or durable workflow execution - Strong debugging skills across application, infrastructure, data, and external dependency boundaries - An ability to reason about both real-time and long-running workloads with different latency and durability requirements - A track record of improving system reliability, observability, scalability, or operational efficiency - Comfort working across backend, infrastructure, product, and AI systems rather than within a narrowly defined layer - Strong proficiency in at least one modern backend programming language - Exposure to healthcare, HIPAA, SOC 2, encryption, privacy, security, or other regulated environments - Experience supporting AI-native, real-time, or agentic product experiences - Experience with offline clients, background synchronization, or systems that reconcile delayed and duplicated events - Experience optimizing systems for throughput, tail latency, infrastructure cost, and workload isolation - Experience with containers, Kubernetes, infrastructure as code, and cloud-native deployment systems - Familiarity with distributed tracing and observability platforms such as Grafana, OpenTelemetry, Sentry, or similar tools - Experience defining SLOs, managing error budgets, designing alerts, and leading production incident response - Experience building internal tooling for workflow inspection, replay, evaluation, or model debugging - Experience with model routing, inference gateways, rate limiting, batching, caching, or GPU-backed workloads - Experience with event-streaming and messaging technologies such as Kafka, Pub/Sub, SQS, or similar systems - Familiarity with workflow orchestration systems such as Temporal or similar durable execution platforms - Experience orchestrating large language models or multi-model inference pipelines in production - Experience building speech-to-text, diarization, transcription, or other audio-based machine-learning workflows