Staff Backend Engineer, Ambient AI

About the Role

We’re building a next-generation ambient AI product suite for healthcare, helping clinicians focus on patient care by automatically capturing, understanding, and structuring clinical conversations.

Behind that experience is a distributed system responsible for ingesting long-form audio, processing real-time and asynchronous streams, orchestrating multiple AI models, and reliably producing clinical outputs. These workflows must remain observable and recoverable across network interruptions, partial failures, model timeouts, and rapidly changing inference infrastructure.

We’re hiring a Staff Backend Engineer to help build and operate this foundation. You’ll work across audio ingestion, media processing, inference orchestration, distributed workflows, storage, observability, and cloud infrastructure.

What You’ll Do

• Design, build, and operate backend systems that power our Ambient AI products across mobile and web.
• Build and evolve inference workflows that coordinate transcription, diarization, language models, clinical extraction, summarization, and other AI capabilities.
• Develop orchestration systems for long-running, multi-stage workflows (scheduling, queueing, workload prioritization, retries, timeouts, fallbacks, dead-letter handling, idempotency, replay, model routing, and state tracking).
• Improve the reliability and scalability of services operating under variable, compute-intensive workloads.
• Define and maintain service-level objectives for critical workflows, including availability, processing latency, completion rates, and data durability.
• Build strong observability across services and pipelines through structured logging, metrics, distributed tracing, dashboards, alerting, and diagnostic tooling.
• Lead incident response and post-incident analysis for important production failures, turning operational lessons into lasting improvements.
• Identify systemic failure patterns and eliminate them through better abstractions, automation, testing, and architecture.
• Improve cloud infrastructure, deployment systems, capacity planning, and operational tooling as the platform scales.
• Design APIs and data models that allow mobile, web, and internal systems to interact with long-running workflows safely and predictably.
• Build tools that make inference pipelines easier to inspect, test, replay, evaluate, and debug.
• Balance reliability, latency, quality, and infrastructure cost across AI and media-processing workloads.
• Partner closely with mobile, product, AI, security, and clinical teams to turn new capabilities into production-ready workflows.
• Mentor other engineers and help establish strong practices for distributed systems, observability, operational readiness, and backend architecture.

What You Have

• 8+ years of professional backend or infrastructure engineering experience.
• Experience designing, building, and operating production distributed systems.
• Strong proficiency in at least one modern backend programming language.
• Experience with asynchronous processing, message queues, event-driven systems, or durable workflow execution.
• 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.
• Experience designing APIs, service boundaries, and data models for complex product workflows.
• A track record of improving system reliability, observability, scalability, or operational efficiency.
• 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.
• Comfort working across backend, infrastructure, product, and AI systems rather than within a narrowly defined layer.
• An ownership mindset and bias for action.

Nice to Haves

• Experience building speech-to-text, diarization, transcription, or other audio-based machine-learning workflows.
• Experience orchestrating large language models or multi-model inference pipelines in production.
• Familiarity with workflow orchestration systems such as Temporal or similar durable execution platforms.
• Experience with event-streaming and messaging technologies such as Kafka, Pub/Sub, SQS, or similar systems.
• Experience with model routing, inference gateways, rate limiting, batching, caching, or GPU-backed workloads.
• Experience building internal tooling for workflow inspection, replay, evaluation, or model debugging.
• Experience defining SLOs, managing error budgets, designing alerts, and leading production incident response.
• Familiarity with distributed tracing and observability platforms such as Grafana, OpenTelemetry, Sentry, or similar tools.
• Experience with containers, Kubernetes, infrastructure as code, and cloud-native deployment systems.
• Experience optimizing systems for throughput, tail latency, infrastructure cost, and workload isolation.
• Experience with offline clients, background synchronization, or systems that reconcile delayed and duplicated events.
• Exposure to healthcare, HIPAA, SOC 2, encryption, privacy, security, or other regulated environments.
• Experience supporting AI-native, real-time, or agentic product experiences.