Staff Backend Engineer (Ambient AI)
Posted Updated
- 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
- This role is ideal for an engineer who enjoys making complex systems dependable
- You think carefully about failure modes, idempotency, backpressure, data integrity, latency, and operational simplicity
- You also understand that infrastructure exists to serve the product: the systems you build must ultimately create a fast, reliable, and trustworthy experience for clinicians
- 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, including:
- Scheduling, queueing, and workload prioritization
- Retries, timeouts, fallbacks, and dead-letter handling
- Idempotency, replay, and safe workflow recovery
- Model routing, versioning, and configuration
- Progress tracking and user-visible workflow state
- Graceful degradation when dependencies are unavailable
- 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
- 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
