Staff Backend Engineer, Athelas

• 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. You identify important risks, create clarity, and drive ambiguous infrastructure work through completion

Desirable

• 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