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Staff Backend Engineer (Ambient AI)

Athelas

Skills

Required

  • infrastructure
  • incident response
  • cloud
  • deployment
  • debugging
  • observability
  • monitoring
  • Kafka

Preferred

  • AI
  • model routing / inference gateways / rate limiting / batching / caching / GPU-backed workloads
  • building speech-to-text
  • diarization
  • transcription
  • audio-based machine-learning workflows
  • orchestration systems
  • scalability
  • inference workflows
  • Kubernetes

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.

Responsibilities

  • 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 designing, building, and operating production distributed systems
  • 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
  • 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
  • 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

Requirements

  • 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
  • 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
  • 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
  • Familiarity with distributed tracing and observability platforms such as Grafana, OpenTelemetry, Sentry, or similar tools
  • 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

Preferred

  • Experience with model routing, inference gateways, rate limiting, batching, caching, or GPU-backed workloads
  • Experience building speech-to-text, diarization, transcription, or other audio-based machine-learning workflows

Pay

USD 210,000 - 275,000