Staff Backend Engineer, Ambient AI

About the job

Responsibilities

• Design, build, and operate backend systems for Ambient AI products across mobile and web.
• Build and evolve inference workflows coordinating transcription, diarization, language models, clinical extraction, summarization, and other AI capabilities.
• Develop orchestration systems for long-running, multi-stage workflows, including scheduling, queueing, prioritization, retries, fallbacks, idempotency, recovery, model routing, versioning, progress tracking, and graceful degradation.
• Improve the reliability and scalability of services handling variable, compute-intensive workloads.
• Define and maintain service-level objectives for critical workflows.
• Build strong observability across services and pipelines through logging, metrics, tracing, dashboards, alerting, and diagnostic tooling.
• Lead incident response and post-incident analysis, implementing improvements based on operational lessons.
• Identify and eliminate systemic failure patterns through better abstractions, automation, testing, and architecture.
• Improve cloud infrastructure, deployment systems, capacity planning, and operational tooling.
• Design APIs and data models for safe and predictable interaction with long-running workflows.
• Build tools to simplify inspection, testing, replay, evaluation, and debugging of inference pipelines.
• Balance reliability, latency, quality, and infrastructure cost for AI and media-processing workloads.
• Partner with mobile, product, AI, security, and clinical teams to productionize new capabilities.
• Mentor other engineers and establish best practices for distributed systems, observability, operational readiness, and backend architecture.

Requirements

• 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.
• Strong understanding of distributed-systems concepts like 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.
• Proven track record of improving system reliability, observability, scalability, or operational efficiency.
• Strong debugging skills across application, infrastructure, data, and external dependency boundaries.
• 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.
• An ownership mindset and bias for action.