Job Description
Job Description
Staff Backend Engineer, AI Infrastructure
Location: Not specified
Employment Type: Full-time
Experience: 8+ years
Focus: Distributed Systems, AI Infrastructure, Backend Engineering, Media Processing, Reliability
About Our Client
Our client is building AI-powered healthcare technology that transforms how clinicians capture, process, and document patient interactions.
Their Ambient AI products use speech processing, machine learning, and large language models to turn clinical conversations into structured, actionable information.
The company is looking for an experienced backend infrastructure engineer to build and scale the systems that power these AI workflows across mobile and web applications.
About the Role
Our client is hiring a Staff Backend Engineer, AI Infrastructure to design, build, and operate the distributed systems behind its Ambient AI products.
This is a hands-on engineering role focused on reliability, scalability, observability, and orchestration of long-running AI inference workflows.
You'll own critical backend infrastructure supporting audio ingestion, transcription, speaker diarization, LLM processing, clinical data extraction, and summarization.
The ideal candidate has experience building distributed systems at scale, understands how to operate compute-heavy workloads in production, and has a track record of making systems measurably more reliable.
What You'll Do
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Design and operate scalable backend systems supporting AI-powered healthcare applications.
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Build infrastructure for long-form audio ingestion, media processing, and real-time and asynchronous streaming.
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Own orchestration for multi-stage AI inference workflows, including transcription, diarization, LLMs, clinical extraction, and summarization.
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Build reliable scheduling, queueing, retries, timeouts, dead-letter handling, idempotency, and replay systems.
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Improve system resilience through graceful degradation and recovery mechanisms.
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Support mobile and web workflows operating under poor connectivity, unstable networks, and offline conditions.
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Define and maintain service-level objectives (SLOs) for critical production workflows.
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Build observability through structured logging, metrics, distributed tracing, dashboards, and alerting.
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Lead incident response, root-cause analysis, and long-term reliability improvements.
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Design and improve APIs, data models, cloud infrastructure, and deployment systems.
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Balance infrastructure cost, latency, reliability, and model quality.
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Mentor other engineers and establish standards for distributed systems and operational readiness.
What We're Looking For
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8+ years of backend or infrastructure engineering experience (7+ for exceptional candidates).
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Experience designing, building, and operating production distributed systems at scale.
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Strong understanding of asynchronous processing, message queues, and workflow orchestration.
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Experience building reliable systems for long-running, compute-intensive workloads.
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Strong knowledge of retries, idempotency, timeouts, dead-letter queues, and fault tolerance.
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Experience designing production APIs, backend services, and cloud infrastructure.
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Deep understanding of observability, monitoring, distributed tracing, and incident response.
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Demonstrated ability to improve system reliability, scalability, and performance.
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Experience owning production systems end-to-end, including operational support.
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Ability to make architecture decisions in fast-moving, ambiguous environments.
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Strong technical leadership and ability to mentor engineers.
Bonus Experience
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Current or previous Staff Engineer, Tech Lead, or equivalent technical leadership experience.
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Experience building audio, speech, media processing, or real-time streaming infrastructure.
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Experience orchestrating multi-stage AI or ML inference pipelines.
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Experience with mobile backend systems and unreliable network environments.
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Experience building AI applications used in healthcare or clinical settings.
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Background combining healthcare technology experience with large-scale engineering at a major technology company.
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Familiarity with EHR systems, clinical workflows, or enterprise healthcare infrastructure.
Why This Opportunity
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Own critical infrastructure behind production AI applications used in healthcare.
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Solve challenging distributed systems problems involving audio, streaming, and multi-stage AI inference.
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Build systems where reliability directly affects clinician workflows.
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Work across backend infrastructure, AI, mobile, and product engineering.
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Influence architectural decisions and operational engineering standards.
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Help scale AI-powered healthcare applications through better reliability, observability, and infrastructure.
Ideal Candidate Profile
The ideal candidate is a Staff-level Backend or Infrastructure Engineer who has built and operated complex distributed systems in production.
They understand how to orchestrate long-running workloads, design fault-tolerant systems, debug failures across distributed services, and make infrastructure measurably more reliable.
They are deeply hands-on, comfortable owning production reliability, and excited to build infrastructure that supports sophisticated AI applications in healthcare.