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About This Role
At Commure, we're building the AI Operating System for healthcare, the foundation that defines how care is delivered, documented, and financed. Our platform spans the full care journey: Ambient AI and Dictation eliminating documentation burden at the point of care, intelligent Agents automating patient and revenue workflows, and autonomous RCM processing billions in claims, all on a single AI-native platform integrated with 60+ EHRs.
Healthcare carries a $1 trillion administrative burden and we're at the center of transforming it. Today, 500,000+ clinicians across 500+ healthcare organizations nationwide trust Commure to handle $25B+ in annual claims and support over 200 million patient interactions. Our latest $70M raise at a $7B valuation reflects the confidence the market has placed in this mission. We've also been named to the Fortune Future 50 list and the 2026 AI Breakthrough Awards for “Overall NLP Company of the Year.”
Our team works directly alongside clinicians, not through layers of process, which means the gap between what you build and its impact on patient care is immediate. We move fast, deploy daily, and take full ownership from early thinking to production. If you're energized by hard problems, high stakes, and a team that holds itself to a high bar, you'll find your people here.
The future of healthcare is being built right now. Come deliver this transformation.
About the Role
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.
What You’ll Do
- 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.
What You Have
- 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.
Nice to Haves
- Experience building speech-to-text, diarization, transcription, or other audio-based machine-learning workflows.
- Experience orchestrating large language models or multi-model inference pipelines in production.
- Familiarity with workflow orchestration systems such as Temporal or similar durable execution platforms.
- Experience with event-streaming and messaging technologies such as Kafka, Pub/Sub, SQS, or similar systems.
- Experience with model routing, inference gateways, rate limiting, batching, caching, or GPU-backed workloads.
- Experience building internal tooling for workflow inspection, replay, evaluation, or model debugging.
- Experience defining SLOs, managing error budgets, designing alerts, and leading production incident response.
- Familiarity with distributed tracing and observability platforms such as Grafana, OpenTelemetry, Sentry, or similar tools.
- Experience with containers, Kubernetes, infrastructure as code, and cloud-native deployment systems.
- Experience optimizing systems for throughput, tail latency, infrastructure cost, and workload isolation.
- Experience with offline clients, background synchronization, or systems that reconcile delayed and duplicated events.
- Exposure to healthcare, HIPAA, SOC 2, encryption, privacy, security, or other regulated environments.
- Experience supporting AI-native, real-time, or agentic product experiences.
Why Join
- Build the backend foundation for an AI healthcare product used in real clinical workflows.
- Solve challenging systems problems involving distributed orchestration, and production AI.
- Make reliability improvements that clinicians experience directly through faster, safer, and more dependable workflows.
- Shape how inference systems are orchestrated, observed, recovered, and scaled in production.
- Work closely with product and AI teams while maintaining deep ownership of backend architecture and infrastructure.
- Help define the operational and engineering standards for a rapidly growing platform.
- Join at a stage where core systems, technical strategy, and engineering culture are still highly shapeable.
- Grow into broader backend, infrastructure, or technical leadership as the team and product expand.
Please be aware that all official communication from us will come exclusively from email addresses ending in @commure.com. Any emails from other domains are not affiliated with our organization.
Employees will act in accordance with the organization’s information security policies, to include but not limited to protecting assets from unauthorized access, disclosure, modification, destruction or interference nor execute particular security processes or activities. Employees will report to the information security office any confirmed or potential events or other risks to the organization. Employees will be required to attest to these requirements upon hire and on an annual basis.
Compensation Range: $210K - $275K
Salary Context
This $210K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Commure, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($242K) sits 14% above the category median. Disclosed range: $210K to $275K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Commure AI Hiring
Commure has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Mountain View, CA, US, New York, NY. Compensation range: $180K - $275K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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