Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
San Francisco, California, USA
Open 5 months, first seen by us 155 days agoWork setup in the employer’s posting: HybridFrom the employer’s posting
Employment type in the employer’s posting: FullTimeFrom the employer’s posting
Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training
Develop and maintain low-latency ML model serving infrastructure
Optimize compute-heavy workflows and GPU utilization for ML workloads
Build a robust model API orchestration system
Define strategies with leadership for scaling infrastructure as the company grows
Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training
Develop, optimize, and maintain ML model serving infrastructure, ensuring high-performance and low-latency.
Optimize compute-heavy workflows and enhance GPU utilization for ML workloads.
Build a robust model API orchestration system
Collaborate with leadership to define and implement strategies for scaling infrastructure as the company grows
Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output.
In the employer’s words · Read in context
Open 5 monthsWe first saw this posting 155 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Day 155
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,423 similar postings that came down. Coming down is not always a hire.
4 open jobs
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What You'll Do - Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training - Develop, optimize, and maintain ML model serving infrastructure, ensuring high-performance and low-latency.
- Deep understanding of container orchestration and distributed systems architecture - Expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management - Experience developing APIs and managing distributed systems for both batch and real-time workloads
- Familiarity with GPU cluster management and CUDA optimization - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads
- Expertise with model serving frameworks such as NVIDIA Triton Server, VLLM, TRT-LLM and so on. - Expertise with ML toolchains such as PyTorch, Tensorflow or distributed training and inference libraries. - Familiarity with GPU cluster management and CUDA optimization
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.
Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.
We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh.
As an ML Infrastructure Engineer, Model Inference at Abridge, you’ll play a pivotal role in building and optimizing the core inference infrastructure that powers our machine learning models. Your work will be instrumental in enhancing the scalability, efficiency, and performance of our AI-driven solutions. You will work with our Infrastructure and Research teams to build, deploy, optimize and orchestrate across our AI models.
What You'll Do
Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training
Develop, optimize, and maintain ML model serving infrastructure, ensuring high-performance and low-latency.
Collaborate with ML and product teams to scale backend infrastructure for AI-driven products, focusing on model deployment, throughput optimization, and compute efficiency.
Optimize compute-heavy workflows and enhance GPU utilization for ML workloads.
Build a robust model API orchestration system
Collaborate with leadership to define and implement strategies for scaling infrastructure as the company grows, ensuring long-term efficiency and performance.
What You’ll Bring
5+ years of experience in building and deploying machine learning models in production environments.
Deep understanding of container orchestration and distributed systems architecture
Expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management
Experience developing APIs and managing distributed systems for both batch and real-time workloads
Excellent communication skills, with the ability to interface between research and product engineering
Ideally, You Have
Expertise with model serving frameworks such as NVIDIA Triton Server, VLLM, TRT-LLM and so on.
Expertise with ML toolchains such as PyTorch, Tensorflow or distributed training and inference libraries.
Familiarity with GPU cluster management and CUDA optimization
Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices
Experience with container registries, image optimization, and multi-stage builds for ML workloads
Experience orchestrating across ASR models or LLM models for building various GenAI applications
At Abridge, we’re transforming healthcare delivery experiences with generative AI, enabling clinicians and patients to connect in deeper, more meaningful ways. Our mission is clear: to power deeper understanding in healthcare. We’re driving real, lasting change, with millions of medical conversations processed each month.
Joining Abridge means stepping into a fast-paced, high-growth startup where your contributions truly make a difference. Our culture requires extreme ownership—every employee has the ability to (and is expected to) make an impact on our customers and our business.
Beyond individual impact, you will have the opportunity to work alongside a team of curious, high-achieving people in a supportive environment where success is shared, growth is constant, and feedback fuels progress. At Abridge, it’s not just what we do—it’s how we do it. Every decision is rooted in empathy, always prioritizing the needs of clinicians and patients.
We’re committed to supporting your growth, both professionally and personally. Whether it's flexible work hours, an inclusive culture, or ongoing learning opportunities, we are here to help you thrive and do the best work of your life.
If you are ready to make a meaningful impact alongside passionate people who care deeply about what they do, Abridge is the place for you.
Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees
Comprehensive Health Plans: Medical, Dental, and Vision coverage for all full-time employees and their families.
Generous HSA Contribution: If you choose a High Deductible Health Plan, Abridge makes monthly contributions to your HSA.
Paid Parental Leave: Generous paid parental leave for all full-time employees.
Family Forming Benefits: Resources and financial support to help you build your family.
401(k) Matching: Contribution matching to help invest in your future.
Personal Device Allowance: Tax free funds for personal device usage.
Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits.
Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more.
Mental Health Support: Dedicated access to therapy and coaching to help you reach your goals.
Sabbatical Leave: Paid Sabbatical Leave after 5 years of employment.
Compensation and Equity: Competitive compensation and equity grants for full time employees.
... and much more!
Abridge is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability.
We're committed to providing reasonable accommodations throughout the interview process. Once you submit your application, we'll follow up with details on how to request an accommodation for interviewing, completing any assessments, or otherwise participating in the selection process.
We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article. Please exercise caution and cease communications if something feels suspicious about your interactions.
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San Francisco, California, USA
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