Level

Abridge

Member of Technical Staff, Machine Learning Infrastructure

AI in this role

vllmpytorchtensorflow

About Abridge

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.

The Role

As an ML Infrastructure Engineer 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

Why Work at Abridge?

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.

How we take care of Abridgers:

  • 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!

Equal Opportunity Employer

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.

Staying safe - Protect yourself from recruitment fraud

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. 

How we rate this

Member of Technical Staff, Machine Learning Infrastructure at Abridge rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

vLLMPyTorchTensorFlow

Questions you could be asked

  1. What's a project where you used vLLM hands-on?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of TensorFlow that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: vLLM, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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