Machine Learning Infrastructure Engineer

Abridge•San Francisco, CA

46d

About The Position

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 a Senior Machine Learning Systems Engineer at Abridge, you’ll play a pivotal role in building and optimizing the core 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.

Requirements

Strong 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

Nice To Haves

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

Responsibilities

Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training
Develop, optimize, and maintain ML model serving and training 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, throughout 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.

Benefits

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!