Machine Learning Infrastructure Engineer

Machine Learning Infrastructure Engineer

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$/yr - $/yr

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.

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 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.

• 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, 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.

• 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

• Expertise with model serving frameworks such as NVIDIA Triton Server, VLLM, TRT‑LLM and so on.
• Expertise with ML tool chains 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 Git Ops 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…