Abridge
Machine Learning Infrastructure Engineer, Model Inference
Remote ML Engineering role with clear candidate location fit.
PostedAug 25, 2025
Eligible countries1 accepted country
Seniority signalOpen level
Work settingRemote
Accepted candidate locations
USA
Role overview
Machine Learning Infrastructure Engineer, Model Inference
Requirements and responsibilities
Readable role content extracted into sections for faster review.
The Role
- 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.
The Role
- 2+ 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
The Role
- 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
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!
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