Role overview

Machine Learning Infrastructure Engineer, Model Inference

Requirements and responsibilities

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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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Browse stack
FocusML EngineeringRole area
Seniority signalOpen levelCandidate level
StackKubernetesPrimary skills
Location1 accepted countryEligibility

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