2+ years of experience in Platform Engineering or DevOps/MLOps
Strong programming skills for automating ML lifecycles and building custom CLI tools
Hands-on experience with NVIDIA GPU infrastructure and CUDA libraries
Experience with MLOps platforms like Kubeflow, MLflow, or Weights & Biases
Familiarity with high-performance storage solutions for terabytes of data
Proven track record in building CI/CD pipelines for ML
Experience with model optimization tools for ARM targets like NVIDIA Jetson
Proficiency with observability stacks tailored for ML.
Responsibilities
Deploy and operate bare-metal Kubernetes clusters with 130+ NVIDIA GPUs
Manage NVIDIA GPU clusters for machine learning training
Own the CI/CD pipeline for CV/ML
Enhance core ML infrastructure like model registration and experiment tracking
Maintain tools for model testing and reporting
Automate repetitive model training and testing tasks
Coordinate with Software Team to minimize duplication between systems
Collaborate on optimizing models for edge hardware deployment.
Benefits
Health, Dental, Vision Insurance
Paid Time Off
Full Job Description
Position Overview
We are seeking an experienced CV/ML Platform Engineer with specialization in Computer Vision and Machine Learning (CV/ML) to design, build, and own the data, model, and compute infrastructure powering ACS CV/ML team. You will help manage a 130+ GPU bare-metal Kubernetes cluster, own CV/ML CI/CD pipelines, and ensure ML model training proceeds at high volume with low friction.
What You'll Do:
Deploy and operate Kubernetes clusters on bare-metal infrastructure hosting 130+ NVIDIA GPUs, with hybrid burst capability to AWS for scalable compute and storage workloads.
Manage NVIDIA GPU clusters for ML training.
Own the ACS CV/ML CI/CD pipeline.
Improve and maintain core ML infrastructure, such as model registration and versioning, experiment tracking, and model and data provenance tracking.
Improve and maintain ML model testing, performance analysis, and reporting tools.
Automate repetitive model training and testing tasks to increase developer velocity.
Work with Software Team Platform Engineers to ensure efficient coordination and minimal duplication between CV/ML infrastructure and wider Software infrastructure.
Collaborate with the Software Team to automate the optimization of models (TensorRT/quantization) for deployment on NVIDIA Jetson and other edge hardware.
Required Technical Skills:
2+ years of experience in Platform Engineering or DevOps/MLOps.
Strong programming skills are required for automating ML lifecycles and building custom CLI tools for CV engineers.
Hands-on experience with NVIDIA GPU infrastructure, including managing CUDA libraries and development environments, GPU Operator, device plugins, and scheduling (MIG, Volcano, or fractional GPU sharing).
Experience implementing and maintaining MLOps platforms such as Kubeflow, MLflow, Weights & Biases (W&B), or DVC for experiment tracking and model versioning.
Familiarity with high-performance storage solutions (e.g., MinIO, WEKA, or Ceph) and data orchestration tools capable of handling terabytes of video/image data.
Proven track record building CI/CD pipelines that include automated model validation, performance benchmarking, and artifact management for both cloud and edge targets.
Experience with model optimization toolchains, including TensorRT, ONNX, and quantization techniques, specifically for cross-compilation to ARM targets like NVIDIA Jetson.
Proficiency with observability stacks (ELK, Prometheus/Grafana) adapted for ML, including monitoring GPU health, training throughput, and model inference metrics.
Strong Linux systems knowledge (Debian/Ubuntu), including networking for high-throughput data, storage, and security hardening for defense-grade production environments.
