CV/ML Platform Engineer
AI Startup
San Francisco, CAOn-siteFull-time$140k – $220k / year
About this role
CV/ML Platform Engineer
CV/ML Platform Engineer
at Allen Control Systems Full Time
Company Overview
Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing a small, autonomous gun turret that employs advanced computer vision and control systems to precisely target and neutralize small drones and loitering munitions. Our innovative approach requires overcoming significant technical challenges, making this an exciting and dynamic environment for experienced engineers.
With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders’ successful exits from two previous ventures acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop have a real-world impact.
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