ML Ops Engineer

Job Description

• Join Spotter's Product team as our first Machine Learning Operations Engineer • Design, build, and maintain scalable solutions for delivering our in-house models across our product • Enhance the platform’s efficiency and scalability, focusing on GPU resource allocation, data processing, model training, and model deployment • Collaborate with data scientists, AI engineers, and backend engineers to provide frictionless pipelines for deployment • Develop and oversee CI/CD pipelines with ArgoCD and Argo Workflow • Set up and manage monitoring frameworks using tools like Prometheus and Grafana to ensure the health and performance of our machine learning platform • Keep up to date on the latest developments in MLOps, distributed computing, and GPUs

Qualifications

• A Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience • A minimum of 5 years experience deploying and scaling machine learning models in a production environment • Extensive experience with CI/CD tools, orchestration platforms (e.g., Airflow, Kubeflow), and containerization (Docker, Kubernetes) • Familiarity with LLMs and diffusion models, especially open-source ones • Excellent communication skills, capable of collaborating effectively across multiple teams and stakeholders

Benefits

• Medical & vision insurance covered up to 100% • Dental insurance • 401(k) matching • Stock options • Complimentary gym access • Autonomy and upward mobility • Diverse, equitable, and inclusive culture, where your voice matters.