Develop and manage infrastructure for distributed model training (e.g., SageMaker, Ray, Kubernetes).
Deploy ML models using containerization (Docker), orchestration tools (Kubernetes, ECS), and serving frameworks
Integrate ML workflows seamlessly with CI/CD pipelines for efficient model building, testing, and deployment
Create and maintain robust data and ML pipelines using Prefect, Airflow, or custom orchestration tools
Implement comprehensive experiment tracking (MLflow, Weights & Biases) and observability systems (Arize, Evidently)
Establish effective monitoring, logging, and governance practices for ML systems.

Requirements

BS/MS in Computer Science, Data Science, or related technical discipline
5+ years experience in ML Operations, with at least 3 years focused on scalable AI/ML deployments
Strong proficiency with cloud infrastructure (preferably AWS), container technologies (Docker, Kubernetes), and modern MLOps frameworks
Extensive experience managing GPU and CPU resources for specialized AI workloads (Computer Vision, NLP, or LLM fine-tuning)
Practical experience with data quality and performance monitoring in production ML environments
Bonus: Experience designing data architectures optimized for AI/ML (vector, graph databases)
Bonus: Familiarity with RAG systems and agentic applications

Benefits

A close-knit and collaborative early-stage startup environment where every voice is heard and every opinion matters
Competitive salary and stock option equity packages
4 Medical Plans to choose from including 100% covered option. Plus Dental and Vision Insurance!
Learning & Growth stipend
Flexible long-term work options (remote and hybrid)
Free lunch provided in the office in NYC & Austin - you’ll never go hungry with us!
Unlimited PTO; We truly believe in work-life balance and that hard work should be balanced with time for rest and rejuvenation
IRL / In-Person retreats throughout the year

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learning operationsdistributed model trainingexperiment trackingdata pipelinesmonitoringlogginggovernance practicesdata quality monitoringperformance monitoringdata architecture