- Lead the development of machine learning systems for VFM & VLM production
- Optimize inference infrastructure for scalability and reliability
- Oversee ML deployment & operations (VFMOps / VLMOps) for model optimization and automation
- Manage data infrastructure and preparation for high-quality video data
- Design effective team processes for operational efficiency
- Coach and develop team members for career growth
- Drive recruiting efforts during rapid growth periods
- 10+ years of software development experience, including ML engineering
- 5+ years building end-to-end ML systems, including MLOps and data management
- Experience managing high-output engineering teams for 2+ years
- Proficiency in video processing and data pipelining
- Experience with secure software development environments
- MS or PhD in CS, Math, or equivalent experience
- Startup engineering experience in a fast-paced environment
- Familiarity with large-scale models and both cloud/on-premise environments
- ML research experience is a plus
- Understanding of large-scale computing systems and cloud scaling approaches
- Languages: Python, Golang, C++, CUDA
- ML/Platform: PyTorch, Docker, Kubernetes, Terraform
- ML Ops: MLFlow, Weights and Biases
- Data: Pachyderm, DVC
- Automation: Airflow, Kubeflow
- Model Serving: Triton, FasterTransformer
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Machine Learning Infrastructure Engineer - California, United States - Intelletec
Description
ML Systems Engineer - Lead Role
Join our team as the Lead ML Systems Engineer and drive the development of cutting-edge machine learning systems for video foundation (VFM) and language model (VLM) in production. In this role, you'll lead a talented team, set technical strategies, and ensure our systems exceed user expectations in speed, efficiency, and reliability.
Responsibilities:
Requirements:
Desired Experience:
Tech Stack:
