ML Engineer
Core
Design, build, and operate infrastructure and platforms enabling the development, evaluation, deployment, and operation of ML models and LLM-based features at scale.
Role type
Senior IC ML Engineer (ML Infrastructure & Platform)
Builds
GPU-based training/evaluation workflows, model serving, LLM infrastructure, observability, internal SDKs, and developer tooling
Domain
AI Security / Machine Learning Infrastructure
Deliverable
production ML models
Required skills
Python, Kubernetes, Docker, Cloud (AWS/GCP/Azure), ML lifecycle (orchestration/serving/evaluation/training), CI/CD, Observability, End-to-end system design
Preferred skills
GPU workloads, vLLM, BentoML, Argo Workflows, Kubeflow, LLM gateways, Agentic systems
Technologies
Kubernetes, Docker, AWS, GCP, Azure, vLLM, BentoML, Argo Workflows, Kubeflow
Responsibilities
Design and build ML workflows for training, evaluation, batch prediction, and experimentation; Develop ML infrastructure and developer tooling; Operate and improve production ML services; Deploy and operate ML workloads on Kubernetes and cloud infrastructure; Contribute to LLM platform including self-hosted model serving and SDKs; Own engineering problems end-to-end from ideation to production operation; Collaborate across research and engineering teams; Explore and integrate emerging technologies in ML infrastructure and agentic systems
Seniority
Senior, hands-on IC
