Remote Infrastructure Ml Jobs

Experience that helps: GPU and ML infrastructure: capacity planning, training or inference pipelines

Hands-on fluency with SQL, Python, AI code generation tools, and version control best practices Working knowledge of current ML

/ Stats knowledge, i.e. can easily employ the suite of standard ML / stats tools to quickly scope out solutions, and double down where needed.

What You Have Experience building ML infrastructure, distributed systems, or research tooling, with ownership

infrastructure and DevOps optimization.

ML/Big Data Ops : Hands-on experience with the infrastructure required for machine learning at scale (GPUs, Docker, Kubernetes

Infrastructure Engineer (SRE) Department: Engineering – Infrastructure About Atomic Industries

MLOps: Build infrastructure that enables ML model development and deployment in production systems.

As a Senior ML Engineer, you will run the experiments that decide how that system improves, and ship the winners to production.

of HUD’s core infrastructure and backend systems.

What you'll do: Own the infrastructure powering AI and ML models across critical business surfaces–supporting growth,

The primary need is a production ML and evaluation authority who also builds production agent systems.

ML infrastructure: Own the serving and orchestration layer for ML models (vision, audio, language) in production.

Substantial hands-on experience building and deploying AI/ML systems in production, including MLOps, model serving and inference infrastructure, feature

Lead to own Closure’s infrastructure end-to-end, reporting directly to the CTO.