Software Engineer, ML Infrastructure

Responsibilities

• Build and evolve the core ML infrastructure platform providing researchers and engineers seamless access to compute and data resources
• Scale automated Infrastructure-as-Code (IaC) pipelines to manage thousands of GPU/CPU nodes across diverse environments
• Design and optimize workload orchestration to maximize hardware utilization, minimize job wait times, and handle massive-scale distributed training
• Design robust pipelines for extraction and transformation of petabyte-scale sensor and telemetry data into ML-ready formats
• Implement robust feature caching and storage solutions to reduce redundant computations and ensure low-latency access to pre-computed features
• Contribute to a unified ML platform that abstracts complex cloud infrastructure for end-users

Requirements

• 3+ years of professional experience in ML Infrastructure, Backend Platform Engineering, or Distributed Systems
• Deep familiarity with modern Infrastructure-as-Code and provisioning tools such as Terraform, Pulumi, or Crossplane
• Hands-on experience building or managing large-scale orchestrators for compute-heavy workloads (e.g., Kubernetes, KubeRay, Ray, Slurm, or Volcano)
• Proficiency in at least one distributed processing framework, such as Apache Spark or Apache Beam, for large-scale data extraction and transformation
• Experience implementing or maintaining feature stores and caching layers (e.g., Feast, Hopsworks, or Redis-based custom caching)
• Strong understanding of distributed systems, networking, and storage bottlenecks in the context of high-performance computing

Nice-to-Haves

• Active contributor to open-source projects in the MLOps or Cloud-Native ecosystem (e.g., CNCF, Ray, or Kubeflow communities)
• Experience with high-performance storage systems (e.g., Lustre, Ceph, or specialized NVMe caching) for ML data loading
• Knowledge of cost-optimization strategies for large-scale GPU clusters in public clouds (AWS, GCP, or Azure)