Machine Learning Infrastructure Engineer, GenAI Technology - job 1 of 2
Skills
- Deep understanding of distributed systems and container orchestration (Kubernetes).
- Proficiency with public cloud platforms (AWS, GCP, or Azure) and hybrid cloud patterns.
- Hands-on experience with MLOps and infrastructure tools such as MLflow, Ray, Airflow, Kubeflow, and Terraform.
- Strong knowledge of reinforcement learning concepts and their infrastructure implications.
- Proficiency in Python and systems-level programming (Go, C++, or Rust).
- Experience with performance profiling and optimization on GPU and CPU compute stacks.
- Experience implementing observability, monitoring, and cost-optimization for accelerator-based environments.
- Excellent collaboration, communication, and systems-thinking skills.
Responsibilities
- Design and implement high-performance infrastructure for large-scale generative AI and ML workloads.
- Design and operate distributed systems for training, hyperparameter tuning, inference, and data preprocessing pipelines.
- Collaborate with ML researchers and engineers to optimize compute utilization, training throughput, and inference latency.
- Develop and automate deployment, orchestration, and CI/CD pipelines for models and data workflows using IaC and container orchestration.
- Implement observability, monitoring, and cost-management strategies for GPU and accelerator compute environments.
- Evaluate, integrate, and benchmark emerging hardware and software technologies to improve scalability and throughput.
- Define and drive security, compliance, and operational runbooks including access controls and incident response.
- Troubleshoot, profile, and optimize performance across GPU and CPU compute stacks to remove bottlenecks.
- Document architecture and operational practices and mentor engineers to expand team capability.
Education
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related technical field.
Benefits
- Fully-paid health care benefits
- Generous parental and family leave policies
- Volunteer opportunities
- Support for employee-led affinity groups (women, people of color, LGBT+)
- Mental and physical wellness programs
- Tuition assistance
- 401(k) savings program with employer match
- Discretionary bonus potential
Average salary estimate
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