🛠 Tech Stack

💼 About This Role

You'll design high-performance infrastructure for large-scale generative AI and ML workloads at a leading hedge fund. Your work will directly impact model iteration speed and production reliability. This role offers hands-on experience with state-of-the-art GPU clusters and distributed systems.

🎯 What You'll Do

• Design and implement high-performance infrastructure for GenAI/ML workloads
• Operate distributed systems for model training, inference, and data pipelines
• Develop and automate CI/CD pipelines for models and data workflows
• Implement observability, monitoring, and cost-management for GPU environments

📋 Requirements

• 3–7 years experience building ML or compute infrastructure systems
• Deep understanding of distributed systems, Kubernetes, and public cloud (AWS/GCP/Azure)
• Hands-on experience with MLflow, Ray, Airflow, Kubeflow, or Terraform
• Proficiency in Python and systems programming (Go, C++, or Rust)

✨ Nice to Have

• Understanding of reinforcement learning concepts
• Strong debugging and performance profiling skills across GPU/CPU stacks
• Experience with cost-optimization for GPU-based environments

🎁 Benefits & Perks

• 🏖️ Fully-paid health care benefits
• 👶 Generous parental and family leave policies
• 🎓 Tuition assistance
• 💰 401(k) savings program with employer match
• 🧘 Mental and physical wellness programs

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Phone Interview· 60 min
3. 3Onsite Interviews (2-3 rounds)· 3 hours total