Machine Learning Engineer

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AI/ML Solutions Architect – Distributed Training & GPU Infrastructure

Location: Remote from anywhere in the U.S.

Salary: Up to $230k base + bonus, depending on seniority

Join a fast-moving AI infrastructure team working on the cutting edge of large-scale ML workloads. This role is ideal for engineers who enjoy solving deep technical challenges in distributed training, multi-GPU systems, and scalable AI inference infrastructure. You'll work directly with AI-focused clients, helping them get the most out of modern GPUs (H100, B200, etc.) and ML frameworks like PyTorch and JAX.

• Work alongside world-class engineers building the infrastructure behind next-gen AI systems. As part of the customer solutions team, you'll:
• Design and deploy high-performance ML pipelines across hundreds/thousands of GPUs
• Guide customers in optimizing distributed training and inference setups
• Deliver tech talks, contribute to whitepapers, and gather feedback for product teams
• Work cross-functionally with engineering, product, and R&D to shape our AI platform

• 7+ years in ML infrastructure, MLOps, or similar roles
• Deep experience with PyTorch or JAX and multi-node training
• Strong understanding of ML pipeline design, performance tuning, and deployment
• Programming in Python (Go, Java, or C++ a plus)

Experience moving ML systems from POC to production-scale; familiarity with Hugging Face, Tensor Flow, or inference optimization

We’re looking for hands-on engineers who understand real-world ML problems and love building scalable, robust systems. If you thrive at the intersection of infrastructure and AI, this is your next move.

• Mid-Senior level

• Full-time

• Engineering and Information Technology

• IT Services and IT Consulting, Technology, Information and Media, and IT System Design Services

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