Role OverviewWe are seeking a MLOps Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads.
What You Will Do
Design and operate model serving platforms, optimize inference performance, implement multi-tenant routing, rate limiting, and quality-of-service policies, build autoscaling and capacity management systems, and drive end-to-end observability.
Why It Might Be a Fit
The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.
Requirements
- Bachelor’s or Master’s degree in Computer Science or a related field.
- Six or more years of experience in distributed systems, infrastructure, or ML platform engineering.
- Strong proficiency in Python and a systems language such as Go, Rust, or C++.
- Deep experience operating high-throughput, low-latency services in production.
- Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM.
- Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization.
- Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
- Experience with observability stacks including metrics, tracing, and structured logging.
- Solid grounding in performance engineering and capacity planning.
- Strong communication and incident response skills.
Benefits
- Salary Range: $100,000–$150,000 Annually
- 100% Remote (U.S.)
- Full-time, Direct W2
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