Inference Engineer - Member of Technical Staff

Designworks Talent • Bellevue, WA

1d • Hybrid

About The Position

Build and operate the model-serving systems behind a next-generation AI inference platform. This foundational engineering role focuses on bringing AI models from research environments into reliable production services by building infrastructure that serves large models efficiently, optimizes performance, and maintains reliability as usage scales. The work involves high-throughput, low-latency inference experiences delivered through production-scale APIs.

The engineer collaborates with GPU performance, AI training infrastructure, platform engineering, orchestration, and operations teams on model serving, latency optimization, resource efficiency, and production reliability. The role suits engineers working at the intersection of distributed systems, machine learning infrastructure, GPU computing, and large-scale production systems. It is a hybrid role based in the Bellevue, Washington area, with approximately three days per week in the office. Candidates elsewhere in the United States who are open to relocation are encouraged to apply. U.S. work authorization is required; visa sponsorship is not currently available.

Requirements

• Experience building and operating production machine-learning inference or model-serving systems at scale.
• Strong understanding of performance trade-offs in serving large AI models, including latency, throughput, memory utilization, and cost efficiency.
• Experience designing reliable distributed systems or production infrastructure.
• Understanding of GPU-backed AI workloads and the challenges of scaling inference systems.
• Strong engineering fundamentals and ability to independently own complex technical problems.
• Comfort working in a fast-moving environment where systems and processes are being built from the ground up.
• U.S. work authorization is required; visa sponsorship is not currently available.

Nice To Haves

• Experience with inference-serving frameworks such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or similar technologies.
• Experience optimizing LLM inference workloads or large-scale AI serving platforms.
• Background operating API-based AI products or high-volume production services.
• Experience with GPU scheduling, distributed systems, Kubernetes, or cloud infrastructure platforms.
• Familiarity with model optimization techniques such as quantization, batching, caching, or performance tuning.
• Experience at a hyperscaler, AI lab, GPU cloud provider, or large-scale ML infrastructure organization.

Responsibilities

• Build and operate production-grade model-serving and inference systems for high-throughput, low-latency AI workloads.
• Optimize inference infrastructure for token throughput, latency, scalability, and cost efficiency across model architectures and workloads.
• Design systems that maximize GPU utilization while maintaining predictable performance and reliability.
• Improve inference-platform scalability and operational maturity as customer demand grows.
• Partner with AI training, GPU performance, orchestration, and infrastructure teams to support transitions from model development to production serving.
• Develop monitoring, alerting, and operational practices to maintain reliable inference services.
• Investigate and resolve performance, reliability, and capacity challenges across inference workloads.
• Contribute to architecture decisions and engineering standards as the platform evolves.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k) plan and company match
• Paid holidays
• Certain roles may be eligible for merit increases, annual bonus, and long-term incentives, allocated based on individual performance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed