Staff Inference Engineer

Designworks Talent LLC

. Build and operate production-grade model-serving and inference systems for high-throughput, low-latency AI workloads .

Posted 10/2/2026full-timeBellevue • Washington • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating production-grade machine learning inference systems, optimizing for latency, throughput, and cost efficiency. Proficient in designing reliable distributed systems and managing GPU-backed AI workloads to ensure scalability and performance.

Highest-signal resume keywords

Production Machine Learning Inference SystemsGPU Utilization OptimizationDistributed Systems DesignInference-Serving FrameworksAPI-Based AI Products

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model-Serving SystemsLatency OptimizationThroughput ManagementMemory UtilizationCost-Efficiency Trade-OffsGPU SchedulingPerformance TuningQuantizationBatchingCloud Infrastructure

Soft Skills

Problem SolvingAdaptabilityCollaboration

Tools & Technologies

VLLMSGLangTensorRT-LLMTriton Inference ServerKubernetes

Industry Keywords

AI WorkloadsHigh-Volume Production ServicesHyperscalerAI LabML Infrastructure

Tech Stack

Tools & technologies

CloudDistributed SystemsKubernetes

About the role

Key responsibilities & impact

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 transition models from development to production serving
Develop monitoring, alerting, and operational practices for 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

Requirements

What you’ll need

Experience building and operating production machine learning inference or model-serving systems at scale
Strong understanding of latency, throughput, memory utilization, and cost-efficiency trade-offs when serving large AI models
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
Comfortable working in a fast-moving environment where systems and processes are built from the ground up
Experience with modern 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 quantization, batching, caching, or performance tuning
Experience working at a hyperscaler, AI lab, GPU cloud provider, or large-scale ML infrastructure organization
U.S. work authorization is required
Visa sponsorship is not currently available
Willingness to work in a hybrid situation based in downtown Bellevue, WA, with a minimum of three days per week in-office once the permanent office is established

Benefits

Comp & perks

Competitive base pay for Bellevue market
Merit increases
Annual bonus
Long-term incentives
Medical insurance
Dental insurance
Vision insurance
401(k) plan
Company 401(k) match
Paid holidays
Startup ownership and technical impact
Collaboration with an experienced AI infrastructure team