Inference Engineer – Member of Technical Staff

Designworks Talent LLC

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

Posted 11/10/2026full-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 SystemsLatency And Throughput OptimizationReliable Distributed Systems DesignGPU-Backed AI WorkloadsKubernetes And Cloud Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Model-Serving SystemsInference Infrastructure OptimizationPerformance TuningAPI-Based AI ProductsQuantizationBatchingCachingGPU SchedulingDistributed SystemsEngineering Fundamentals

Soft Skills

Problem SolvingIndependent OwnershipAdaptability

Tools & Technologies

VLLMSGLangTensorRT-LLMTriton Inference ServerKubernetes

Industry Keywords

High-Throughput AI WorkloadsOperational MaturityScalability ChallengesHyperscaler ExperienceLarge-Scale ML 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 on model-to-production serving transitions
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 inference-system scaling challenges
Strong engineering fundamentals and ability to independently own complex technical problems
Comfortable working in a fast-moving, ground-up environment
Preferred: experience with vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or similar technologies
Preferred: experience optimizing LLM inference workloads or large-scale AI serving platforms
Preferred: background operating API-based AI products or high-volume production services
Preferred: experience with GPU scheduling, distributed systems, Kubernetes, or cloud infrastructure platforms
Preferred: familiarity with quantization, batching, caching, or performance tuning
Preferred: experience at a hyperscaler, AI lab, GPU cloud provider, or large-scale ML infrastructure organization
U.S. work authorization is required

Benefits

Comp & perks

Certain roles are eligible for additional rewards, including merit increases, annual bonus, and long term incentives
U.S. based employees have access to medical, dental, and vision insurance
401(k) plan and company match
Paid holidays per calendar year
Hybrid work arrangement