Inference Engineer - Member of Technical Staff

• Build and operate production-grade model-serving and inference systems supporting high-throughput, low-latency AI workloads.
• Optimize inference infrastructure for token throughput, latency, scalability, and cost efficiency across different model architectures and workloads.
• Design systems that maximize GPU utilization while maintaining predictable performance and reliability.
• Improve the scalability and operational maturity of inference platforms as customer demand grows.
• Partner with AI training, GPU performance, orchestration, and infrastructure teams to ensure smooth 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.

• Experience building and operating production machine learning inference or model-serving systems at scale.
• Strong understanding of the performance trade-offs involved 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 the ability to independently own complex technical problems.
• Comfortable working in a fast-moving environment where systems and processes are being 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 model optimization techniques such as quantization, batching, caching, or performance tuning.
• Experience working at a hyperscaler, AI lab, GPU cloud provider, or large-scale ML infrastructure organization.

• Hybrid role based in the Bellevue, WA area.
• Approximately three days per week in the office.
• Candidates elsewhere in the U.S. who are open to relocation are encouraged to apply.
• U.S. work authorization is required.

WHY JOIN?

• Build the inference platform powering the next generation of AI applications.
• Work directly on large-scale model serving, GPU optimization, and production AI systems.
• Solve complex challenges around latency, throughput, reliability, and cost efficiency.
• Join early enough to influence architecture, tooling, and engineering practices.
• Collaborate with a highly experienced team building critical AI infrastructure from the ground up.
• Enjoy the ownership and technical impact of a startup environment backed by significant long-term investment.

• Competitive base pay for Bellevue market
• Certain roles are eligible for additional rewards, including merit increases, annual bonus, and long term incentives. These awards are allocated based on individual performance
• U.S. based employees have access to medical, dental, and vision insurance, a 401(k) plan and company match, employees also receive per calendar year, paid holidays.