Machine Learning Engineer

UST is seeking a Machine Learning Engineer (ML Engineer II) to build and optimize multi-agent systems for real-world performance. This onsite role in Oregon focuses on taking agent workloads from application design through inference runtime behavior and down to accelerator hardware and middleware layers.

What you’ll do

• Develop a multi-agent workload at the application level, then profile and optimize its execution all the way through the inference runtime, OMIX/middleware, compute runtime, Linux GPU driver, and accelerator hardware.
• Improve key performance metrics including latency, throughput, tokens/sec, memory footprint, and accelerator utilization.
• Select and tune models and inference engines based on agent workload characteristics, hardware capabilities, and performance requirements.

What you bring

• Hands-on development experience with multi-agent workloads.
• Experience with agent frameworks such as LangGraph/LangChain, AutoGen, and CrewAI.
• Strong understanding of LLMs, SLMs, and multimodal models, including Transformer architecture, attention, tokenization, context windows, and KV cache concepts.
• Hands-on experience selecting, evaluating, and deploying models for different agentic workload requirements.
• Knowledge of model formats and optimization workflows, including ONNX, OpenVINO IR, safe tensors, and quantization (FP16, BF16, INT8, INT4).
• Experience with inference engines and runtimes such as OpenVINO, ONNX Runtime, vLLM, llama.cpp, or TGI.
• Understanding of prefill vs. decode, batching, continuous batching, speculative decoding, KV-cache management, and memory optimization.
• Solid grasp of CPU/GPU execution, device placement, and heterogeneous inference.
• Ability to benchmark and compare models, inference engines, and runtime configurations, then identify performance bottlenecks.
• Knowledge of GPU memory, kernel execution, synchronization, device selection, and host/device data movement.
• Familiarity with middleware and accelerator compute runtimes such as OMIX/OneAPI/SYCL, Level Zero, or OpenCL.
• Python strength, plus working knowledge of C/C++.
• Experience with Git/GitHub, Linux shell, and debugging tools.
• Practical use of GitHub Copilot for development, debugging, and code generation.
• Docker and container fundamentals.
• Ability to benchmark and profile AI workloads.
• Understanding of latency, throughput, tokens/sec, GPU utilization, memory bandwidth, and CPU/GPU bottlenecks, including identifying whether a performance issue comes from the agent, model, inference engine, runtime, or driver.

Core technologies

• LangGraph, LangChain, AutoGen, CrewAI
• Python, C/C++, Git, GitHub, GitHub Copilot
• Docker
• ONNX, Open VINO IR, safe tensors
• FP16, BF16, INT8, INT4
• Open VINO, ONNX Runtime, vLLM, llama.cpp, TGI
• OMIX, OneAPI, SYCL, Level Zero, OpenCL

Role location

Oregon (onsite)

Compensation

$82,000 - $123,000 USD per year

Benefits

• Full-time employees accrue a minimum of 10 days of paid vacation per year.
• 6 days of paid sick leave each year (pro-rated for new hires throughout the year).
• 10 paid holidays.
• Eligible for paid bereavement leave and jury duty.
• 401(k) Retirement Plan with employer matching.
• Medical, dental, and vision insurance for employees and dependents residing in the US.
• Company-paid Employee Only benefits: basic life insurance, accidental death and disability insurance, and short- and long-term disability benefits.
• Regular employees may purchase additional voluntary short-term disability benefits.
• Eligible to participate in a Health Savings Account (HSA).
• Eligible to participate in a Flexible Spending Account (FSA) for healthcare, dependent child care, and/or commuting expenses.

Additional skills

• Agentic AI
• Python
• Linux
• MCP3
• RAG
• Kubernetes