5 ai ml engineer jobs at 1 company in Boardman, OR
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DevOps/Platform Engineer II
Richland, Washington, United States
$109k-$164k/yrHybridFull Time
Pacific Northwest National Laboratory: Department of Energy research laboratory focused on scientific innovation.
2+ YOEDegree (BS+ with 2yrs, MS, or PhD) and hands-on Python development experience; experience with cloud, infrastructure automation, CI/CD, containerization, and AI/ML workflows preferred.
Pacific Northwest National Laboratory: Department of Energy research laboratory focused on scientific innovation.
1+ YOEBS+5 or MS+3 or PhD+1 experience; expertise in catalysis, reaction engineering, biomass conversion, AI/ML for R&D, coding, and demonstrated technical leadership; strong publication and proposal record.
Pacific Northwest National Laboratory: Department of Energy research laboratory focused on scientific innovation.
Extensive software engineering experience with hands-on Python; advanced expertise in AI/ML, distributed systems, cloud, data platforms, and technical leadership; ability to obtain federal security clearance and U.S. citizenship required.
Carbon Robotics: Builds AI-powered laser robots for autonomous weed control.
4+ YOE4+ years technical recruiting experience hiring software, hardware, robotics, and AI/ML engineers; experience with Greenhouse and LinkedIn Recruiter; ability to source passive candidates and evaluate technical fit for roles using Python, C++, TypeScript, React, Dart/Flutter, ROS, and AI/ML.
Post Doctorate Research Associate - Engineering Data Scientist
Richland, Washington, United States
$69k-$119k/yrHybridFull Time, Temporary
Pacific Northwest National Laboratory: Department of Energy research laboratory focused on scientific innovation.
PhD within past 5 years (or within next 8 months). PhD in data science/AI/ML or engineering with AI/ML; experience developing, training, deploying AI/ML models; proficiency in statistics, numerical modeling, HPC; programming in C/C++, Python, R; strong communication.
C/C++, Python, R, physics-informed neural networks (PINNs), deep learning, Finite Element Methods