OPF is hiring a contract engineer to run the data collection phase of an emissions measurement study comparing edge versus cloud LLM inference across multi-modal AI workloads. The methodology is being designed by external advisors with prior published work on AI carbon measurement; the engineer's role is to implement that methodology rigorously, capture clean instrumented data, and hand it off cleanly to OPF's internal team for analysis and modeling.
Findings will appear in a co-branded industry publication backed by a defensible methodology — meaning the public artifact is industry-format, but the underlying data has to withstand potential scrutiny.
Engagement scope
You will design, build, and run a Python-based testing harness that captures inference runtime, energy consumption, and component-level power across three edge devices and one cloud GPU instance, then deliver clean, documented outputs to the OPF analytical team for downstream modeling.
The work spans Windows and Linux, three distinct hardware platforms (Intel Core Ultra with NPU, AMD Ryzen AI with XDNA NPU, NVIDIA L4 cloud GPU), multiple deployments (OpenVINO and Lemonade Server for local; vLLM, Triton, and/or FastAPI for cloud as appropriate per task); and two layers of measurement (software-based estimation and on-die telemetry).
The engagement begins with a proof-of-concept phase on the AMD Ryzen 7 Pro laptop, focused on a single task (Basic Queries), to build the harness, telemetry, and methodology end-to-end before scaling to the rest of the platforms and tasks. Following this, a cloud GPU instance must be provisioned for cloud data collection and harness validation. Once the harness has been validated on one laptop and in the cloud environment, the approach will be scaled to the remaining laptops and tasks. Proof-of-concept outcome serves as an interim milestone before proceeding with the remainder of the engagement.
See https://onepointfive.notion.site/job-spec-al-ml-engineer for the full job description.
Requirements
Required experience
- Strong Python proficiency, experimental discipline, and reproducibility practices
- Hands-on hardware telemetry — Intel RAPL, NVIDIA NVML, plus at least one of Intel SoC Watch / VTune, AMD μProf, or HWiNFO64
- Working knowledge of at least one in-scope deployment (OpenVINO, Lemonade Server, vLLM, or Triton)
- Linux and Windows systems experience, including hardware performance counter telemetry
- Demonstrated reproducibility discipline: pinned environments, version-controlled configs, documented assumptions
Strongly preferred
- Prior MLPerf Power or MLCommons Power working group submissions
- Published or contributed to AI energy / carbon measurement research or related sustainability topics
- Experience with NPU-class accelerators (Intel AI Boost, AMD XDNA, or comparable)
- Experience with GPU benchmarking methodology — DCGM telemetry, clock-pinning for reproducibility, or comparable practices
- Comfort working alongside external advisors and delivering well-documented handoffs to a downstream analytical team
About the Company
OnePointFive is a leading authority on climate action, offering clear, actionable solutions and skills through our Advisory & Academy.
Contact Info
