Founding ML & Evaluation Engineer - Remote, Equity

QLUU Lab Inc.; Berkeley (CA); Remote; USD 120,000 - 180,000; Full time

Job summary

Qluu Lab, Inc. is seeking a founding ML and Evaluation Engineer to own the critical question: does the model actually work, and can we prove it?

You will be the independent check on our code and drive improvements, with remote-first collaboration and field tests in the U.S. In the first 90 days you will build an independent evaluation harness, benchmark against baselines, train and ablate on GPUs, export to ONNX for edge devices, and own the human-review protocol.

Qualifications

• Trained sequence or trajectory models in PyTorch and can read a loss curve and tell when it is lying.
• Understanding of grouped splits, leakage, bootstrap confidence intervals, multiple-comparison control.
• Write reproducible code with pinned environments, hashed data, deterministic seeds where it matters.
• Comfort with a small team, ambiguity, and shipping something on real hardware.
• Nice to have: ONNX/TensorRT/ONNX Runtime and edge deployment experience.

Responsibilities

• Own the evaluation framework and build an independent harness separating data loading, transforms, and metrics from training code.
• Measure the deployed selector against strong baselines and publish where Foresight wins or loses by domain.
• Train and ablate with GPUs, ensuring clean provenance for every checkpoint (config, data hashes, splits, seeds).
• Take the model to edge devices with ONNX export, latency/memory measurements on Raspberry Pi 5 and Nvidia Jetson.
• Own the human-review protocol with blinded samples and reviewer agreement.

Skills

Sequence/trajectory models

PyTorch experience

Statistics knowledge

Reproducible code practices

Edge deployment

Tools

PyTorch

ONNX

ONNX Runtime

TensorRT

Job description

Qluu Lab, Inc. is seeking a founding ML and Evaluation Engineer to own the critical question: does the model actually work, and can we prove it?

You will be the independent check on our code and drive improvements, with remote-first collaboration and field tests in the U.S. In the first 90 days you will build an independent evaluation harness, benchmark against baselines, train and ablate on GPUs, export to ONNX for edge devices, and own the human-review protocol.