Design and build learned evaluation pipelines that assess driving behavior using LLMs, VLMs, and multimodal models
Develop agentic workflows that chain model inference, retrieval, and structured reasoning to evaluate complex driving scenarios
Define evaluation-of-evaluation methodology — how do we know our learned evaluators are correct?
Build golden-set frameworks and calibration loops for learned metrics
Partner with AML (Alpamayo Logos) teams on model-specific eval needs (e.g., COT prediction quality, AML regression coverage)
Instrument evaluation systems with robust experiment tracking, A/B comparison tooling, and model versioning
Contribute to the team's transition from rule-based to learned evaluation: identify metrics and analyzers that are candidates for ML replacement and build the alternatives

Requirements

PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
Hands-on experience building LLM/VLM-based pipelines — fine-tuning, prompt engineering, retrieval-augmented generation, chain-of-thought
Track record of shipping ML systems to production (not just prototyping or publishing)
Strong software engineering fundamentals — you write clean, tested, reviewable code in Python and C++
Experience with evaluation methodology: precision/recall, inter-rater reliability, calibration, annotation pipelines
Comfort with large-scale data processing (Spark, Dask, or similar)
Strong Python skills.
Experience with PyTorch or JAX.
Comfortable with GPU-based training workflows.

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

LLMVLMmultimodal modelsmodel inferenceretrieval-augmented generationchain-of-thoughtPythonC++PyTorchJAX

Soft Skills

clean codetested codereviewable code

Certifications

PhDMSBS