Research Engineer - Evaluations

• Design and implement scalable pipelines for automated evaluation of generative models, with a focus on visual and multimodal outputs (image, video, text, audio).
• Develop novel metrics and evaluation models that capture qualities like fidelity, coherence, temporal consistency, and alignment with human intent.
• Integrate evaluation signals into training loops (including reinforcement learning and reward modeling) to continuously improve model performance.
• Build infrastructure for large-scale regression testing, benchmarking, and monitoring of multimodal generative models.
• Collaborate with researchers running human studies to translate human evaluation frameworks into automated or semi-automated systems.
• Partner with model researchers to identify failure cases and build targeted evaluation harnesses.
• Maintain dashboards, reporting tools, and alerting systems to surface evaluation results to stakeholders.
• Stay current with emerging evaluation techniques in generative AI, multimodal LLMs, and perceptual quality assessment.

• Master's or PhD in Computer Science, Machine Learning, or a related technical field (or equivalent industry experience).
• 5+ years of experience building ML evaluation systems, model pipelines, or large-scale infrastructure.
• Hands-on experience working with visual data (images and/or video), including evaluation, modeling, or data preparation.
• Proficiency in Python and ML frameworks (PyTorch, JAX, or TensorFlow).
• Familiarity with human-in-the-loop evaluation workflows and how to scale them with automation.
• Strong background in machine learning, with experience in generative models (diffusion, LLMs, multimodal architectures).
• Strong software engineering skills (CI/CD, testing, data pipelines, distributed systems).

• Experience with reinforcement learning or reward modeling.
• Prior work on perceptual metrics, multimodal evaluation benchmarks, or retrieval-based evaluation.
• Background in large-scale model training or evaluation infrastructure.
• Experience designing metrics for perceptual quality
• Familiarity with creative media workflows (film, VFX, animation, digital art).
• Contributions to open-source evaluation libraries or benchmarks.

Compensation

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