About the RoleLuma is pushing the boundaries of generative AI, building tools that redefine how visual content is created. We're seeking a Research Engineer to design and scale the infrastructure that powers our model evaluation efforts. This role is about building the pipelines, metrics, and automated systems that close the loop between model output, evaluation, and improvement. You'll work across research, engineering, and product teams to ensure our models are measured rigorously, consistently, and in ways that directly inform development.
Responsibilities- 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.
Qualifications- 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).
Nice to Have- 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.
