Build, train, evaluate, and deploy computer vision and multimodal models, taking them from early prototype through to production
Design systems that infer structured attributes and spatial context from imagery, combining learned models with geometric and heuristic reasoning
Train and fine-tune models on large, diverse real-world image datasets, and build the pipelines to curate and label that data at scale
Work with vision-language models (VLMs) and build rigorous evaluation frameworks to measure their accuracy on our tasks
Develop and benchmark high-performance image retrieval capabilities with embedding models and vector indexing strategies
Optimize models for inference latency and throughput using techniques like distillation, quantization, and GPU acceleration
Read current research, prototype novel algorithms from academic literature, and turn promising ideas into reliable production code
Implement efficient, scalable data pipelines and inference infrastructure
Develop high-performance tooling in ML and data engineering
Additional duties and responsibilities as reasonably required by the employee’s supervisor or CEO

Experience building, training, evaluating, and deploying ML models in production
Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize models
Strong software engineering ability to build and maintain complex systems and work with large-scale datasets
Ability to solve open-ended problems and quickly learn new domains
Comfort operating with significant ownership and autonomy, making pragmatic trade-offs between model sophistication, velocity, inference and business constraints
BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience

Nice to have:

Experience inferring structured, real-world attributes from images
Experience training models on large-scale, real-world image datasets
Familiarity with vision-language models (VLMs)
Ability to digest academic literature, prototype novel algorithms, and bridge the gap between research and production code
Experience building LLM or VLM pipelines and the evaluation frameworks to measure their performance
Experience in an ML role at a growth-stage startup
Publications in major ML or computer vision conferences (e.g., CVPR, ICML, ICCV, WACV)

Senior Machine Learning Engineer

Key skills