We are seeking an engineer who brings both hands-on model training, model fine-tuning, feature engineering expertise and a strong background in data/AI/ML infrastructure to join our world-class team, pushing the frontiers of multimodal data infrastructure.
Requirements
- Resident expert on AI engineering, bringing familiarity with frameworks such as PyTorch or JAX.
- Champion a superior Developer Experience, maximizing productivity for AI engineers.
- Drive the end-to-end design and development of a high-performance and large-scale feature engineering infrastructure for leading multimodal AI companies.
- Collaborate closely with the customers, design partners, and the Lance/LanceDB community
- You like working with a small, high-caliber team with a lot of autonomy and drive, and you can iterate fast.
- You have 3+ years of experience building and deploying ML/DL models in production environments or supporting infrastructure for AI researchers and AI engineers performing these tasks, using Python and libraries such as PyTorch or Tensorflow.
- You have a proven ability to deliver projects end-to-end, from scoping and resourcing to implementation and delivery.
- You have a working knowledge of cloud platforms (AWS, GCP, Azure) including managed storage (S3, GCS) and compute (EC2, GKE, AKS).
- You have knowledge of monitoring/logging stacks (Prometheus, Grafana, ELK/EFK) for alerting on data pipeline failures, resource saturation, or model skew.