Senior Software Engineer (ML Infrastructure)
Posted Updated
- We are looking for both infrastructure engineers with expertise in machine learning pipelines and ML engineers that want to work beyond modeling to join the Data & ML infra group
- This role will work across the entire ML lifecycle (dataset generation, training frameworks, compute, evaluation, and deployment) and work directly with modeling teams
- This team is a good fit if you are excited to work on broad, ambiguous problems and develop across the entire ML stack
- At Applied Intuition, we encourage all engineers to take ownership over technical and product decisions, closely interact with external and internal users to collect feedback, and contribute to a thoughtful, dynamic team culture
- Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation
- Build end-to-end machine learning pipelines and integrate them into core product workflows
- Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence
- Collaborate with engineers across the entire company to solve complex data problems at scale
- Knowledge of the open source landscape with judgment on when to choose open source versus build in-house
- Excellent analytical and problem-solving skills
- A Bachelor’s degree in Computer Science, Software Engineering, or equivalent
- Experience with building software components to address production, full-stack machine learning challenges. This is not purely a research problem
- Opinions about building a company-wide platform for ML training, evaluation, and deployment
- Experience with developing, running, and managing orchestration systems like Airflow and Flyte that non engineers can use to build data pipelines
- Experience with ML modeling frameworks (PyTorch, Tensorflow, etc.), and model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.)
- 3+ years of professional experience