Senior Software Engineer - ML Infrastructure
Applied Intuition is seeking a Senior Software Engineer to join our Data & ML Infrastructure team. In this role, you will work across the entire machine learning lifecycle, including dataset generation, training frameworks, compute, evaluation, and deployment. This position offers the opportunity to collaborate directly with modeling teams and contribute to the development of cutting-edge machine learning infrastructure.
As a Senior Software Engineer, you will design and implement distributed cloud GPU training approaches for deep learning model training and evaluation. You will build end-to-end machine learning pipelines and integrate them into core product workflows. Additionally, you will encourage change in support of ML engineering best practices and maintain a high standard of excellence. Collaboration with engineers across the company to solve complex data problems at scale is also a key aspect of this role.
The ideal candidate will have a Bachelor's degree in Computer Science, Software Engineering, or a related field, along with at least 3 years of professional experience. Experience in building software components to address production, full-stack machine learning challenges is essential. Candidates should have opinions about building a company-wide platform for ML training, evaluation, and deployment, as well as knowledge of the open-source landscape with judgment on when to choose open source versus build in-house. Excellent analytical and problem-solving skills are also required.
The base salary for this full-time position ranges from $153,000 to $222,000 annually. Compensation includes base salary, equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life, and disability insurance coverage, 401(k) retirement benefits with employer match, learning and wellness stipends, and paid time off. Please note that benefits are subject to change and may vary based on the jurisdiction of employment.
Applied Intuition fosters a dynamic and collaborative work environment where engineers are encouraged to take ownership of technical and product decisions. We value flexibility and trust our employees to manage their schedules responsibly, recognizing the importance of work-life balance. This role offers the opportunity to work on broad, ambiguous problems and develop across the entire ML stack, contributing to the advancement of machine learning infrastructure in a rapidly growing company.