Summary
At Springtail AI, we are developing highly sample efficient learning algorithms for AI reasoning. As a Research Scientist, you'll be at the forefront of developing novel machine learning architectures and algorithms to push the boundaries of AI reasoning and problem-solving capabilities.
Research Areas of Interest
- Sample efficient learning for limited data regimes
- Planning and graph / search algorithms
- Reinforcement learning and learning from self-play
- Successor architectures to transformers
You Are
- A highly accomplished machine learning researcher with a strong publication record or equivalent industry experience
- Skilled in developing and implementing novel machine learning architectures
- Experienced in both theoretical aspects (e.g., algorithm design, complexity analysis) and practical implementation (e.g., CUDA kernels)
- Adept at rapidly iterating and improving model performance
- Capable of reasoning pragmatically about limitations and constraints of current and proposed approaches
- An excellent communicator, able to collaborate with cross-functional teams and external partners
What we are looking for
- PhD or Masters in Computer Science, Machine Learning, a related field, or equivalent
- Experience with deep learning; familiarity with diffusion models, transformers, planning, or program synthesis is a plus
- Strong programming skills in languages such as Python, C++ and frameworks such as PyTorch
- Knowledge of CUDA and/or parallel hardware algorithm design
Compensation and Benefits
- Comprehensive health and financial benefits
- Generous paid time off and holiday schedule
- Ability to publish your research and opportunity to work on an important outstanding AI problem
- Base salary range: $150,000 - $175,000 annually. If your compensation expectations are outside of this range, we still encourage you to apply.
How to Apply
Please email your resume along with an (optional) cover letter outlining your qualifications and research interests to info@springtail.ai. We encourage you to include any relevant publications, open-source contributions, or personal projects that showcase your expertise.