Machine Learning Research Assistant

Modern Intelligence is seeking exceptional students to join the team as a Machine Learning Research Assistant, and contribute to the creation of classical and hierarchical neural networks for American defense, in areas including sensor fusion, decision making, autonomy, complex inference, electronic warfare, and more. This position is the candidate’s choice of half-time or full-time, and runs during Summer 2022 with the option to extend through the end of the year. Interested candidates with a PhD are encouraged to apply to our AI Research Scientist Internship position. This position is open to remote candidates.

About Modern’s Research:Modern Intelligence is an Artificial Intelligence product and R&D company. Our product line is defense-first, and tackles some of the hardest and most interesting problems of the decade in machine learning, with a current focus on multi-modal domain awareness and complex scene understanding. And our research program focuses on foundational improvements to machine learning that aim to close the performance gap between artificial and biological neural networks. Whereas the current trend in machine learning is towards more data, more GPUs, and models with trillions of parameters, the fruit fly brain needs less than 200,000 neurons to achieve vision, target identification, navigation, and obstacle avoidance that is orders of magnitude more robust than any artificial neural network. 
The brittleness and inefficiency of artificial neural networks likely comes from a combination of * artificial neurons having “less compute”, * artificial neural architectures produced by NAS and human engineers having suboptimal topologies, and* SGD and other training processes being highly inefficient
relative to their biological equivalents. Modern’s research program uses proprietary advances in information theory to train hierarchical neural networks, or HNNs, with modular intermediate levels of structure that address the problem of multiscale competence. (See a recent interview with Dr. Michael Levin [video][transcript] to learn more about the high-level biological insights that motivate multiscale competence as a desirable [and possibly necessary] factor for robustness and efficient learning of high-complexity problems.)

About the position:Pay: $3,500 / month for half-time or $7,000 / month for full-timeWork period: Summer 2022, with option to extend
We’re looking for a curious and insightful student who believes that the answer isn’t just more data, and who has a deep appreciation of the benefits of sparsity, hierarchy, efficiency, and taking the right lessons from biology. With your team at Modern, you’ll get hands-on experience with some of the hardest problems in ML from both the product and the research direction. You’ll help build Modern’s research community, and will contribute directly to a research program that creates and trains HNNs in order to address the multiscale competence problem, work towards natural robustness in neural networks, and create general re-usable neural modules that can be leveraged in future learning. You’ll have as much guidance as you need and as much project ownership as you want, including the opportunity and encouragement to propose, design, and run your own experiments, and to publish the results.

Basic Qualifications:

• Recent PhD or active enrollment in a PhD program in Computer Science, Data Science, Mathematics, Statistics, Physics, or related field. Undergraduates and masters students will be considered if their application is sufficiently strong.
• Strong proficiency in coding and data science
• Competence, and ideally proficiency, in at least one popular ML framework, e.g. Tensorflow or Pytorch.
• Strong interest and academic experience in AI / ML, specifically in deep learning and/or reinforcement learning
• Some project-level experience in AI / ML, with publications preferred but not required
• Excellent communication skills, and the ability to work effectively in a team

Deeper Qualifications:

• Recent PhD or active enrollment in a PhD program in Computer Science, Data Science, Mathematics, Statistics, Physics, or related field. Undergraduates and masters students will be considered if their application is sufficiently strong.
• Strong proficiency in coding and data science
• Competence, and ideally proficiency, in at least one popular ML framework, e.g. Tensorflow or Pytorch.
• Strong interest and academic experience in AI / ML, specifically in deep learning and/or reinforcement learning
• Some project-level experience in AI / ML, with publications preferred but not required
• Excellent communication skills, and the ability to work effectively in a team

To apply, submit a resume or CV, and a cover letter, here on Lever. Letters of recommendation are not required but are strongly encouraged, especially if written from professors, researchers, or engineers who can speak to your interest and abilities.