Melonfrost is hiring an experienced scientist with a background in statistics, machine learning, optimization, or similar field, to head up the data science wing of our company. You will be responsible for designing and improving the algorithms used to steer the evolution of microbes in our Evolution Reactor™. You will be the first person in the world to use a machine that is unlike anything that has ever been built, collecting evolutionary data at an unprecedented scale, to modify organisms in a way that was once impossible.
More broadly, you will be part of a very small team of people building this company from the ground up, helping guide the future of Melonfrost, and the future of how this revolutionary tech will be used to make the world a better place. We expect you to be agile and adaptable as our company grows and stretches, ready to take on unpredictable challenges and opportunities. One minute you might be training predictive models in the cloud, and the next minute planning the company’s ten-year strategy. If none of that scares you off, you should apply.
- Working with technical co-founder to identify and design novel methodologies for controlling evolution.
- Contributing to the overall scientific direction of the company.
- Managing all algorithmic and analytical aspects of experiments conducted in the lab.
- Contributing to the design of future iterations of the Evolution Reactor™
- Working with scientific/technical team and clients to develop, communicate, and execute projects.
- Managing Melonfrost’s data science-related code repositories.
Again, this is an early-stage start-up that is growing quickly, and we all need to be prepared to wear many hats.
- Masters, PhD, or equivalent practical experience, in a relevant technical field.
- A track record of generating new ideas or improving upon existing ideas in mathematical optimization and/or machine learning, such as those demonstrated by one or more publications or projects.
- PhD, or equivalent practical experience, in statistics, computer science, computational mathematics, or electrical engineering.
- One or more first-author publications.
- A track record of generating new ideas or improving upon existing ideas in derivative-free optimization, reinforcement learning, or optimal control.
- Post doctoral roles and/or experience managing/mentoring in a research environment.
- Experience creating high-performance implementations of machine learning/optimization algorithms.
- Interest in evolutionary biology and/or evolutionary game theory
- Grant writing
- Designing and implementing ML/optimization algorithms in Python and related frameworks (e.g. Tensorflow/Pytorch/JAX)
- Performing statistical analysis and forecasting on large multidimensional time-series data
- Developing simulations for in silico testing of algorithms
- Deploying ML/optimization algorithms in the cloud (e.g. in AWS/Google Cloud/Azure)
- Integrating ML/optimization algorithms into real systems (e.g. robotics, IoT)
- Deep theoretical understanding of derivative-free optimization techniques
- Deep theoretical understanding of reinforcement learning and optimal control
- Writing mathematical proofs
- Ability to read papers and synthesize information across computer science, statistics, optimization, and related fields.
- Ingenuity in applying scientific theory to practical applications