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Deploying autonomous indoor farms outside every city on earth

Machine Learning Research Engineer

£40k – £75k • 0.0% – 1.0%
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We are a collection of engineers and scientists from Oxford, MIT and DeepMind, on a mission to grow safer, healthier food by deploying fully autonomous indoor farms outside every city on earth.

Our team includes a professor of control engineering, a research scientist who helped reduce the cooling bill of a Google data centre by 40%, and a farmer who started as a vegetable picker 40 years ago and now runs one of the most advanced indoor farming operations in the world.

We are backed by world-leading deep technology VC funds, including Founders Fund, who have backed companies such as SpaceX, Palantir and Square from the very start. We are well capitalised for the future having raised one of the largest seed rounds ever in Europe.


Machine Learning breakthroughs follow the availability of high-quality datasets, well-defined benchmarks and innovative algorithmic approaches.

You will be designing and developing our internal Kaggle/OpenAI Gym environments and creating predictive models within them, exploiting our deep, proprietary datasets to improve upon the metrics needed to grow safer, healthier food most efficiently.

You will be working in a multidisciplinary team across machine learning, reinforcement learning and software engineering on a wide variety of important problems.


- Design and develop data pipelines and predictive models to improve the performance of our AI agents.
- Improve our simulation, training and benchmarking environments (internal Kaggle/OpenAI Gym).
- Create visualisation and monitoring tools to ensure the integrity and performance of our datasets, environments and algorithms.
- Work with our professor-level research supervisors to analyse results and iterate approaches.
- Work closely with our reinforcement learning and software engineers.
- Continually learn and self-improve, helping others to do the same.


- Strong foundations in probability, statistics and linear algebra.
- Strong programming skills in python and its machine learning stack: numpy, pandas, matplotlib, tensorflow, pytorch etc.
- Strong data manipulation, exploration and visualisation skills.
- Experience working with real-world datasets.
- Ability to understand cutting-edge research papers.
- Knowledge or willingness to learn techniques from reinforcement learning to complement your skills.

Extra credit
- Experience with bayesian and frequentist approaches for time series modelling: GPs, RNNs, Bayesian NNs, etc.
- Experience deploying commercial machine learning models.
- Comfortable in a distributed linux-based environment.
- Extracurricular activities: contributing to the open-source community, side projects, coding competitions, etc.

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