We are hiring ML scientists and engineers in SF, Helsinki, Berlin and worldwide to work with us on some of the hardest problems around. Applied math or physics background preferred.
You will join a seasoned team of recognized experts working on research and implementation for products and services used at scale by millions of daily users and some of the largest companies in the world.
Who are we? We've worked at companies like Apple, Google, Amazon, and Cloudera, studied at institutions like Stanford and MIT, and scaled some of the largest online services in the world.
We came together to work on hard problems because we care about making the world a better place. Improving machine intelligence and designing new approaches to protect privacy are two ways we attempt to achieve this goal.
We often publish our results at conferences like ICLR and CVPR, but are not simply a research lab. The work is directly inspired by real problems and does not end with toy datasets.
Our research often goes into production, and our production needs are extreme. Distributed systems expertise and familiarity with probabilistic data structures or tick data systems is ideal.
We are an employee-owned company with a strong focus on research, culture, and sustainable work-life balance.
Our tools and interests: multi-objective optimization, active learning, unsupervised & weakly supervised learning, anomaly detection, reinforcement learning, semantic image analysis, meta-learning, structure search, Python, TensorFlow, pyTorch, Keras, ONNX, OpenCL, Kubernetes.
COVID-19 Advisory: all 2020 and 2021 Fellowship and Internship positions will be remote.
We are now taking applications for our Winter 2020 and Spring 2021 Engineering and ML Fellows programs. You have the option of working from our San Francisco, Berlin, or Helsinki offices, or remotely.
Click on one of the links above. Feel free to include your CV, github, citizenship or visa status, and a brief cover letter. Please include: where you’d like to work, which role interests you, and why when applying