As a Machine Learning Engineer at Synthesized, you'll work alongside (thoughtful and nice) machine learning and statistics PhDs from the University of Cambridge and world class software engineers. You'll be tasked with developing machine learning techniques and applying them at scale to our projects. You'll interact with customers on a regular basis.
You should be motivated by a desire to solve the most important problems and obtain unprecedented results, rather than producing “yet another paper”. You should be eager to push your architectures to their maximal performance, rather than being satisfied with toy tasks and proofs of concept.
- Develop new and improved methods for generative modelling, unsupervised learning and metalearning.
- Run machine learning tests and experiments
- Perform statistical analysis and fine-tuning using test results
- Extend existing ML libraries and frameworks
- Good knowledge of probability, statistics and algorithms
- 2+ years of experience in creating high-performance implementations of machine learning algorithms
- Good knowledge of data structures, data modeling and software architecture
- Proficiency with machine learning frameworks (like Keras or Tensorflow) and libraries (like scikit-learn)
- Past experience in developing data software products (optional)
- Track record of coming up with new ideas in machine learning, as demonstrated by one or more publications or projects (optional)
Remote and opportunity to join us in our high-tech office in the heart of London’s tech scene in Shoreditch (only following government COVID guidance)
Personal development plans (coaching, courses, events
- ) Generous cash compensation and options Snacks and drinks provided weekly Working alongside great people in a friendly and respectful environment
- Flexible work hours Company events
- and international trips!
We are committed to an inclusive and diverse Synthesized. Synthesized is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.