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Applied Science Manager

Unkliely AI

Unkliely AI

London, UK
Posted on Thursday, May 30, 2024
As an Applied Science Manager at Unlikely AI, you’ll be leading a team of 4 - 6 individuals, providing technical leadership and guidance and working the full project lifecycle: from literature to POC to production. You will be a strong mentor and coach, offering technical expertise to support team members in fostering their professional development and aligning with company goals.
You will play a key role in delivering products, starting with literature review, creating proof of concepts using cutting-edge technology including the latest Machine Learning techniques, collaborating with colleagues to implement and test your ideas, and helping deliver your solutions to production.
The role is not limited to a specific field, but our current areas of interest include NLP including NMT and Reinforcement Learning.
Unlikely AI is a deep tech startup working to create a world where highly intelligent automated systems enable humanity to flourish and benefit us all. We are pioneering transformative technology aimed at making Artificial Intelligence more accurate, trustworthy and safe. Based in London, the company was founded by William Tunstall-Pedoe, best known for his key role in the creation of Alexa following the acquisition of his first start-up by Amazon in 2012.
Please see our Company Principles to understand the core things we value – in particular, we are looking for exceptional people who are willing to tackle some of the most difficult technical problems there are, in order to create something extraordinary with huge impact.
As an Applied Scientist Lead working for Unlikely AI you will:
Pair program and mentor other Applied Scientists
Convert cutting edge research into real products
Design, build and experiment with cutting edge technologies
Drive the design, development, and execution of scientific research projects
Write software that is built and deployed in production systems
Communicate complex solutions to colleagues
Analyse & inspect large scale datasets
Required:
5+ years of hands-on experience in deep learning with 2 + years of experience managing technical teams.
Demonstrable project planning experience and leading on this within a fast-growing, deeply technical environment.
A clear track record of mentorship and coaching others
Deep knowledge of machine learning fundamentals
Strong coding skills in Python, including the use of pytorch or tensorflow
Enthusiasm to learn and get up to speed with cutting edge technologies which you may not already be deeply familiar with
Strong verbal and written communication skills
Cloud (AWS)
Data analysis skills
Desirable:
Experience utilising & deploying transformer models
Use of Python libraries that encourage best practice such as pytest, pylint, black etc
ML Ops
Docker, K8s
Start-up experience
Please note this role is not a pure research role and does not involve the creation of academic literature, but you should be very comfortable with reading and utilising academic papers and applying these concepts in your work.
Location:
We are currently operating a hybrid scheme with a small office near Holborn tube station available to anyone who wants to work there. We also have occasional team days where everyone meets face to face and days where people work heads down from home, communicating with colleagues using Slack and Zoom.
Compensation:
Compensation will be through salary and generous share options. The company has a tax-efficient EMI share option scheme set up (not available to larger companies) which allows us to provide real exposure to the success of the company without taxes being due when they are paid.
Equal Opportunities:
We are committed to having a truly diverse team where everyone is encouraged to be their authentic selves. We, therefore, do not discriminate in employment based on gender, race, religion, sexual orientation, national origin, political affiliation, disability, age, marital status, medical history, parental status or genetic information. Having a broad mix of people helps us to be the best we can.