Senior Machine Learning Software Engineer, Faculty
Energy Transition and Environment
Salary not provided
AWS
Docker
Kubernetes
Python
Tensorflow
Azure
Scikit-Learn
PyTorch
Senior and Expert level
London
3+ days a week in office
Making AI real
Job no longer available
Making AI real
201-500 employees
B2BArtificial IntelligenceBig dataMachine Learning
Job no longer available
Salary not provided
AWS
Docker
Kubernetes
Python
Tensorflow
Azure
Scikit-Learn
PyTorch
Senior and Expert level
London
3+ days a week in office
201-500 employees
B2BArtificial IntelligenceBig dataMachine Learning
Company mission
To help organisations across the public and private sectors use AI to understand more deeply, make better decisions and act faster.
Role
Who you are
- At Faculty, your attitude and behaviour are just as important as your skills and experience. Our principles guide our day-to-day actions and we look for individuals who can demonstrate their alignment with these
- We like people who combine expertise and ambition with optimism -- who are interested in changing the world for the better -- and have the drive and focus to make it happen. If you’re a good fit for Faculty, you probably:
- Love finding new ways to solve old problems - when it comes to your work and professional development, you don’t believe in ‘good enough’. You always seek new ways to solve old challenges
- Think scientifically, even if you’re not a scientist - you test assumptions, seek evidence and are always looking for opportunities to improve the way we do things
- Are pragmatic and outcome-focused - you know how to balance the big picture with the little details and know a great idea is useless if it can’t be executed in the real world
- To succeed in this role, you’ll need the following - these are illustrative requirements and we don’t expect all applicants to have experience in everything (70% is a rough guide):
- Understanding of, and interest in, the full machine learning lifecycle, including deploying trained machine learning models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch
- Understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques
- Technical experience of cloud architecture, security, deployment, and open-source tools
- Demonstrable experience with containers and specifically Docker and Kubernetes
- Comfortable in a high-growth startup environment
- Outstanding verbal and written communication
- Excitement about working in a dynamic role with the autonomy and freedom you need to take ownership of problems and see them through to execution
- Have experience in working directly with clients and end users to conduct: Requirements Gathering, Technical Planning and Scoping
- Technical experience of cloud architecture, security, networking, deployment, and open-source tools ideally with one of the 3 major cloud providers (AWS, GPS or Azure)
- Experience with software engineering best practices and developing applications in Python
What the job involves
- You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning
- The work you do will help our customers solve a broad range of high-impact problems in the Energy Transition and Environment space - examples of which can be found here
- You are engineering-focused, with a keen interest and working knowledge of operationalised machine learning
- You have a desire to take cutting-edge ML applications into the real world
- You will develop new methodologies and champion best practices for managing AI systems deployed at scale, with regard to technical, ethical and practical requirements
- You will support both technical and non-technical stakeholders to deploy ML to solve real-world problems
- To enable this, we work in cross-functional teams with representation from commercial, data science, product management and design specialities to cover all aspects of AI product delivery
- The Machine Learning Engineering team is responsible for the engineering aspects of our customer delivery projects
- As a Senior Machine Learning Engineer, you’ll be essential to helping us achieve that goal by:
- Building software and infrastructure that leverages Machine Learning
- Creating reusable, scalable tools to enable better delivery of ML systems
- Working and mentoring data scientists and engineers to develop best practices and new technologies to deliver technically sophisticated, high-impact systems
- Implementing and developing Faculty’s view on what it means to operationalise ML software
- We’re a rapidly growing organisation, so roles are dynamic and subject to change
- Your role will evolve alongside business needs, but you can expect your key responsibilities to include:
- Leading on the scope and design of projects
- Offering leadership and management to more junior engineers on the team
- Providing technical expertise to our customers
- Technical Delivery: Work with cross-functional teams of engineers (Frontend & Cloud), data scientists, product designers and managers to deliver ML systems
- Translate user research outcomes into full system architecture that leverages Machine Learning
- Build software and infrastructure that leverages Machine Learning, and see it through to production
Application process
- Screening call with Talent Acquisition (30 mins)
- Pair Programming Interview (90 mins)
- System Design Interview (90 mins)
- Commercial Interview (60 mins)
Salary benchmarks
Our take
Faculty work with companies such as Samsung, the BBC, and Just Eat to figure out what they could do with AI, and then help them to do it through implementing the right skills, strategy and software.
The team have developed a platform with large cloud computing space for accessing large amounts of data usable to design and test AI models. As businesses realise the importance of using data to drive growth and stay competitive, Faculty have focused on making AI real through real world deployments and aim provide data to organisations so that they can make better decisions.
Faculty has worked with clients in a range of different industries, including healthcare, retail, government and energy. They are just one of the many companies utilising AI, but it’s their ability to work alongside organisations and apply the findings that will really help them to succeed.
Steph
Company Specialist at Welcome to the Jungle
Insights
Top investors
Few candidates hear
back within 2 weeks
36% female employees
19% employee growth in 12 months
Company
Employee endorsements
Autonomy
"People here get a lot of trust to figure things out. They also get the chance to earn a lot of responsibility at a relatively early stage in their..."
Funding (last 2 of 3 rounds)
May 2020
$37.4m
SERIES A
Nov 2019
$10.5m
SERIES A
Total funding: $49.8m
Company benefits
- Hybrid working: We believe people have needs, responsibilities and interests that require something different to a strict working day. We trust people to organise and take accountability for their own work and do our best to support their lives outside Faculty. We provide you with all you need to work from home, including a laptop, keyboard, chair…even Sony headphones!
- Equity, we want you to benefit from Faculty’s growth and success
- Unlimited holidays: We encourage each other to use this time to take a break, work on personal projects, or to spend time with their friends and family
- Fantastic private health, optical and dental cover, for you & your family - including 24/7 unlimited virtual private GP appointments and covering pre-existing medical conditions
- Access to mental health coaching with Sanctus
- Lunch weekly, and more fruit, drinks and snacks than you could ever eat (for office-based employees)
- We work hard and make sure we enjoy what we do. So we have frequent socials and informal get-togethers to help make sure you enjoy your time with us. You’ll make friends and professional connections that will last a lifetime
Company values
- Grow the Best Team - Only hire people you’re convinced will make Faculty better. Take others’ development as seriously as your own. Build strong and collaborative teams.
- Do Great Work - Master your craft. Take pride in your work, even when no one is watching. Demand the highest standards from yourself and others. Work hard, but do it sustainably.
- Be Humble & Kind - Recognise that you’re always learning, and have something to learn from everyone. Always make the decision that is best for faculty, but treat people kindly as you do it.
- Act with Integrity - Have the courage to maintain integrity even when it’s costly to do so. Demand this from others without hesitation.
- Seek Truth - Seek the truth over winning the argument. Understand the root cause. Create an atmosphere in which the best idea succeeds, regardless of who or where it comes from.
- Maximise Impact - Care about the impact of your work. Find the thing that is most important to your customer. Design the best solution, not just what’s easiest or fashionable. Get it done fast. Make sure nothing falls through the cracks.
Company HQ
Old Street, London, UK
Articles
Leadership
Marc Warner
(CEO)Previously a Marie Curie Fellow in Physics at Harvard University, specialising in quantum sensing
Andrew Brookes
(CTO)Previously a CTO at Hillgate and Vice President at BlackRock
Angie Ma
(Co-Founder)Previously a Scientific Consultant at Bio Nano Consulting
Diversity, Equity & Inclusion at Faculty
Angie Ma (Co-Founder)
- AI changes everything, and so must we to make sure our people reflect the real diversity of society. We believe that recruiting talent with diverse experiences, perspectives and backgrounds encourages people to think differently and be more creative
- We welcome difference whether it’s ethnicity, religion, age, gender, gender identity, sexual orientation or disability so please be yourself!
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