At a Glance
- Tasks: Design, build, and deploy machine learning software for real-world energy and environmental challenges.
- Company: Join Faculty, a leader in impactful AI solutions with a diverse team of experts.
- Benefits: Enjoy a dynamic work environment with opportunities for mentorship and professional growth.
- Why this job: Make a difference by solving high-impact problems while collaborating with brilliant minds.
- Qualifications: Experience in machine learning, cloud architecture, and software engineering best practices required.
- Other info: We value diversity and encourage applications from underrepresented groups in AI.
The predicted salary is between 48000 - 84000 £ per year.
This job is brought to you by Jobs/Redefined, the UK’s leading over-50s age inclusive jobs board.
About Faculty
At Faculty, we transform organisational performance through safe, impactful and human-centric AI.
With a decade of experience, we provide over 300 global customers with software, bespoke AI consultancy, and Fellows from our award-winning Fellowship programme.
Our expert team brings together leaders from across government, academia, and global tech giants to solve the biggest challenges in applied AI.
Should you join us, you’ll have the chance to work with and learn from some of the brilliant minds who are bringing Frontier AI to the frontlines of the world.
What You’ll Be Doing
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.
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.
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.
Who We’re Looking For
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;
- Think scientifically, even if you’re not a scientist;
- Are pragmatic and outcome-focused.
We believe diversity of individuals working together fosters diversity of thought, and this is the bedrock of true innovation. We recognise the potential for AI to evolve in ways that continue to serve only dominant populations; part of our commitment to countering this is to ensure we hire, and support, a diverse workforce to lead the ground-breaking work we do in applied AI. We encourage applications from all underrepresented groups in this field.
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;
- Understanding of the core concepts of probability and statistics;
- 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;
- Experience with software engineering best practices and developing applications in Python.
What we can offer you:
The Faculty team is diverse and distinctive, and we all come from different personal, professional, and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day.
Faculty is the professional challenge of a lifetime. You’ll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.
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Senior Software Engineer (Machine Learning) - Energy Transition and Environment employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Software Engineer (Machine Learning) - Energy Transition and Environment
✨Tip Number 1
Familiarize yourself with the specific machine learning frameworks mentioned in the job description, such as Scikit-learn, TensorFlow, and PyTorch. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your technical expertise during discussions.
✨Tip Number 2
Showcase your understanding of the full machine learning lifecycle by preparing examples of projects where you have successfully deployed models. Be ready to discuss the challenges you faced and how you overcame them, as this will highlight your problem-solving skills.
✨Tip Number 3
Since collaboration is key in this role, think about how you can demonstrate your ability to work in cross-functional teams. Prepare anecdotes that illustrate your experience working with engineers, data scientists, and product designers to deliver successful ML systems.
✨Tip Number 4
Emphasize your passion for ethical AI and diversity in tech during your conversations. Faculty values individuals who are committed to making a positive impact, so sharing your thoughts on these topics can help you stand out as a candidate who aligns with their mission.
We think you need these skills to ace Senior Software Engineer (Machine Learning) - Energy Transition and Environment
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Senior Software Engineer position. Tailor your application to highlight your relevant experience in machine learning, software engineering, and cloud architecture.
Showcase Your Skills: In your CV and cover letter, emphasize your technical skills related to machine learning frameworks like Scikit-learn, TensorFlow, or PyTorch. Mention any experience with Docker, Kubernetes, and cloud services (AWS, GCP, Azure) to demonstrate your fit for the role.
Align with Company Values: Faculty values diversity, innovation, and a human-centric approach to AI. Make sure to reflect these values in your application by discussing how your personal and professional experiences align with their mission and principles.
Craft a Compelling Cover Letter: Write a cover letter that not only outlines your qualifications but also tells a story about your passion for machine learning and its applications in solving real-world problems. Highlight specific projects or experiences that showcase your problem-solving abilities and leadership skills.
How to prepare for a job interview at Faculty
✨Showcase Your Machine Learning Knowledge
Be prepared to discuss your understanding of the full machine learning lifecycle. Highlight your experience with frameworks like Scikit-learn, TensorFlow, or PyTorch, and be ready to explain how you've deployed models in real-world scenarios.
✨Demonstrate Your Problem-Solving Skills
Faculty values innovative thinkers. Prepare examples of how you've approached complex problems in the past, especially in the context of machine learning and AI. Show your ability to think scientifically and pragmatically.
✨Communicate Effectively
Outstanding verbal and written communication skills are crucial. Practice explaining technical concepts in a way that non-technical stakeholders can understand. This will demonstrate your ability to bridge the gap between technical and non-technical teams.
✨Emphasize Team Collaboration
Since the role involves working in cross-functional teams, be ready to discuss your experience collaborating with engineers, data scientists, and product managers. Share specific examples of how you contributed to team success and mentored junior engineers.