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Machine Learning Engineer - Utilities


Help us use technology to make a big green dent in the universe!


Kraken powers some of the most innovative global developments in energy.


We’re a technology company focused on creating a smart, sustainable energy system. From optimising renewable generation, creating a more intelligent grid and enabling utilities to provide excellent customer experiences, our operating system for energy is transforming the industry around the world in a way that benefits everyone.


It’s a really exciting time in energy. Help us make a real impact on shaping a better, more sustainable future.


Kraken Utilities


Our tech platform ‘Kraken’ is already licensed to support 55 million customer accounts globally, and we aim to serve 100 million by 2027. Kraken is the most AI-driven, innovative, forward-thinking platform for energy management. From optimising resources to delivering cost-effective, exceptional customer experiences through advanced Customer Information Systems (CIS), billing, meter data management, CRM, and AI-driven communications. 


We’re now charging the Kraken platform to other utility industries (Water and Broadband) and have created a new team called - Kraken Utilities. Over the last 2 years we have built this team from scratch to re-architect, design, and develop our Kraken software platform to solve complex industry wide problems within the water and broadband sectors (such as customer experience & water leak detection).


The Kraken Utilities team is in a very exciting growth phase, and has already signed 4 clients; Severn Trent, Leep, Portsmouth Water, and Cuckoo. We are currently 90+ people (engineers, product, implementation, strategy) along with 1500+ people in the overall Kraken world.


Our team


All our technology is written and maintained by a multi-discipline engineering team of around 1500 people globally. Our engineers work in ‘super teams’ which are focused on key areas of our platform as well as other innovative products. This also includes server-side, client-side and mobile engineers working closely with UX experts, copywriters and designers. 

 

Teams are empowered to choose a way of working that works for them, often opting for a Kanban-like approach. Notion and Asana are then used to specify and manage work; Github, CircleCI and Terraform Enterprise as part of an immutable-infrastructure, continuous delivery pipeline; and Datadog, Sentry and Cloudwatch to measure performance and monitor production. 


We’re now charging Kraken to power utilities beyond energy (think water, broadband,..) and have created a new business Kraken Utilities. We are building an innovation (ML/Data/AI) team from scratch and are looking for a Machine Learning Engineer to join and help us scale, implement a good culture, and work on some really exciting tech challenges. 

 

You'll join a talented team working on research around how to improve water efficiency (e.g. using smart meter data to detect leakages, reduce water consumption etc.); improve customer support efficiency (e.g. using LLM agents to automate actions and replies etc.).

 

Some of our coding conventions are open-source

 

Kraken is a great place to learn, work with some talented engineers and level-up your skills.  

 

Our technology


Our Data/ML stack includes: Python, SQL, pandas, Numpy, AWS, PyTorch, TensorFlow, NLP.


On the server-side, we mainly use Python. Most of our websites are powered by Django, Django-REST-framework and GraphQL (Graphene).  

 

We use AWS heavily  as part of a continuous deployment pipeline. See, for example, Django, ELB health checks and continuous delivery. 

 

Client-side, we use React, htmx and SASS; our mobile apps are built using native code or React Native.


What you'll do:
  • Design, build, and deploy cutting-edge machine learning systems to address complex business challenges
  • Work collaboratively with cross-functional teams including product managers, software engineers, and other stakeholders to deliver exceptional ML products
  • Stay at the forefront of technology. Explore and evaluate new technologies to inspire the creation of new ML products and enhance existing ones
  • Conduct A/B experiments in collaboration with other teams, analyse results, and iterate to drive continuous improvement
  • Embrace ambiguity. As we scale, your role will evolve, and you should be comfortable adapting to changing responsibilities and focus areas.


What you'll need:
  • You have a proven track record with 4+ years of hands-on experience applying machine learning to real-world business challenges within an industry setting
  • A solid foundation in the fundamentals of machine learning is a must. You are proficient in exploratory data analysis, model selection, model pipeline development, and the end-to-end process of model deployment and monitoring
  • Demonstrated expertise in Python, SQL, common ML, DL and visualisation libraries (pandas, numpy, scikit-learn, tensorflow, pytorch, huggingface, matplotlib etc.), ETL and data modelling
  • Experienced in cloud technologies, preferably with AWS
  • Strong communication skills are essential. You can articulate complex technical concepts to a diverse range of stakeholders with clarity
  • Proficient in software engineering fundamentals including version control and CI/CD pipelines
  • Nice to have: experience building NLP products; Kubernetes; dbt


Why you'll love it here:
  • Wondering what the salary for this role is? Just ask us! On a call with one of our recruiters it's something we always cover as we genuinely want to match your experience with the correct salary. The reason why we don't advertise is because we honestly have a degree of flexibility and would never want salary to be a reason why someone doesn't apply to Kraken - what's more important to us is finding the right Kraken-fit!
  • Kraken has an unique culture. An organisation where people learn, decide, and build quicker. Where people work with autonomy, alongside a wide range of amazing co-owners, on projects that break new ground. We want your hard work to be rewarded with perks you actually care about! Our Group CEO, Greg has recorded a podcast about our culture and how we empower our people 
  • Visit our perks hub - Kraken Employee Benefits


This team will require candidates to work on a hybrid remote basis, coming into our office in Oxford Circus 2 days a week. You do also need to be able to work in the UK.


We're very excited to be growing our team. We're looking for skills and experience to help shape and define the future of not only our team, but the wider business at a global scale. If you're reading this and grinning, please apply! There are huge challenges to tackle, and we need amazing people who are keen to get stuck in.


Are you ready for a career with us? We want to ensure you have all the tools and environment you need to unleash your potential. Need any specific accommodations? Whether you require specific accommodations or have a unique preference, let us know, and we'll do what we can to customise your interview process for comfort and maximum magic!


Studies have shown that some groups of people, like women, are less likely to apply to a role unless they meet 100% of the job requirements. Whoever you are, if you like one of our jobs, we encourage you to apply as you might just be the candidate we hire. Across Octopus, we're looking for genuinely decent people who are honest and empathetic. Our people are our strongest asset and the unique skills and perspectives people bring to the team are the driving force of our success. As an equal opportunity employer, we do not discriminate on the basis of any protected attribute. Our commitment is to provide equal opportunities, an inclusive work environment, and fairness for everyone.

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What You Should Know About Machine Learning Engineer - Utilities, Kraken

Join Kraken as a Machine Learning Engineer in London and embark on an exciting journey to help shape a smarter, more sustainable energy future! At Kraken, we're passionate about leveraging innovative technologies to create impactful solutions across utilities. Our state-of-the-art platform not only optimizes renewable energy generation but also enhances customer experiences in ways you've never imagined. As a vital part of our newly formed Kraken Utilities team, which aims to solve challenges in the water and broadband sectors, your expertise will play a key role in our mission to support millions of customer accounts worldwide. You'll collaborate with an amazing multi-disciplinary team, using cutting-edge tools such as Python, SQL, and various ML libraries like TensorFlow and PyTorch. Your work will encompass everything from designing and implementing machine learning systems to conducting A/B experiments that push the boundaries of what's possible. We’re looking for someone who's not just technically savvy but is also passionate about using technology to make a real difference. If you bring 4+ years of industry experience and thrive in an agile, innovative environment, you’ll fit right in at Kraken. Plus, our unique, flexible culture encourages you to learn, grow, and contribute with autonomy. Ready to dive in and be part of an incredible team that's making waves in the utility sector? We can’t wait to hear from you!

Frequently Asked Questions (FAQs) for Machine Learning Engineer - Utilities Role at Kraken
What are the responsibilities of a Machine Learning Engineer at Kraken?

As a Machine Learning Engineer at Kraken, your primary responsibilities will include designing, building, and deploying machine learning systems that address complex business challenges in the utilities sector. You'll work closely with cross-functional teams, collaborate in A/B experimentation, and stay updated with the latest technologies to innovate our ML products.

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What qualifications do I need to apply for the Machine Learning Engineer position at Kraken?

To be considered for the Machine Learning Engineer role at Kraken, you should have a solid foundation in machine learning, with 4+ years of hands-on experience. Familiarity with Python, SQL, and major machine learning libraries is essential, alongside strong communication skills and experience in cloud technologies, particularly AWS.

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What technologies will I work with as a Machine Learning Engineer at Kraken?

At Kraken, Machine Learning Engineers will work with a diverse tech stack that includes Python, SQL, and libraries such as Pandas, Numpy, TensorFlow, and PyTorch. You'll also be involved in using cloud platforms like AWS and various tools for continuous delivery and monitoring.

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What type of projects will a Machine Learning Engineer tackle at Kraken?

In the role of Machine Learning Engineer at Kraken, you will engage in exciting projects aimed at improving water efficiency through smart meter data analysis and enhancing customer support efficiency utilizing LLM agents. You'll also have the opportunity to develop innovative ML products in the growing utilities sector.

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What is the work culture like for a Machine Learning Engineer at Kraken?

Kraken fosters a unique culture where autonomy and innovation thrive. As a Machine Learning Engineer, you’ll work in an agile environment that encourages learning, embraces flexibility, and allows you to contribute significantly to groundbreaking projects in the utilities space.

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Common Interview Questions for Machine Learning Engineer - Utilities
Can you explain your experience with machine learning models and their deployment?

When answering this question, highlight your specific experiences with various model types, the challenges faced during deployment, and the technologies used, such as Docker or Kubernetes, to facilitate continuous integration and deployment.

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Describe your familiarity with Python in machine learning applications.

Discuss your proficiency in Python, particularly focusing on libraries you've used, such as Pandas and Scikit-learn, and provide examples of projects where Python was instrumental in developing ML models.

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How do you approach exploratory data analysis?

Detail your process for exploratory data analysis, emphasizing the tools and techniques you employ to uncover patterns or anomalies in data which inform the model development process. Be prepared to discuss specific instances from previous projects.

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What are some challenges you have faced when working with ML algorithms?

Talk about specific challenges, such as data quality issues or model overfitting, and explain the solutions you implemented to overcome these challenges, showcasing your problem-solving skills.

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Can you provide an example of how you’ve collaborated with cross-functional teams?

Illustrate your experience working with product managers and engineers on previous projects. Highlight how this collaboration contributed to successful outcomes and the importance of clear communication and teamwork.

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Have you worked with AWS or any cloud platforms for ML deployment?

If you have cloud experience, describe specific services you utilized for machine learning tasks, like S3 for storage or SageMaker for model training, emphasizing how cloud computing enhanced your workflow.

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What strategies do you use to evaluate machine learning models?

Explain the metrics you prefer, such as precision, recall, or RMSE, and discuss how you validate models using techniques like cross-validation and test/train splits to ensure accuracy and effectiveness.

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How do you stay updated with the latest developments in machine learning?

Discuss the resources you use for keeping your skills sharp, such as attending conferences, participating in online courses, and following thought leaders in the ML community. Mentioning practical applications of new techniques is a plus.

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What is your experience with natural language processing (NLP)?

Share any relevant projects involving NLP, discussing the libraries and methodologies you employed, such as using NLP for customer support efficiency, and illustrate the impact of these solutions.

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Describe a time when you had to adapt to a significant change in project scope.

Present a specific example where project scope changed unexpectedly and how you navigated that change, focusing on your adaptability and the strategies you utilized to meet the new goals while maintaining team morale.

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DATE POSTED
December 12, 2024

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