Machine Learning Engineer (12-month FTC)

Are you ready to revolutionise the publishing industry with your expertise in machine learning?

Come join the Data Science and Analytics team at Penguin Random House! As a Machine Learning Engineer, youll be at the forefront of crafting innovative, data-driven solutions that ensure ethical and commercially responsible decisions for our diverse portfolio of books. Your work will directly impact how we understand and engage with our readers, helping us to publish books that resonate and make a difference.

In this role, you will be instrumental in building and maintaining the technical infrastructure that powers our machine learning initiatives. Youll architect and oversee our ML platforms and pipelines, from initial development environments to production deployments, ensuring robust and scalable solutions. Your role as a technical leader will involve implementing MLOps best practices, establishing efficient CI/CD workflows, and developing infrastructure-as-code solutions that enable our data scientists to experiment, iterate, and deploy models effectively. Your expertise in cloud architecture and containerisation will drive the optimisation of our infrastructure, ensuring cost-effective and sustainable practices that enhance the long-term maintainability of our machine learning systems.

Collaborating across the disciplines of data engineering, software engineering, and data science, you will play a crucial role in building, deploying, and maintaining our machine learning models in production. Your responsibility will extend to developing the practices, tools, and infrastructure that elevate our quality and scalability standards at Penguin Random House.

If you are passionate about pushing the boundaries of whats possible with data and technology, and are eager to contribute to a team that values both innovation and sustainability, this is the perfect opportunity for you. Join us and help shape the future of publishing.

Key responsibilities:

1. Work closely with data scientists to develop and deploy efficient, maintainable, scalable machine learning models in production.
2. Lead initiatives supporting the global data science community by structuring, monitoring and streamlining end-to-end ML processes and algorithm performance.
3. Drive strategic discussions on infrastructure development to support machine learning models.
4. Articulate the lifecycle of machine learning products through infrastructure-as-code.
5. Establish and follow best practice guidelines for delivering robust data engineering solutions.
6. Build reliable, scalable data and ML pipelines that allow our data scientists to bring models to production quickly and responsibly.
7. Collaborate with data scientists to implement model monitoring to ensure optimal performance and detect model drift.
8. Stay current with practical applications and implementation techniques in machine learning through hands-on experimentation and industry best practices.
9. Help the data science team to cultivate engineering knowledge and skills.
10. Drive and implement cost optimisation strategies through efficient cloud solutions, resource utilisation analysis, and budgeting frameworks.

What youll bring

Essential criteria:

1. Previous experience as a Machine Learning Engineer.
2. Broad knowledge of ML techniques and intuition for selecting appropriate solutions.
3. Expert Python user with knowledge of Python internals and performance characteristics.
4. Expert SQL user, capable of fashioning complex and efficient queries, and maintaining reliable and efficient ETL routines.
5. Proven track record in building and maintaining robust, efficient ML pipelines with strong error handling and observability.
6. Experience working with both quantitative and qualitative data, particularly unstructured text.
7. Demonstrated ability to manage priorities effectively in a hybrid working environment.
8. Strong analytical and problem-solving abilities, with attention to detail and quality.
9. Previous experience with cloud platforms (AWS/GCP/Azure) and infrastructure-as-code tools (Terraform/CloudFormation/Pulumi).
10. Proven track record of architecting and deploying scalable cloud-native solutions.

Preferred criteria:

1. Previous professional experience with AWS cloud services (especially EC2, ECR, Lambda, and S3).
2. Strong background in MLOps platforms such as Databricks, MLflow, AWS SageMaker, Azure ML Studio, or similar enterprise ML development environments.
3. Experience in Natural Language Processing (NLP) techniques and applications, including text classification, sentiment analysis, and language modeling.
4. Track record of reducing operational costs through efficient architecture design, resource scheduling, and automated cost management practices.
5. Close familiarity with key Python data science libraries (Pandas/Numpy, SKLearn, PyTorch).
6. Strong experience with CI/CD using Github Actions or GitLab CI/CD pipelines for automated testing, quality checks, and deployments.
7. Deep familiarity with Kubernetes and Docker for ML model deployment in containerised environments.
8. Strong grasp of computer science fundamentals, including algorithms and data structures.
9. Expertise in DBT for data transformation and analytics engineering tasks.
10. Strong communication skills, able to explain complex concepts to both technical and non-technical stakeholders.
11. Demonstrated ability to mentor and share technical knowledge with team members.

Application instructions

Please apply with your CV and cover letter outlining why you are the right candidate for the role by 11:59pm on Sunday 9th March.

Please ensure you include a cover letter, as it is a crucial part of our assessment process. The cover letter offers an opportunity to show how your experience and interests align with the role requirements. Typically, we expect the cover letter to be no more than one or two pages in length.

Salary

£75,000 - £80,000 dependent on how your skills and experience align to the role, plus bonus and benefits.

What you can expect from us

Our people are the heart of our business, and we work hard to support a culture of responsibility and recognition.

Our benefits include:

• Financial - income protection, life assurance, childcare allowance.
• Wellbeing - healthcare cash plan, critical illness cover, health checks.
• Lifestyle - enhanced parental leave, tech scheme, free and discounted books.

Hybrid working

While our offices across the UK are places to connect, collaborate and celebrate with colleagues, we recognise that flexibility around where you work is just as important. This is a hybrid role. Due to the nature of the contract, we would be seeking for the successful candidate to work on-site frequently. However, hybrid working arrangements can be discussed at offer stage.

About Penguin

Were the UKs largest publisher; made up of some 2,000 people and publishing over 1,500 books each year. Our doors are open to all kinds of talent. In a constantly evolving industry, we work hard to stretch the definition of the word publisher. Here, youll work with a breadth of talent who all play their part to make each of our books a success. Together, we make books for everyone because a book can change anyone.

The recruitment process

You can read about our recruitment process at https://www.penguinrandomhousecareers.co.uk/how-we-hire/

As a Disability Confident Committed organisation, we offer interviews to candidates with a disability who meet the essential criteria for the role, and opt-in on their application form. The essential criteria for this role are listed as part of the What youll bring section. There may be times when the volume of applications means we cannot take all eligible candidates to interview.

We encourage you to tell us about any reasonable adjustments you may need by emailing . Remember, you only need to share what you are comfortable to for us to support your request.

Please note, we are not able to accept agency CVs for this role. Any CVs sent speculatively will not be eligible for a fee.J-18808-Ljbffr