Job Description .buttontext1967aeac1bf131b4 a{ border: 1px solid transparent; } .buttontext1967aeac1bf131b4 a: focus{ border: 1px dashed #757575 !important; outline: none !important; }
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Job Requisition ID: 47560
Job Closing Date: 06/07/2026
Cardiff, GBR, CF10 1FT
| Glasgow, GBR, G511DA
| Newcastle-upon-Tyne, GBR, NE991RN
| Salford, GBR, M50 2QH
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JOB DETAILS
JOB TITLE: Senior Software Engineer - Machine Learning Enablement
JOB REF: 47560
JOB BAND: D
CONTRACT TYPE: Full-time, Permanent
DEPARTMENT: Data Platforms
LOCATION: Salford Dock House (Primary), Glasgow Pacific Quay, Newcastle, Cardiff Central Square, Birmingham Mailbox (1 day a week hybrid)
PROPOSED SALARY RANGE: £60000 – £70000, depending on relevant skills, knowledge and experience. The expected salary range for this role reflects internal benchmarking and external market insights.
CLOSING DATE: 23:59 on Monday 6th of July 2026
We're happy to discuss flexible working. If you'd like to, please indicate your preference in the application – though there's no obligation to do so now. Flexible working will be part of the discussion at offer stage.
Interview Process
The selection process will consist of two stages:
Stage 1:
- A coding assessment designed to evaluate technical proficiency.
Final Stage: A comprehensive interview comprising:
- A technical presentation, and
- A competency-based interview focused on values and behaviours.
PURPOSE OF THE ROLE
As a Senior Software Engineer within the Machine Learning Enablement Team at the BBC, you will design and deliver the tools, platforms, and capabilities that empower data scientists and engineering teams across the organisation. Your work will enable scalable, high-quality machine learning workflows and help shape the BBC’s future through innovative technology solutions.
Why Join the Team
Join a forward-thinking engineering community at one of the world’s most respected media organisations. The Machine Learning Enablement Team sits at the forefront of technical innovation, building state-of-the-art systems that directly support the BBC’s global impact. You’ll contribute to a modern engineering culture rooted in collaboration, continuous learning, and craftsmanship—while developing tools used by millions of people worldwide. The BBC will support your growth with mentorship, learning opportunities, and exposure to cutting-edge technologies.
YOUR KEY RESPONSIBILITIES AND IMPACT:
- Design, build, and maintain tools, services, and infrastructure to support machine learning workflows.
- Apply strong engineering practices, including TDD, CI/CD, and clean software design principles.
- Mentor and support junior engineers, fostering a culture of continuous learning and excellence.
- Collaborate through pair programming and cross-functional engagement to deliver efficient, maintainable solutions.
- Contribute to architectural decisions, particularly involving data-driven technologies and system integrations.
- Conduct code reviews and implement robust testing strategies to ensure quality, security, reliability, and scalability.
- Build and maintain relationships with local engineering communities to support team growth and capability development.
YOUR SKILLS AND EXPERIENCE
ESSENTIAL:
1. Strong experience with modern Python development, including TDD and CI/CD pipelines.
2. Solid understanding of software engineering principles, design patterns, best practices including optimisation, monitoring, and security considerations.
3. Experience building cloud-based services with AWS (e.g., SageMaker, S3, VPC, KMS).
4. Experience with infrastructure-as-code tools such as AWS CDK or CloudFormation.
5. Experience developing and maintaining data or ML-focused pipelines
DESIRED:
1. Experience designing scalable architectures for data-driven products.
2. Engagement with wider engineering communities or networks to support recruitment and knowledge sharing.
3. Experience collaborating with cross-functional teams, particularly data scientists and product teams, to deliver end-to-end solutions.
4. Experience with containerisation and orchestration technologies.
5. Familiarity with statistical concepts and machine learning techniques or frameworks.
If you can bring some of these skills and experience, along with transferable strengths, we’d love to hear from you and encourage you to apply.
Before your start date, you may need to disclose any unspent convictions or police charges, in line with our Contracts of Employment policy. This allows us to discuss any support you may need and assess any risks. Failure to disclose may result in the withdrawal of your offer.
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Disclaimer
This job description is a written statement of the essential characteristics of the job, with its principal accountabilities, incorporating a note of the skills, knowledge and experience required for a satisfactory level of performance. This is not intended to be a complete, detailed account of all aspects of the duties involved.
Please note: If you were to be offered this role, the BBC will conduct Employment screening checks which include Reference checks; Eligibility to work checks; and if applicable to the role, Safeguarding and Adverse media/Social media checks. Any offer made is conditional on these checks being satisfactory.
Before your start date, you may need to disclose any unspent convictions or police charges, in line with our Recruitment policy. This allows us to discuss any support you may need and assess any risks. Failure to disclose may result in the withdrawal of your offer.
For any general queries, please contact: [email protected]
We are unable to accept applications via CV and only applications made online will be considered. Please click on the APPLY NOW button to proceed with your application.
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Redeployment
The BBC is committed to redeploying employees seeking suitable alternative employment within the BBC and they will be given priority consideration ahead of other applicants. Priority consideration means for those employees seeking redeployment their application will be considered alongside anyone else at risk of redundancy, prior to any individuals being considered who are not at risk.
