Staff Software Engineer, Data & Machine Learning

Linktree; City Of London; Hybrid; GBP 70,000 - 90,000; Full time

Job summary

A leading tech company in London is looking for a Staff Software Engineer to develop and implement ML-powered applications. You will be responsible for the full lifecycle from data pipelines to production-ready features. Ideal candidates should have a strong software engineering background, familiarity with machine learning frameworks, and a commitment to an inclusive workplace. The position offers a hybrid work model with 3 days in the office.

Qualifications

• Experience in building and maintaining production systems.
• Hands-on experience with machine learning frameworks.
• Solid understanding of data engineering practices.

Responsibilities

• Implement, test, and scale various ML and AI capabilities.
• Own the end-to-end development cycle of ML-powered features.
• Collaborate with cross-functional teams to integrate ML solutions.

Skills

Software engineering background

Machine learning frameworks

Data engineering practices

Cloud infrastructure

Prototyping and experimentation

Education

Computer science or related STEM background

Tools

PyTorch

TensorFlow

scikit-learn

MLOps tools (Airflow, MLflow)

Job description

The Role

We’re looking for a Staff Software Engineer who thrives at the intersection of software engineering and applied ML. This role is ideal for engineers who enjoy building real products — not just models — and want to take machine learning systems all the way from data to production. You’ll own the full lifecycle of ML-powered applications: designing data pipelines, building training workflows, integrating models into services, and deploying production-ready features that power delightful user experiences.

You’ll help us test new ideas quickly, by working in lean, build-measure-learn cycles. You’ll develop rapid prototypes, test them on real users, and iterate based on learnings and user-feedback.

No hands-on AI/ML experience, no problem! Do you excel at solving end-to-end software problems but don’t have hands-on experience with AI/ML? Do you have a computer science, statistics or other relevant STEM background? If so we'd love for you to learn the ML part on the job.

You’ll work in London, with 3 days per week from our office and 2 days per week from home.

What You’ll Own

• You’ll implement, test, and scale a wide variety of ML and AI capabilities, leveraging both established tools and emerging technologies;
• You’ll own the end-to-end development cycle of ML-powered features, including assessing the impact through means of experimentation
• You’ll collaborate with other cross-functional teams to integrate ML solutions into our products;
• You’ll monitor, evaluate, and improve the performance of models and ML-powered features in production;
• You’ll stay up-to-date with the latest developments in ML and AI, to ensure we’re always operating at the cutting-edge;

Who We’re Looking For

• Strong software engineering background with experience in building and maintaining production systems.
• Hands-on experience with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and MLOps tools (e.g., Airflow, MLflow, Vertex AI, or similar).
• Solid understanding of data engineering practices, including ETL, batch/streaming pipelines, and data quality monitoring.
• Familiarity with cloud infrastructure (AWS, GCP, Azure) and containerization.
• You have experience with shortening “build-measure-learn” by means of prototyping and experimentation to iterate quickly and build software users love.

At Linktree, we believe in promoting a culture that celebrates unique backgrounds, talents, and experiences, and we’re proud to be an equal opportunity workplace. We are creating an inclusive workplace where every individual feels valued, respected, and has equal opportunities to thrive. We aim to foster a diverse and inclusive environment where all team members have a sense of belonging. Linktree welcomes all people regardless of sex, gender identity, race, ethnicity, disability, pregnancy, age, or other lived experience.