At a Glance
- Tasks: Lead and innovate in ML software engineering, guiding teams and shaping technical vision.
- Company: Join a forward-thinking company at the forefront of machine learning technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with a focus on collaboration and continuous improvement.
- Why this job: Make a real impact by driving cutting-edge ML solutions and mentoring future tech leaders.
- Qualifications: Proven experience in ML software engineering and strong leadership skills.
The predicted salary is between 80000 - 100000 £ per year.
ML Software Engineering Lead We are seeking an experienced and visionary ML Software Engineering Lead to serve as the technical and functional leader for the Data Science Enablement engineering function, which owns the production development and ongoing operations of high‑profile ML products.
The role balances strategic leadership with hands‑on technical contribution, guiding the technical vision and participating in architecture, design and code reviews.
Responsibilities Strategy and Vision Define the technical vision and strategy for ML software engineering initiatives, aligning them with business goals.
Develop scalable capabilities to power real‑time decisioning engines throughout the payment lifecycle and beyond.
Enable rapid experimentation while ensuring robust, scalable and secure deployment of ML solutions.
Engineering and Operational Excellence Establish and evolve engineering standards, operating practices and technical governance.
Mentor engineers, provide technical coaching and promote technical excellence.
Champion collaboration, continuous improvement and knowledge sharing.
Drive alignment across teams through technical influence, architectural guidance and shared engineering standards.
Identify capability gaps and drive improvements to tooling, automation, observability and operational processes.
Establish operational standards for production ML systems, including reliability objectives, observability, incident management and support processes.
Technical Leadership and Contribution Guide the architecture, implementation, deployment and operation of ML products and reusable components.
Ensure systems and components meet requirements for scalability, latency, explainability and regulatory compliance.
Establish and promote best practices for ML software engineering; stay abreast of industry trends and emerging technologies.
Contribute to QA and code as needed.
Cross‑Functional Collaboration Partner closely with research‑focused data science teams, business stakeholders, infrastructure support teams, data engineering teams, security/compliance teams to incorporate ML into products and systems.
Collaborate with other data science and engineering leaders to establish an operating model for machine learning R
