Senior ML Engineer

Fractal; Greater London; On-site; GBP 120,000 - 170,000; Full time

Job summary

Fractal is seeking a seasoned ML engineer to design, train and optimise models focused on personalised recommendations and user experiences. You will build robust data pipelines, supervise production deployments and drive experiments to improve model performance.

Join a collaborative team at Fractal, leveraging cutting-edge ML frameworks and generative AI insights to power scalable solutions across enterprise partners.

Qualifications

• Experience across the ML model lifecycle from development to deployment and monitoring.
• Proven ability to build robust data pipelines and production ML systems.
• Strong programming skills in Python with ML libraries.

Responsibilities

• Design, train, and optimise ML models for personalization and recommendation tasks.
• Build and maintain scalable data pipelines for feature engineering and training.
• Deploy and supervise models in production ensuring high availability.
• Lead A/B tests and offline experiments to measure model improvements.
• Collaborate with cross-functional teams to align ML with business goals.
• Explore emerging research in ML and generative AI for production use.

Skills

ML frameworks

Model serving

Python

TensorFlow

PyTorch

Data pipelines

Real-time streaming

Recommendation systems

Generative AI

Communication skills

Tools

TFX

Kubeflow

TensorFlow Serving

Job description

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

What you'll be doing

• Model Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis.
• Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets.
• Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance.
• Experimentation: Lead the design and analysis of A/B tests and offline experiments to evaluate model efficacy and support continuous improvement.
• Cross-Functional Collaboration: Engage with multidisciplinary teams to align machine learning initiatives with business objectives and user needs.
• Research & Innovation: Evaluate emerging research in machine learning, deep learning, and personalisation for potential integration within existing systems.

What you'll bring

• Strong demonstrated experience using ML Training frameworks (mainly TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe).
• Demonstrated expertise in the full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance.
• Proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch).
• Experience with high-volume data processing and real-time streaming architectures.
• Strong understanding of recommendation system design and personalisation algorithms.
• Familiarity with Generative AI and its applications in production settings.
• Good communication and analytical problem-solving skills.