machine learning engineer

The client is a publicly listed, global leader in creative effectiveness and marketing decision-making, transforming its ad testing methodology into an AI-powered prediction platform.

• Take ownership of the existing multimodal emotion prediction model: master its architecture and limitations, and drive accuracy improvements, particularly on underrepresented emotion classes;
• Design, train, and evaluate new models on the roadmap: brand fluency/recognition, emotional intensity, saliency, and social ad performance prediction;
• Bring experience-based judgment to model strategy: assess ideas quickly, select the highest-value experiments, and protect the team from costly dead ends in training time and GPU spend;
• Build and improve ML infrastructure: migrate training workloads to AWS SageMaker (or Lightning AI), establish proper dataset management, and move from aggregated data snapshots to respondent-level training data via direct database integration;
• Extend the models with new capabilities: speech understanding encoders, OCR, and LLM-based metadata feature extraction;
• Write production-quality, tested code within a modern CI/CD and AI-assisted development workflow;
• Actively share knowledge: pair with and coach internal engineers transitioning into ML, raising the team's overall competency so expertise is retained in-house;
• Work directly with the client's technology leadership on roadmap prioritization, evaluation frameworks, and platform architecture;
• Contribute to shaping an API-first SaaS platform built on top of the models.

• 5+ Years of hands-on ML engineering experience, including training and fine-tuning deep learning models end to end (beyond consuming pre-trained APIs or LLMs);
• Strong PyTorch expertise;
• Practical experience with multimodal architectures — video, audio, and fusion/ensemble models (e.g., VideoMAE, ViT, BEATs, HuBERT, CLIP-class encoders);
• Solid computer vision background and experience with video data pipelines (frame sampling, feature extraction and pre-caching, large-scale video datasets);
• Proven transfer learning and fine-tuning experience: selective layer unfreezing, handling class imbalance and label scarcity;
• MLOps skills: experiment tracking (Weights & Biases or similar), reproducible training pipelines, dataset versioning and management, cloud GPU training (AWS SageMaker, Lightning AI, or Azure ML);
• Strong software engineering fundamentals: Git workflows, CI/CD, automated testing, code review culture;
• Cost-aware experimentation mindset — able to evaluate ideas quickly, prioritize high-value directions, and stop dead-end experiments early;
• Individual contributor profile with a proven ability to mentor and upskill colleagues by example;
• Pragmatic, delivery-focused attitude and a genuine growth mindset;
• Excellent English communication skills; comfortable working directly with UK-based senior leadership;
• English B2;
• Nice to have: Affective computing / emotion recognition from video or audio, Audio ML: speech understanding, music and audio classification, Saliency prediction and visual attention modeling, OCR and on-screen text understanding, Using LLMs for automated feature extraction or labeling within ML pipelines, Background in AdTech, MarTech, media/creative analytics, or behavioral science, Experience migrating ML workloads between cloud providers (Azure → AWS), Familiarity with AI-assisted development workflows (Claude Code, Copilot, Cursor).

• Flexible working format - remote, office-based or flexible;
• A competitive salary and good compensation package;
• Personalized career growth;
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more);
• Active tech communities with regular knowledge sharing;
• Education reimbursement;
• Memorable anniversary presents;
• Corporate events and team buildings;
• Other location-specific benefits.