Senior Machine Learning Engineer (AI)

N-iX is looking for a Senior ML Engineer to join our team. Our Client is a publicly listed, global leader in creative effectiveness and marketing decision-making, headquartered in the UK. For over two decades, the company has helped the world's leading advertisers predict and improve the commercial impact of their advertising using a proprietary methodology rooted in behavioral science — measuring audiences' instinctive emotional responses to creative content rather than relying on rational, questionnaire-driven analysis. Its effectiveness metrics, predicting both long-term brand growth and short-term sales impact, are independently validated and backed by one of the industry's largest databases of professionally tested ads. Project Description: The Client is transforming its human-panel ad testing methodology into an AI-powered prediction platform trained on 140K+ professionally surveyed ads already predicts human emotional responses to video ads. The roadmap includes brand recognition social ad scoring models, migration from Azure to AWS SageMaker, and an API-first SaaS platform, with a 6-12 month time to market. Requirements: 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 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) Responsibilities: 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 We offer*: 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 *not applicable for freelancers