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