Senior Machine Learning Engineer (Multimodal Ai) (#5594)

Senior Machine Learning Engineer (Multimodal AI)Senior Machine Learning Engineer (Multimodal AI) (#***)European Union, UkraineWork type:Office/RemoteTechnical Level:SeniorJob Category:Software DevelopmentN-iX is looking for aSenior ML Engineerto 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 expertisePractical 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 scarcityMLOps 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 cultureCost-aware experimentation mindset - able to evaluate ideas quickly, prioritize high-value directions, and stop dead-end experiments earlyIndividual contributor profile with a proven ability to mentor and upskill colleagues by examplePragmatic, delivery-focused attitude and a genuine growth mindsetExcellent English communication skills; comfortable working directly with UK-based senior leadershipNice to Have:Affective computing / emotion recognition from video or audioAudio ML: speech understanding, music and audio classificationSaliency prediction and visual attention modelingOCR and on-screen text understandingUsing LLMs for automated feature extraction or labeling within ML pipelinesBackground in AdTech, MarTech, media/creative analytics, or behavioral scienceFamiliarity 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 classesDesign, train, and evaluate new models on the roadmap: brand fluency/recognition, emotional intensity, saliency, and social ad performance predictionBring 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 spendBuild 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 integrationExtend the models with new capabilities: speech understanding encoders, OCR, and LLM-based metadata feature extractionWrite production-quality, tested code within a modern CI/CD and AI-assisted development workflowActively share knowledge: pair with and coach internal engineers transitioning into ML, raising the team's overall competency so expertise is retained in-houseWork directly with the Client's technology leadership on roadmap prioritization, evaluation frameworks, and platform architectureContribute to shaping an API-first SaaS platform built on top of the modelsWe offer:Flexible working format - remote, office-based or flexibleA competitive salary and good compensation packageProfessional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)Active tech communities with regular knowledge sharingProject: Global biopharmaceutical company#J-*****-Ljbffr

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 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 Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more) Active tech communities with regular knowledge sharing Project: Global biopharmaceutical company #J-*****-Ljbffr

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.