• Own and continuously improve Eneba's Featured Offers pricing algorithm — from model design through experimentation to production monitoring. • Build and iterate on willingness-to-pay and price elasticity models using behavioural signals: purchase history, browsing patterns, session data, price sensitivity indicators. • Collaborate with Product and Marketing/Growth to define pricing strategies for promotional campaigns and featured placements. • Define and track evaluation metrics connecting model output to business KPIs — revenue per session, conversion rate, margin, promotional ROI. • Work with Data Platform and Backend Engineering to ship pricing models as low-latency APIs integrated into live marketplace surfaces. • Monitor deployed models for data drift, distribution shifts, and degradation; own observability and alerting. • Contribute pricing-relevant features to the feature store — user price sensitivity signals, historical purchase behaviour, category-level demand indicators.

• Analyse user behaviour data (purchase history, browsing patterns, game genre preferences, session signals) to identify high-value personalisation features. • Design, train, and iterate on recommendation models — from collaborative filtering and matrix factorisation to sequence-based and embedding-based approaches. • Build and maintain end-to-end training and serving pipelines in collaboration with data and backend engineers. • Define and track evaluation metrics — offline (precision@k, NDCG, coverage) and online (CTR, conversion, revenue per session) — tied directly to business KPIs. • Run rigorous A/B tests to benchmark new approaches against the current internal baseline. • Own monitoring and observability of deployed models: data drift, prediction distribution shifts, latency, degradation. • Contribute reusable user and item features to our feature store.

• Define architecture and long-range technical strategy for ML systems and enabling platforms (training/inference orchestration, feature foundations, serving patterns, interoperability layers) across the measurement ecosystem • Lead development and productionization of next-generation ML approaches for identity resolution, audience modeling, and cross-platform measurement; translate advanced algorithms into scalable, production-quality implementations • Guide major data engineering and platform investments required for ML: distributed processing patterns, data routing/storage strategies, data integrity controls, and automation across internal/external data sources • Establish enterprise-grade MLOps, governance, and assurance processes: CI/CD standards, automated evaluation, model versioning/registry practices, drift detection, monitoring, and operational excellence • Lead cross-organization technical decision-making: align stakeholders, define success metrics, and drive complex trade-offs to deliver durable, scalable ML solutions • Coach and develop senior technical talent: mentor Staff/Senior engineers, set engineering standards, and build communities of practice • Champion privacy, security, and responsible AI: ensure privacy-by-design, PII safeguards, and audit readiness (GDPR/CCPA) for ML and data workflows

Role Description Plugging into an LLM API is not the same as building an AI system that works in production under real-world pressure. The gap between those two keeps widening, and the engineers who can bridge it are becoming the most valuable people on any team. These are the people who understand model behaviour, evaluate rigorously, and design systems where AI is a load-bearing component. We're not looking for engineers who have just discovered GenAI. We're looking for engineers whose craft is AI: who reason about model behaviour the way strong software engineers reason about distributed systems, who build evaluations before they build features, and who can tell you exactly why a model is failing and how to fix it. This role is for the engineers our clients rely on when the stakes are highest: when the AI has to actually work, not just demo well. Concretely, you will: - Design and build production AI systems where model behaviour is load-bearing: retrieval architectures, agentic workflows, multi-model orchestration, structured generation pipelines. - Own the evaluation methodology end-to-end. Define what "good" means for a given system, build regression suites, run offline and online experiments, and track drift and degradation once it's in production. - Make model and architecture decisions with real trade-offs in mind (latency, cost, accuracy, data sensitivity, operational complexity). - Debug AI systems at the model level: prompt behaviour, retrieval failure modes, tokenisation edge cases, and generation drift. - Contribute to or drive LLMOps infrastructure: inference serving, caching, monitoring, experimentation, and cost control. - Translate ambiguous business problems into concrete AI system designs, with clear reasoning about what's feasible today versus what's still speculative. - Stay at the frontier of AI/ML research: read papers, evaluate techniques critically, and bring what's genuinely useful into practice. - Use AI coding tools (Claude Code, Cursor, Copilot, or similar) as a natural part of your workflow. Qualifications - 3+ years as a software engineer and/or at least 2+ years of substantial production work on AI/ML or GenAI systems. - Deep understanding of modern AI systems: LLMs, embeddings, vector search, retrieval pipelines, agentic architectures, evaluation frameworks, and production observability for AI. - Working knowledge of classical ML fundamentals: supervised learning, evaluation metrics, overfitting, data distribution shifts. - Strong engineering fundamentals in Python (primary), ideally with working comfort in TypeScript/Node.js. - Proven ability to design and own evaluation methodology, not just use existing frameworks. - Can reason about model behaviour at depth: why a system failed, how to measure improvement, how to build systems that degrade gracefully. - Comfortable at the boundary between research and production. - High agency and technical leadership. - Pragmatic about when AI is the right tool, and when it isn't. - Low ego, high standards. Requirements - Hands-on experience with fine-tuning, LoRA/QLoRA, DPO, or similar adaptation techniques. - Has built evaluation harnesses from scratch: LLM-as-judge pipelines, regression suites, red-teaming setups, or human-in-the-loop eval workflows. - Experience deploying AI systems under serious latency or cost constraints. - Contributions to or deep familiarity with the open-source ML ecosystem. - Experience with the full LLMOps lifecycle: model routing, inference serving, caching, monitoring for drift, cost attribution. - Can discuss specific papers with informed opinions. - Has worked on classical ML problems and can speak to what changes with GenAI and what doesn't. - Active contributor to the AI tooling or research community. - Has shipped real products using AI-assisted coding workflows. Benefits - Problems worth your depth: work on AI systems that are load-bearing, not decorative. - Access to frontier AI as part of your daily work: latest models, open-source weights, and tooling available without an approval gauntlet. - A team that engages at depth: peers to push back on you, and engineers to mentor. - Agile Company Culture and the Best Team. - Global Projects & Opportunities. - Social Events & Team Building. - Continuous Development: Training & Development, Growth Opportunities. - Flexible Working: Remote Friendly Culture. - Great Equipment & Tools. - Flexible Benefits. - Extra Days Off. - Health Insurance. Hiring Process Our process is direct and designed to respect your time: - PX interview - Technical interview - Final conversation - Offer The technical interview focuses on real problems, not algorithmic puzzles. We evaluate candidates holistically. If you don't meet every requirement listed above, apply anyway. We care more about how you think and work than whether your CV matches a checklist. See you on the other side!