Founding Machine Learning Engineer, Recommendations, GenAI

Job not on LinkedIn

🕒 July 29

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 19%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Gamingtec

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2013

🎲 Gambling

🤝 B2B

☁️ SaaS

Gambling • B2B • SaaS

Gamingtec is a B2B iGaming platform provider that offers turnkey and white-label software for online casinos and sportsbooks. The company supplies modular, scalable platform solutions—casino, sportsbook (including an AI-powered sportsbook), affiliate software, and agent management—designed for rapid market entry (6–8 weeks), multi-jurisdiction regulatory compliance, and extensive integrations (game providers, payments, PAM). Gamingtec supports numerous markets and currencies, reports large event coverage (50,000+ monthly live events, 40,000+ monthly pre-match events), and promotes partnerships with payment and anti-fraud providers. It positions itself as a partner to operators seeking to launch or scale online gambling brands.

📋 Description

• Design, build, and improve ML systems for recommendations, ranking, personalisation, retrieval, and GenAI workflows; • Turn product goals into concrete ML problems, evaluation plans, experiments, and shipped features; • Work with behavioural, transactional, contextual, and unstructured data to identify signals and improve model quality; • Build offline evaluation frameworks and online experiments to measure relevance, quality, latency, cost, and business impact; • Improve GenAI agent behaviour through better retrieval, context management, prompting, tool use, orchestration, and evaluation; • Investigate failure modes, run error analysis, and make practical tradeoffs across quality, reliability, speed, and complexity; • Partner closely with platform and backend engineers to deploy, monitor, and iterate on models in production; • Help define how the company does ML: metrics, experimentation discipline, technical standards, and long-term direction; • Work with real-time behavioural and transactional signals to improve recommendations, personalisation, and intelligent product behaviour; • Contribute to predictive and insight-driven ML use cases such as segmentation, churn prediction, recommendation measurement, and opportunity ranking; • Write clean, testable Python and contribute reusable ML components and shared libraries used across the platform.

🎯 Requirements

• Strong foundations in machine learning, statistics, computer science, or a similar quantitative discipline; • Experience building and shipping ML systems or intelligent product features in production or near-production environments; • Strong Python skills and comfort working across data, modelling, evaluation, and production collaboration; • Good understanding of experimentation, model evaluation, feature engineering, data quality, and error analysis; • Clear communication and the ability to work through messy, ambiguous product problems; • High ownership, self-direction, and a strong bias toward action; • 5+ years building and shipping ML systems or intelligent product features in production; • Strong understanding of model evaluation, cross-validation, feature engineering, and data quality challenges in real-world environments; • Experience working with large-scale behavioural, transactional, or contextual data; • Strong software engineering habits, including writing clean, testable, maintainable Python code. • Experience with recommendation systems, ranking, search, personalisation, or marketplace/feed optimisation; • Experience with LLM applications, RAG, GenAI agents, prompt iteration, or evaluation of GenAI systems; • Experience running A/B tests or online experiments; • Experience working closely with product teams and translating user problems into ML solutions; • Experience with real-time ML, streaming features, low-latency inference, or online learning; • Experience with causal inference, uplift modelling, multi-armed bandits, or other decision-optimisation methods; • Familiarity with cloud ML infrastructure, containerised deployment, and MLOps workflows; • Experience in iGaming, fintech, e-commerce, or another domain with large-scale transactional and behavioural data; • Experience with predictive analytics use cases such as segmentation, churn prevention, LTV modelling, or opportunity prioritisation.