6 months ago
Remote, WorldwideSenior
Responsibilities
- Design, build, and improve ML systems for recommendations, ranking, personalization, retrieval, predictive analytics, and GenAI workflows.
- Translate product goals into ML problems, evaluation plans, experiments, and production features.
- Work with behavioral, transactional, contextual, and unstructured data to identify signals and improve model quality.
- Build offline evaluation frameworks and online experiments measuring relevance, quality, latency, cost, and business impact.
- Improve GenAI agent behavior through retrieval, context management, prompting, tool use, orchestration, and evaluation.
- Investigate failure modes, perform error analysis, and make practical tradeoffs across quality, reliability, speed, and complexity.
- Partner with platform and backend engineers to deploy, monitor, and iterate on models in production.
- Help define ML metrics, experimentation practices, technical standards, and long-term direction.
- Write clean, testable Python and contribute reusable ML components and shared libraries.
- Drive meaningful improvements to production recommendations, personalization, or GenAI workflows during the first six months.
Requirements
- Strong foundations in machine learning, statistics, computer science, or a similar quantitative discipline.
- 5+ years of experience building and shipping ML systems or intelligent product features in production or near-production environments.
- Strong Python skills and experience working across data, modeling, evaluation, and production collaboration.
- Strong understanding of experimentation, model evaluation, cross-validation, feature engineering, and real-world data quality challenges.
- Experience working with large-scale behavioral, transactional, or contextual data.
- Strong software engineering habits, including clean, testable, and maintainable Python code.
- Clear communication, high ownership, self-direction, and ability to work through ambiguous product problems.
- Experience with recommendation systems, ranking, search, personalization, marketplace or feed optimization is advantageous.
- Experience with LLM applications, RAG, GenAI agents, prompt iteration, or GenAI system evaluation is advantageous.
- Experience with A/B tests, online experiments, product collaboration, real-time ML, streaming features, low-latency inference, or online learning is advantageous.
- Experience with causal inference, uplift modeling, multi-armed bandits, predictive analytics, or decision optimization is advantageous.
- Familiarity with cloud ML infrastructure, containerized deployment, and MLOps workflows is advantageous.
- Experience in iGaming, fintech, e-commerce, or another domain with large-scale transactional and behavioral data is advantageous.
Tech Stack
Categories
About Gamingtec
Gamingtec builds B2B iGaming software for online casino and sportsbook operators, offering turnkey and white-label platforms with PAM, affiliate management, CRM, and APIs. The London‑headquartered company, founded in 2013, licenses its technology to startups and established brands operating in regulated and emerging markets. Its modular infrastructure supports rapid launches and scaling across web and mobile, including casino content aggregation and sportsbook management.