Staff Machine Learning Engineer (iGaming)

TL;DR

Staff Machine Learning Engineer (ML/GenAI): Building and scaling production ML systems for real-time personalization and search in a high-scale igaming environment with an accent on ranking, retrieval, and LLM integration. Focus on designing multi-layer serving architectures and optimizing vector search for low-latency discovery experiences.

Location: Hybrid in New York City

Salary: $159,000 - $208,950 USD

Company

hirify.global is a premier mobile gaming company in the United States and Canada, operating leading sportsbooks and iGaming platforms.

What you will do

• Design and implement intelligent search systems incorporating vector search and ML personalization signals to optimize relevance and user experience.
• Build and scale multi-layer serving architectures for ML and GenAI/LLM models in ambiguous problem spaces.
• Drive the evolution of platform capabilities, including CLI, SDK, and infra automation, to streamline the ML application deployment lifecycle.
• Collaborate with Data Scientists and Analysts to productionize, analyze, and validate AI-powered insights.
• Set engineering standards and mentor junior engineers in system design, reliability, and operational excellence.
• Ensure data security, privacy (GDPR, CCPA), and governance are integrated into reliable data products.

Requirements

• 7+ years of experience developing code in Python, Java, or similar core languages.
• 4+ years of experience designing scalable software architectures for ML, Search, or LLM workloads.
• 2+ years of experience implementing vector search, semantic search, or RAG workflows using stores like AWS OpenSearch or Elasticsearch.
• Experience with Spark, Flink, Kafka, Airflow, and Terraform within cloud environments (AWS, GCP, or Azure).
• Proven ability to drive technical direction, make architectural trade-offs, and influence decisions across multiple engineering teams.
• Must be based in New York City for hybrid work.

Nice to have

• Experience with typeahead/autocomplete systems and integrating ML signals into ranking workflows.
• Experience combining outputs from multiple retrieval systems to improve relevance.
• Hands-on experience with ML frameworks (Scikit-learn, PyTorch, TensorFlow) and platforms (SageMaker, Bedrock, Databricks).

Culture & Benefits

• Comprehensive health plans including medical, vision, and dental insurance.
• 401(k) matching program up to 5%.
• Generous paid personal time off and 14 paid company holidays.
• Inclusive culture with well-defined skill-based career tracks for individual contributors and managers.
• Additional perks such as commuter benefits and pet insurance.