Machine Learning Engineer

The Opportunity

This is a critical and exciting time at Enigma. Our customers consistently tell us that our data products create tremendous value and are deeply aligned with their most important workflows. As demand grows, we have an urgent opportunity to improve both the intelligence of our data and the systems through which customers access it.

We are looking for an experienced Senior/Staff Machine Learning Engineer to join our Match Team and help shape the next generation of Enigma’s customer-facing data products.

In this role, you will combine advanced statistical and machine learning research with the engineering systems required to power fast, relevant, and reliable search experiences at scale. This is a uniquely high-impact role sitting at the intersection of information retrieval, ranking systems, semantic search, distributed systems, and customer data delivery.

The Role

At the core of Enigma’s product is our data, which makes both data science and delivery systems central to what we build.

As a Senior/Staff ML Engineer on the Match Team, you will lead efforts that improve the relevance, latency, and scalability of our customer-facing data products. You’ll work across the full lifecycle: framing retrieval and ranking problems, developing models and experimentation strategies, evaluating results using real-world signals, and implementing high-throughput search and retrieval systems.

This role is ideal for someone who is excited by both hard ranking/search problems and the systems challenges of turning those solutions into low-latency, production-grade retrieval systems.

What You'll Do

• Develop innovative solutions to complex problems in information retrieval, ranking, semantic search, query understanding, and recommendation systems
• Build and optimize low-latency, high-throughput search APIs, indexing pipelines, and retrieval systems using Python, Typesense, and AWS
• Evaluate and evolve our search technology stack, driving technical design decisions across indexing strategies, retrieval architecture, and system performance tradeoffs
• Lead end-to-end work from research design through experimentation, productionization, and customer-facing delivery
• Design evaluation frameworks for measuring relevance, precision/recall, ranking quality, and user engagement signals
• Improve query understanding via techniques like embedding models, vector search, hybrid retrieval, and query rewriting
• Detect and investigate anomalies in search performance, ranking behavior, and data freshness, tracing issues to root cause
• Partner closely with Product, Engineering, and client stakeholders to improve search experience and discoverability