Overview
Monaco is building an AI-native revenue platform that replaces the fragmented GTM stack - CRM, sequencing, call recording, enrichment, pipeline management with one unified system,
Full job description
Monaco is building an AI-native revenue platform that replaces the fragmented GTM stack - CRM, sequencing, call recording, enrichment, pipeline management with one unified system, consolidating 6–10 disconnected tools into a single platform built for the AI era. We launched publicly in Feb 2026, are already 65 people, and have strong early product-market fit generating millions in ARR within months of launch. You'll have the chance to both scale our core systems and build new features from 0 to 1. We've raised $85M in our Series B from Founders Fund, Benchmark, and Human Capital, and our founders previously led Brex, Apollo, and Clari. Come join us if you want to be part of a high autonomy, high pace team reinventing one of the biggest categories in enterprise software. THE ROLE We're looking for a Data Platform Engineer to help build Monaco's data and ML platform - the pipelines, context systems, and infrastructure that power our AI-driven product. You'll work on the foundation that makes models, agents, and workflows actually useful in production. This is a high-ownership role at the intersection of data engineering, distributed systems, and applied AI. WHAT YOU'LL DO
- Build scalable pipelines and event-driven systems for ingesting, transforming, and serving data.
- Support ML workflows: training data, evaluation, embeddings, feature pipelines.
- Solve distributed systems challenges around reliability, latency, consistency, and scale.
- Improve observability, tooling, and developer experience for data, ML, and agent systems. WHAT YOU'LL BRING
- 5+ years building data platforms, ML infrastructure, or backend systems.
- Deep experience in technologies like PostgreSQL, Redis, Celery, Temporal, ElasticSearch or Turbopuffer, Kafka, Spark, Databricks or Snowflake, etc.
- Ability to lead major architecture decisions and execute on them fast, maintain and scale production systems through rapid workload growth. LOCATION
- San Francisco. We're an in-person team - 5 days in the office. At this stage, proximity genuinely accelerates product quality and team cohesion.
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