Remote Senior Data Engineer: ML Pipelines & AI

Float; New York (NY); Remote; USD 150,000 - 198,000; Full time

Benefits offered by this job

Remote-first

Global team

Job summary

Float's Intelligence team is building the AI layer that makes resource management smarter: recommendations that reflect how your team actually works, scheduling that optimizes for real constraints, and insights that surface before it's too late to act. To get there, we need data pipelines that are production-grade and ML-ready.

You'll be the engineer who builds and owns the pipelines that Intelligence features depend on, keeps the warehouse healthy and well-understood, and grows alongside us as

Qualifications

• Applied ML/AI in production: shipped an ML/AI feature used by customers.
• Pipeline ownership and debugging: you own pipelines end-to-end and understand ELT/ETL.
• Feature delivery: data work feeds customer-facing features.
• TypeScript/Node.js: production-grade TS codebases.
• SQL and databases: complex analytical SQL and warehouse costs.
• Software engineering practices: design before coding, tests, reviews.
• Documentation and written communication: documentation as part of work.
• Warehouse modeling: dbt and cloud warehouses (BigQuery or Snowflake).
• Optimization and scheduling: Timefold and OR-Tools are nice to have.

Responsibilities

• Intelligence pipeline ownership: end-to-end ownership of data pipelines powering Intelligence features.
• Applied ML in production: contribute to AI features and reliable data infrastructure.
• Pipeline health and maintenance: ensure pipelines and data infra are reliable and documented.
• Event-driven architecture: explore event streaming (Kafka/Debezium) and architectural decisions.
• Scheduling optimization work: develop familiarity with Timefold and the scheduling domain.
• Cross-team data impact: improve workflows beyond the Intelligence team.

Skills

Applied ML/AI in production

Pipeline ownership

Feature delivery

TypeScript/Node.js

SQL & databases

Software engineering practices

Documentation

Tools

Timefold

OR-Tools

Kafka / Debezium

dbt

BigQuery

Snowflake

MongoDB

Job description

Float's Intelligence team is building the AI layer that makes resource management smarter: recommendations that reflect how your team actually works, scheduling that optimizes for real constraints, and insights that surface before it's too late to act. To get there, we need data pipelines that are production-grade and ML-ready.

You'll be the engineer who builds and owns the pipelines that Intelligence features depend on, keeps the warehouse healthy and well-understood, and grows alongside us as