About the role

Role Overview

Engineering Manager for the Growth Data Platform (GDP) pod at Hinge Health in Bangalore. This is a pivot-point leadership role where you move beyond traditional software management to lead an AI-native, ML-driven growth engine. Your team owns the data pipelines, event streams, and the “Intelligence Layer” that powers marketing decisions and member engagement.

Responsibilities

Build the “Intelligence Layer”: architect a real-time decisioning layer that ingests ML signals and routes them instantly to execution platforms (e.g., Iterable).
Operationalize Growth ML Models: partner with Data Science to productionize high-value models (e.g., Direct Mail Propensity, Contextual Bandits), owning Phase 3 of the model lifecycle (hardening, serving, monitoring).
Transition to “Harness Engineering”: drive adoption of AI-native workflows (e.g., Cursor, Claude Code) so engineers build test harnesses, specifications, and safety rails enabling autonomous agent operation.
Guarantee Data Trust (“Glass Box” Observability): implement radical observability with automated data sentinels and contract tests to catch schema and freshness issues before campaigns are impacted.
Act as a strategic bridge between SF Product Strategy and technical execution in Bangalore.

Requirements (Basic Qualifications)

2+ years managing engineering teams; “player-coach” who builds a high-agency, One Team culture.
3+ years in data engineering with distributed processing frameworks such as PySpark/Databricks and SQL.
Experience with production data pipelines and data lifecycle management (orchestration, monitoring, operational excellence).

Preferred Qualifications

MLOps / Model Serving: lifecycle of data and models; Kafka, event-driven architectures; production ML concerns (latency, feature stores, drift monitoring).
AI-forward leadership: enthusiasm for AI-assisted engineering and agents as auditors/architects of AI-generated code.
Architectural rigor: ability to standardize and converge ad-hoc pipelines into robust event-driven architectures.
Operational excellence: SLOs, runbooks, incident management, reliability as a feature.
Marketing tech / CDPs: Iterable or Braze; Segment or Hightouch.
Experience with Contextual Bandits or experimentation frameworks.
Background in healthcare / HIPAA environments.

About Hinge Health

Hinge Health is a healthcare technology company focused on improving member outcomes through data-driven care experiences. It leverages analytics and machine learning to support growth and engagement while powering real-time member interactions. The role sits within its Growth Data Platform organization, combining data engineering, ML operations, and AI-assisted engineering.