Manager, Data/ML Platform

Inspiren•New York, NY

2h•$200,000 - $230,000•Remote

About The Position

Data and Machine Learning are at the heart of what we do at Inspiren. Our ecosystem of smart, multi-sensor devices produces an enormous volume of raw timestream data across many modalities — and our ML systems are what turn that raw signal into valuable, real-time insights about residents, their health and safety, and care staff (including time-critical notifications for events like falls). We are seeking a seasoned Senior Manager to lead our Data + ML Platform group and evolve the core platform that powers all of this. You will lead a team of Data Engineers, Data Platform Engineers, and ML Ops Engineers to build the foundation that lets our ML engineers move from idea to production quickly and reliably, as well as the foundational data layer for internal company analytics and external data products. You will be a key technical leader, collaborating with cross-functional partners (Data Science, Product, Engineering, Hardware, Analytics) to design scalable systems, modernize legacy infrastructure, and lay the groundwork for the next generation of multimodal and LLM/VLM-powered capabilities. You will also play a key role in scaling the team, growing and upleveling engineers, and contributing to a culture of technical excellence and innovation.

Requirements

8+ years of professional experience in software or ML engineering, with significant time spent on data/ML infrastructure or platforms.
3+ years managing ML, Data Engineering, or platform engineering teams.
Proven experience building or scaling MLOps capabilities (e.g., model registry, feature stores, deployment/serving, drift monitoring, experiment tracking).
Experience building data platforms / data engineering systems at scale (streaming or large-scale batch pipelines).
Proven experience shipping and operating data and ML systems in production at scale.
Demonstrated ownership of the model lifecycle infrastructure: data, training, evaluation, deployment, monitoring, and iteration.
Demonstrated ability to grow people, uplevel teams, and manage performance with clear expectations and direct feedback.
Experience driving alignment and execution across Product, Engineering, and Data, with accountability for outcomes.
Excellent verbal and written communication skills, with the ability to convey complex ideas clearly to cross-functional audiences and connect platform work to business outcomes.
Proven ability to operate under ambiguity, prioritize effectively, and maintain momentum in a fast-moving environment.

Responsibilities

Lead Data + ML Platform strategy and execution
Own technical direction for the data and ML platform, spanning data engineering, data platform, and ML Ops.
Invest in ML Ops capabilities that accelerate ML enablement: offline experimentation, model registry, shadow deployments and testing, drift/product monitoring, online and offline feature stores, and training data management.
Build measurement and experimentation frameworks for LLM and agentic workflows.
Invest in data platform and data engineering capabilities to handle large-scale streaming and batch data across modalities.
Modularize and scale our system, increase reliability, and modernize legacy infrastructure.
Make it easy for ML engineers to have ideas, test them, shadow deploy, gather data, test online, and ship.
Set clear success criteria for platform capabilities and hold the team accountable to outcomes.
Build and scale the team
Anticipate skill gaps and build a deliberate hiring plan; consistently attract and retain strong data and ML platform engineers.
Coach with depth: set explicit expectations, give direct feedback, and accelerate individual growth.
Build an environment of ownership, trust, and continuous learning.
Develop future leaders and create succession within the team.
Partner cross-functionally
Work closely with Product, Hardware, Data Science, and Analytics to shape problem definition and roadmap.
Translate ambiguous business needs into concrete technical direction.
Influence hardware and device design decisions where they intersect with data and ML needs.
Contribute to company strategy at the intersection of data, ML, devices, and care outcomes.