Software Engineer, Data Platform Lead (Horizon AI)

STN Inc; Oregon; Hybrid; USD 150,000 - 210,000; Full time

Job summary

Horizon AI is seeking a Data Platform Lead to own the fleet data model and scalable access patterns. You will shape how we store, index, and query telemetry from a large GPU fleet, enabling agents and operators to reason about state with confidence.

You will collaborate with platform and product teams to build a robust, evolving schema, instrument decision capture, and ensure data access remains reliable as we scale.

Qualifications

• Strong data modelling judgment for infrastructure domains.
• Experience with time-series and event data at scale.
• Deep PostgreSQL knowledge, including vector search.
• Bias towards shipping a usable model early over perfect design.

Responsibilities

• Design and build the fleet knowledge model: assets, topology, configuration, and current state, queryable by an agent in one call.
• Build the decision-capture pipeline, and instrument the operator workflow so we accumulate labelled human decisions from day one.
• Own the data access layer that abstracts telemetry sources, so we can keep consuming today and build our own collection if that stops scaling.
• Negotiate our requirements with the teams who own the telemetry: signals, granularity, retention, raw event access.
• Own the scale path, including vector index behaviour and the analytics separation a growing schema will eventually need.

Skills

Data modeling

Time-series scale

PostgreSQL & vector search

Infrastructure literacy

Shipping mindset

Tools

DCIM tooling

Job description

Data Platform Lead

Horizon AI is building the operating system for how organizations run themselves. The first step is autonomous data centers. A fleet of AI agents senses the state of STN's GPU infrastructure, reasons about what it finds, and increasingly acts on it. This is running in production against real fleet data, not a prototype. Agents already take action where the risk is bounded and the outcome is verifiable. Everything riskier arrives as a recommendation for a human, and the work is moving decisions from the second category into the first, one class at a time, on evidence rather than optimism. Data centers are the proving ground, not the destination. We start here because infrastructure is measurable and unforgiving: an agent is either right, or the fleet tells you it was wrong. What we build here generalizes, and we know where it goes next.

Level: Principal (MTS 5) · Reports to: Chief AI Officer

The problems you inherit

• Fleet state is scattered across a relational store, a DCIM system, and multiple time-series and monitoring systems. Each has its own query path. There is no unified model. No decision-capture pipeline exists. We record what agents did, but not what operators saw, concluded, and chose. That record is the evidence base for granting agents wider autonomy.
• Our retrieval pipeline is document-oriented rather than fleet-state-oriented.
• We consume telemetry from systems other teams operate, where retention and granularity are tuned for dashboards rather than for learning from history.

You will

• Design and build the fleet knowledge model: assets, topology, configuration, and current state, queryable by an agent in one call rather than five.
• Build the decision-capture pipeline, and instrument the operator workflow so we accumulate labelled human decisions from day one.
• Own the data access layer that abstracts telemetry sources, so we can keep consuming today and build our own collection if that stops scaling.
• Negotiate our requirements with the teams who own the telemetry: signals, granularity, retention, raw event access.
• Own the scale path, including vector index behaviour and the analytics separation a growing schema will eventually need.

You have

• Strong data modelling judgment, particularly modelling physical or infrastructure domains as something queryable.
• Experience with time-series and event data at scale, including cardinality and retention trade-offs.
• Depth in PostgreSQL, including vector search, and comfort with a large evolving schema - Enough infrastructure literacy to know what a fleet actually is: nodes, GPUs, fabric, schedulers, power. Familiarity with DCIM tooling is a strong plus.
• A bias toward shipping a usable model early over designing a complete one.

Nice to have

GPU fleet or HPC exposure, DCGM, Slurm, entity resolution across inconsistent sources