Data Infra - Telemetry and Observability (IC)

About the role

ABOUT MATTER INTELLIGENCE

Welcome to Matter, where we are building the future of vision AI: pairing a world-first sensor that sees molecular chemistry, temperature, and 3D shape with a Large World Model that will be the most powerful intelligence engine for the physical world. This system doesn't just see what something looks like; it understands everything from a single pixel. We call this Superintelligent Vision.

Our team has delivered technologies to Mars for NASA/JPL, designed advanced sensors for U.S. Defense, and frontier artificial intelligence systems. We are now building the next generation of space- and airborne-based sensing systems.

ABOUT THE ROLE

Matter is hiring a Telemetry and Observability Engineer to make datasets, training runs, models, environments, agents, and missions observable as one AI system. Reporting to Ignacio Cases Martin, this individual contributor will connect flight and mission reality with infrastructure, model, and product behavior through shared telemetry, traces, alerts, reliability mechanisms, and operational context.

KEY RESPONSIBILITIES

- Build a common telemetry model linking mission state, datasets, code, checkpoints, accelerators, model and prompt versions, environment episodes, agent steps, tools, decisions, cost, latency, and feedback.

- Instrument distributed training, data loading, GPU utilization, checkpointing, experiment health, batch and online inference, and model-serving behavior.

- Instrument agent workflows across prompts, context, retrieval, memory, planning, tool calls, graph state, human intervention, evidence, outcomes, and safety controls.

- Connect planned-versus-actual mission state, hardware context, time, geometry, and geolocation to relevant datasets and model results.

- Establish actionable service objectives, alerts, incident interfaces, rollout and rollback signals, and escalation paths across platform, model, environment, and agent failures.

- Build capacity and cost visibility across storage, processing, accelerators, training, evaluation, inference, retrieval, and agent execution.

QUALIFICATIONS

REQUIRED

- Experience with ML observability, telemetry, time-series systems, distributed training or inference, cloud infrastructure, or production AI platforms.

- Strong hands-on engineering skills in observability, automation, APIs, infrastructure, incident tooling, and systems debugging.

- Understanding of metrics, logs, traces, events, model and data monitoring, service objectives, capacity, release safety, and incident diagnosis.

- Ability to reason about AI failure modes including bad datasets, training instability, stale checkpoints, drift, version mismatch, environment bugs, and agent-tool failure.

- Experience designing telemetry schemas and correlation workflows across multiple systems, time domains, and ownership boundaries.