# Data Infra - Telemetry and Observability (IC) at Matter Intelligence > Onsite · United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Amatter-intelligence%3A525dcd05-7607-4cdc-b377-3e752dc08f05 (read 2026-11-11). ## Facts (observed) - Company: [Matter Intelligence](https://alderhq.app/companies/matter-intelligence) - Location text: San Francisco - Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco") - Posted: 2026-09-15 (58 days ago) - First seen by alder: 2026-11-07 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/matter-intelligence/525dcd05-7607-4cdc-b377-3e752dc08f05 ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 2141): p25 $161k, median $200k, p75 $240k ## Description (the employer's text) ### 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. ### Preferred Experience with MLOps, GPU workloads, model serving, agent observability, reinforcement-learning environments, mission telemetry, or scientific instrumentation. Experience operating high-throughput inference, streaming systems, geospatial pipelines, or mixed cloud and edge deployments. Experience with reliability platforms or observability products used by multiple engineering teams. Familiarity with aerospace, defense, regulated operations, or other settings requiring formal change control and incident evidence. ### What Success Looks Like Teams can correlate mission, data, infrastructure, model, environment, and agent behavior during normal operation and incidents. Alerts and service objectives identify actionable failures without obscuring scientific or operational context. Capacity, cost, version, and reliability signals support safe deployments and faster diagnosis across the AI stack. ### Location This role is based in San Francisco, CA, and requires onsite work. ### ITAR Requirements To comply with U.S. export regulations, applicants must be one of the following: A U.S. citizen or national A lawful permanent resident (green card holder) Eligible to obtain required authorizations from the U.S. Department of State --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Amatter-intelligence%3A525dcd05-7607-4cdc-b377-3e752dc08f05 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.