Platform / Infrastructure Engineer

OverviewMiraxis is building the rights-cleared data factory for robotics and physical AI. The Engineering/Platform team builds the foundation that lets us ingest, validate, transform, and deliver multimodal datasets reliably-without scaling headcount linearly.As Platform / Infrastructure Engineer, you will be a core builder alongside Arsen, owning the reliability and velocity of our systems: environments, deployment, observability, security, and the platform primitives that data/pipeline work depends on. This role is about making it easy (and safe) for the rest of the team to ship.Miraxis was founded by Artem Sokolov, who is also the Founder and CEO of Humanoid AI.

What you'll do (Responsibilities)Build and operate the platform foundation: services, environments, deployments, and internal tooling that support ingestion/QA/delivery workflows.Own day-2 reliability: monitoring/alerting, incident response patterns, SLO thinking, and operational hygiene.Establish pragmatic infrastructure standards: CI/CD, secrets, access controls, and cost controls that support rapid iteration.Provide clear interfaces and primitives for pipeline teams (storage conventions, job orchestration patterns, schema/versioning support).Improve developer experience for a remote/hybrid team: reproducible local/dev setups, fast feedback loops, and well-maintained runbooks.Partner with Product and Data Pipeline Engineering to translate requirements into scalable technical architecture.What we're looking forStrong platform/infrastructure engineering background (cloud, distributed systems, reliability, and developer experience).Comfortable owning systems end-to-end: design, build, deploy, operate, and improve.Pragmatic early-stage mindset: can choose good defaults quickly and evolve them as we learn.Strong written communication and comfort collaborating in a remote/hybrid environment.Nice to haveExperience with data-heavy systems (large files, batch processing, multimodal pipelines).Familiarity with security/compliance considerations around data access and rights management.Experience building orchestration primitives (workers, queues, schedulers) and observability for data pipelines.Working style & expectationsRemote-friendly with high autonomy and high ownership.Bias for simple systems that work, with incremental hardening.LocationRemote-friendly.