IR Data Platforms Lead Engineer (Hybrid: Denver)

Telemetry Today LLC

Denver (CO)

Hybrid

USD 216,000 - 239,000

Full time

28 hours ago

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Benefits offered by this job

Medical insurance

Three weeks vacation

Paid holidays

Unlimited sick time

Parental leave

Job summary

Muon Space is seeking a hands-on staff-level engineer to lead multi-quarter architectural initiatives for production data platforms at scale. You will drive design direction across IR data systems, focusing on pre-processing, routing, delivery orchestration, and geospatial data management, while enabling ML-driven products through robust MLOps foundations.

The role requires deep distributed data systems experience, strong communication, and a track record of coordinating across teams.

Qualifications

Bachelor’s degree with 10+ years or MS with 8+ years or PhD with 5+ years of software engineering experience.
Proven success as a technical leader on production data platforms at scale.
Experience designing and operating production data pipelines on AWS with a modern workflow orchestrator (e.g., Flyte, Airflow, Prefect, Argo, or equivalent).
Track record of leading multi-quarter architectural initiatives that spanned multiple services or teams.
Experience with latency optimization: measurement discipline, benchmarking, per-stage instrumentation, right-sizing, fan-out, caching, async I/O, and knowing when the answer is structural rather than algorithmic.

Responsibilities

Own key aspects of the architectural design for the multi-mission, multi-customer IR data systems in partnership with the Principal Software Engineer: ingest, pre-processing, orchestration, storage and catalog, delivery, and the platforms that run all of the above.
Lead the design and implementation of critical capabilities for Muon's IR missions from problem framing and design document through rollout, adoption, and operational hand-off.
Own key aspects of the technical roadmap to low-latency processing including: structural designs such as event-driven microservices, cloud infrastructure design and optimization, and algorithmic optimizations.
Set direction for geospatial data storage, cataloging, and serving at scale — including tradeoffs across formats, tile stores, STAC catalogs, bucket and IAM topology, and reprocessing / versioning strategy.
Design and build the MLOps foundations for derived IR products — model packaging and versioning, experiment tracking, GPU-backed training and inference, A/B evaluation harnesses, and reproducibility — so ML-driven products can move from research to production on a well-paved path.
Set software standards for the team — codebase organization, release engineering, CI, testing, documentation, code review, observability, and operational excellence — and raise the bar on how the team reasons about failure modes, immutability, idempotency, and reprocessing.
Provide technical leadership across functions: coordinate design and implementation with Product, Program Management, Business Development, and other Software teams; represent IR Data Products in cross-functional design reviews and design processes; and turn ambiguous requirements into concrete engineering plans.