Staff Software Engineer, IR Data Products

Muon Space•Denver, CO

2d•Hybrid

About The Position

Muon Space is building and operating a growing portfolio of infrared (IR) space missions that generate mission-critical Earth observation data for a diverse and expanding set of customers. These missions include the FireSat constellation, which delivers calibrated IR imagery and derived wildfire products to wildfire operators, the scientific community, the US Government, and commercial partners; and the Space Based Environmental Monitoring (SBEM) mission, which provides data for weather and cloud characterization, situational awareness, and a broad range of additional use cases. Muon's IR Data Products team owns the multi-mission, multi-customer software systems that process, store, and disseminate the data from these missions — turning raw downlink into calibrated imagery and derived products, and delivering those products reliably and at low latency to customers at scale. This role is an architecture and technical-leadership seat for the Software focus area of the IR Data Products team. The engineer will own architecture and cross-team execution across defined IR data-system domains, translating longer-term technical direction into multi-quarter designs and delivered production systems. This role will lead multi-quarter initiatives to design and build scalable software architectures that make the system fast, cheap, reliable, evolvable, and ready for many-satellite scale. In this role, the candidate will work across four key areas: Architecture & implementation leadership for the next-year horizon: Own key aspects of the architectural direction for the data systems for Muon’s IR Missions — pre-processing, processing directives & routing, delivery orchestration, compliance evaluation, and the orchestration platform itself. Low-latency optimization & core infrastructure: Own key aspects of the technical roadmap to low-latency processing. Own structural designs such as event-driven microservices, cloud infrastructure design and optimization, and algorithmic optimizations. Geospatial tooling: Own key aspects of the architectural direction for how we store, catalog, serve, and version geospatial data (NetCDF, Zarr, HDF, STAC, tile stores) at scale. MLOps foundations: Build the production foundations for ML products — model packaging, experiment tracking, GPU-backed training and inference — that our derived-product work (hotspots, perimeters, fire behavior) will increasingly depend on. Additionally, the role will represent the team in cross-functional design reviews, drive prioritization syncs with Technical Program Managers and Business Development, and set software standards for the team’s codebase and development practices. This engineer will mentor senior and mid-level engineers across the team and raise the bar on how we reason about failure modes, immutability, idempotency, and reprocessing. The ideal candidate is a hands-on staff-level engineer with a track record of leading multi-quarter architectural initiatives on production data platforms at scale. They combine deep experience with distributed data systems and modern workflow orchestration with real geospatial and (ideally) MLOps depth, and they are as comfortable writing a design document and driving cross-team alignment as they are debugging a complex piece of algorithm code. This position is hybrid and requires working on-site in our Denver, CO office three days per week.

Requirements

Bachelor’s degree with 10+ years or MS with 8+ years or PhD with 5+ years of software engineering experience.
Demonstrated success as a technical leader on production data platforms at scale — with the designs and rollouts to show for it.
Demonstrated 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.
Strong operational mindset: observability, SLOs, on-call, safe deploys, hotfix protocols, immutability, and reproducibility.
Excellent technical writing and cross-functional communication — comfortable driving alignment across engineers, scientists, PMs, and adjacent platform / flight-software teams.

Nice To Haves

Experience with event-driven / microservices architectures: queues, event routing, backpressure, idempotency, retries, DLQs, and clear service contracts.
Familiarity with modern workflow platforms such as Union.ai / Flyte v2.
Terraform / IaC ownership of AWS resources, and hands-on experience shaping IAM boundaries between environments.
Prior work on geospatial / remote sensing systems at scale.
Fluent with the tradeoffs behind geospatial storage formats, tile stores, and STAC.
Hands-on MLOps experience: model packaging and versioning, experiment tracking (MLflow, W&B, or similar), GPU-backed training and inference, feature stores, A/B harnesses.
Experience with GPU acceleration for image processing (e.g., JAX, CuPy, CUDA) or ML inference at scale.
Experience in satellite ground-segment or a comparable domain where compliance, provenance, and data lineage are first-class concerns.
Experience introducing latency SLOs and benchmarking discipline to a team.
Prior experience serving imagery / tiled geospatial data to external customers or partner platforms.

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 (latency budgets, compliance constraints, new missions, new customers) into concrete engineering plans.