Principal Localization Engineer – Location Intelligence

Hubble Network•San Francisco, CA

1d•Hybrid

About The Position

Hubble Network was founded with the intention of delivering on the promise of what Internet-of-Things (IoT) was supposed to be. We're building a global Bluetooth® network dedicated to machine-to-machine connectivity. We differentiate ourselves as the first modem-less and gateway-less, direct-to-satellite network from off-the-shelf Bluetooth® Low Energy chips. Hubble is ideal for applications in logistics, AgTech, and maritime where economies of scale for volume consumer and enterprise asset tracking is a priority. Our goal is to be the first billion-endpoint-connected network in the world. Hubble is an early-stage, venture-backed startup supported by some of the best investors in the world. In their previous lives, the founding team has been successful in raising $100s of millions in venture funding, developing the Amazon Sidewalk network, launching billions of dollars of space assets, and leading their teams to successful exits, both through acquisition and IPO. We are now looking to bring on talented team members who are the best at what they do to help us make Hubble a reality for the world. ABOUT THIS ROLE Hybrid role based in Seattle, WA or San Francisco, CA We're seeking a Principal Localization Engineer to lead the development of robust location computation systems leveraging Hubble's dual-stack BLE + satellite telemetry. This is a highly technical and strategic role focused on solving complex state estimation problems at scale. You'll work cross-functionally with signal processing experts, firmware engineers, and data infrastructure teams to push the limits of what's possible in dynamic, global-scale environments. This role has high visibility and direct impact on our core product performance and customer acquisition.

Requirements

7+ years of hands-on, industry experience in applied state estimation, robotics, or signal processing roles.
Deep expertise in state estimation and sensor fusion, with practical implementation of Kalman, EKF, or Particle filtering techniques.
Strong understanding of geolocation systems, signal propagation models, and statistical inference under uncertainty.
Proven track record of bringing algorithms from prototype to production in real-world, resource-constrained systems.
Strong foundational SQL skills (AWS Redshift, AWS Athena, or Trino/PrestoSQL) to pull and validate analytical datasets.
Strong Python programming skills, with emphasis on numerical methods, simulation, and performance optimization.
Experience working closely with data engineering teams or building pipelines to support analytical model development.
Strong communication skills and the ability to synthesize complex technical ideas for both technical and non-technical audiences.
All applicants must be U.S. citizens or lawful permanent residents (“green card holders”) as defined by ITAR (22 CFR §120.15).

Nice To Haves

Background in robotics localization, GN&C , autonomous systems perception, or RF geolocation is a strong plus.

Responsibilities

Architect and implement cutting-edge location estimation algorithms based on BLE and satellite data, including Kalman filters, Extended Kalman Filters (EKF), and Particle Filters.
Optimize for computational efficiency, real-world performance, and cost — balancing precision with system constraints.
Build and deploy production-grade statistical and probabilistic models for state tracking and error correction.
Partner with Data Engineering to define data collection, structuring, and processing pipelines that support robust modeling.
Evaluate and iterate on models based on live system performance and simulation feedback.
Collaborate with cross-functional stakeholders, including embedded systems and backend infrastructure teams, to ensure end-to-end feasibility and integration.