Six Figure Work

Engineering Manager, State Estimation

Hayden AI

Company websitehayden.ai (Hayden AI, opens in new tab)

Base salary $240K  to $310K

Employer-published annual base salary, USD. Advertised base range in San Francisco, CA

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  • LocationSan Francisco, CA
  • WorkplaceHybrid
  • Status at last checkSource accepting applications when checked
  • Last checked (2 h ago) · observed since

About this job

About Us

At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.

From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.

Job Summary:

The Engineering Manager, State Estimation will lead a high-performing team focused on developing advanced mapping, localization, and SLAM (Simultaneous Localization and Mapping) solutions for embedded camera systems. You will oversee the research, development, and deployment of large-scale mapping and localization solutions, while shaping the technical roadmap and ensuring successful collaboration across device, cloud, and applied AI teams.

Responsibilities:

  • Build, mentor, and manage a team of engineers developing state-of-the-art mapping, localization, and SLAM algorithms.

  • Set technical direction and project priorities, aligning them with broader organizational goals.

  • Drive high-impact, cross-functional projects from conception through deployment, coordinating with research, hardware, and product teams.

  • Provide technical guidance on algorithm design, system architecture, and implementation, while ensuring best practices in software development and performance optimization.

  • Foster a culture of innovation, technical rigor, and collaboration across the team.

  • Develop and maintain project roadmaps, milestones, and technical documentation.

  • Partner with senior leadership to define long-term strategies for localization, state estimation, and multi-sensor calibration.

  • Review and approve designs, architectures, and implementations, ensuring technical excellence and scalability.

  • Lead recruitment, hiring, and performance management to grow a world-class engineering team.

Required Qualifications:

  • Bachelor of Science degree (M.S. or Ph.D. preferred) in Electrical and Computer Engineering, Robotics, Machine Learning, Computer Science, or a related field.

  • 10+ years of industry experience (8+ years with an M.S., 6+ years with a Ph.D.), including significant leadership or management responsibilities.

  • Proven experience leading engineering teams working on state estimation, localization, or SLAM technologies.

  • Strong foundation in C++ development, geometric computer vision, stochastic processes, and nonlinear/convex optimization.

  • Deep understanding of filtering algorithms (e.g., Kalman, particle filters) and optimization-based methods (e.g., nonlinear least squares).

  • Familiarity with camera geometry, structure from motion, multi-sensor fusion, factor graphs, and related state estimation concepts.

  • Strong track record of technical execution combined with leadership and team-building skills.

  • Exceptional communication skills, with the ability to collaborate effectively across engineering, research, and product functions.

  • Experience in technology transfer and delivering research innovations into production systems.

Preferred Qualifications:

  • Demonstrated success deploying SLAM/VIO estimators in real-world applications.

  • Experience with multi-sensor systems, including GPS, IMU, camera, and wheel odometry.

  • Exposure to integrating deep learning with classical state estimation approaches.

  • Strong record of published research or contributions to the robotics and computer vision community.

  • Experience scaling and leading multi-disciplinary engineering teams

Source: Hayden AI on Ashby · View original posting (opens in new tab)

Pay in context

This role's base floor ($240,000) is above 78% of the 1,470 Data & ML roles in California listed on this site; 5 others list the same floor. Their median floor is $190,000; the middle half runs from $160,000 to $229,900.

Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 6, 2026.

This range next to 6 other live Hayden AI roles with the nearest advertised base floor
Chart as a table
This range next to 6 other live Hayden AI roles with the nearest advertised base floor
RolePublished base
Engineering Manager, State Estimation (this listing)$240K - $310K
Staff Deep Learning Engineer$230,522 - $299,679
Staff Software Engineer$230,522 - $299,679
Engineering Manager, Cloud Infrastructure$223K - $290K
Principal Technical Program Manager, Hardware$213,743 - $277,866
Principal Technical Program Manager$213,743 - $277,866
Chief of Staff$212K - $276K

Same $100K to $400K scale as every card. All Hayden AI roles · How to read the bar

Observed changes

  1. First observed on the employer's board
  2. Began meeting the published-salary criteria
Observed since
Employer-stated posting date
2026-06-29
Where this pay comes from

Location as the employer wrote it: San Francisco HQ Office

USD 240,000 – 310,000 · 1 YEAR · Salary

Method
Structured pay field published by the employer
Checked
Rule set
us-base-v2

The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.

What we verified

  • The pay figures come from the employer's own listing, quoted exactly above.
  • The pay is an annual base salary in US dollars with a minimum of at least $100,000.
  • The role was publicly listed on the employer's job board when last checked.

We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.