Full-Time

Principal Engineer

Autonomy

Updated on 8/23/2026

AeroVect

AeroVect

51-200 employees

Aviation tech software and digital solutions

Compensation Overview

$350k - $500k/yr

Seattle, WA, USA + 3 more

More locations: Toronto, ON, Canada | South San Francisco, CA, USA | New York, NY, USA

Hybrid

The posting lists hybrid arrangements for South San Francisco and Seattle, and remote arrangements tied to New York City and Toronto.

Category
Software Engineering (1)
Required Skills
Python
Distributed Systems
Neural Networks
Machine Learning
C/C++
DevOps

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Requirements
  • 15+ years of hands-on experience building production autonomy systems, with strong technical depth across multiple modules including localization, perception, prediction, planning, and controls.
  • A demonstrated track record of shipping autonomy components that have run in production on real vehicles at non-trivial scale, rather than only research prototypes or simulation results.
  • Prior experience as the most senior individual contributor in an autonomy organization, setting direction, mentoring staff and senior engineers, and partnering with engineering leadership without managing a team.
  • Deep technical depth in perception, prediction, or planning, ideally in more than one of these areas.
  • Strong software engineering fundamentals in C++ and Python.
  • Fluency with modern deep learning for autonomy, including training, evaluation, deployment, and lifecycle management of models for real-world use.
  • Experience working in or with ROS or ROS 2 and the distributed-systems realities of on-vehicle compute, including real-time constraints, inter-process communication, and fault containment.
  • Ability to execute by converting ambiguity into plans and plans into running code on a vehicle.
Responsibilities
  • Own the design and evolution of the perception stack, including detection, classification, tracking, and multi-modal sensor fusion across available modalities.
  • Drive perception robustness across the long tail of real-world operating conditions and set the direction for applying deep learning across the perception pipeline.
  • Own the prediction stack and design models for intent inference, behavior forecasting, occlusion handling, and edge cases.
  • Set the direction for prediction integration with perception upstream and planning downstream.
  • Own the planning and decision-making stack, from structured driving behaviors to domain-specific maneuvers for autonomous ground support equipment operations.
  • Set the direction for the use of learned components in the planner.
  • Set technical direction at the interfaces between the primary autonomy areas and the rest of the stack, partnering with senior autonomy engineers to maintain end-to-end system coherence.
  • Own the functional and software architecture of the autonomy stack and partner with neighboring teams on implementation.
  • Ship and review code for a safety-relevant system and deliver autonomy software running on vehicles.
Desired Qualifications
  • Experience with safety-critical or functional-safety-relevant systems, including ISO 26262, ISO 13849, SOTIF, or aerospace equivalents.
  • Experience operating in an Operational Design Domain involving heavy interaction with humans, mixed traffic, or unstructured environments.
  • Familiarity with simulation-driven verification and using simulation within a continuous integration and continuous delivery pipeline for autonomy.

AeroVect builds digital software solutions for the aviation industry. Its platform, based on JavaScript, is used to deliver web and mobile applications and data-driven tools for aviation customers such as airlines, airports, and related businesses. The product likely runs as a web-based service or subscription, enabling users to access, customize, and deploy aviation-focused software and analytics from a browser or app. Unlike general software firms, AeroVect targets aviation-specific workflows and data needs, leveraging a JavaScript-based platform to offer scalable digital products and possibly professional services like custom development or data analysis. The company’s goal is to help aviation stakeholders manage software, data, and operations more efficiently by providing practical digital solutions that can be deployed and consumed through a cloud-based platform.

Company Size

51-200

Company Stage

Seed

Total Funding

$9.1M

Headquarters

San Francisco, California

Founded

2020

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Simplify Jobs

Simplify's Take

What believers are saying

  • January 2026 LinkedIn posts describe record demand and expansion of facilities, team, and partnerships.
  • GAT partnership targets up to 50 tractors, signaling a commercial ramp.
  • dnata’s trial of up to 100 tractors validates demand from a global ground handler.

What critics are saying

  • Autonomy certification and airport safety approvals can stall deployments through 2026.
  • Customer concentration risk is high; GAT and dnata likely anchor near-term revenue.
  • Larger industrial autonomy players can undercut AeroVect on hardware, integration, and pricing.

What makes AeroVect unique

  • AeroVect’s AeroVect Driver is OEM-agnostic, built specifically for airport driving environments.
  • GAT and dnata partnerships target fleet-scale autonomous GSE across major airports.
  • The company claims trusted by the world’s largest airlines and airports in 2026.

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Benefits

Flexible Work Hours

Growth & Insights

Headcount

6 month growth

0%

1 year growth

-2%

2 year growth

-2%