# GNC Software Engineer

**Company**: Anduril
**Location**: Waltham, Massachusetts
**Experience**: senior
**Job type**: full-time
**Salary**: $146,000-$194,000 USD
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/andurilindustries/jobs/5178086007?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_1a328ad0-c99

## Description

## Job Overview

We're seeking a highly skilled GNC Software Engineer to join our Battlespace Awareness Command and Control Software team at Anduril. You'll play a key role in developing cutting-edge algorithms and intelligent software integrations for mission-critical applications.

## About the Team

Our team specializes in solving complex, real-world problems through innovative software solutions. We focus on delivering advanced technologies with real-world impact, particularly in path planning, route optimization, and autonomous mission execution.

## Responsibilities

- Define and influence the direction of a small team within the Command and Control Software organization, focusing on path planning and route optimization.

- Prototype and deploy state-of-the-art algorithms for global and local path planning, trajectory optimization, and multi-agent coordination within command and control systems.

- Develop high-performance software for real-time C2 systems, including operator-facing mission planning tools and autonomous tasking and routing services.

- Design and implement robust planners, search algorithms, and decision-making systems for safe, feasible, and optimal routes in dynamic environments.

- Analyze system performance using high-fidelity simulations, modeling tools, and statistical techniques to validate planner behavior across diverse mission scenarios.

- Drive customer success by customizing algorithms and software for mission-critical use cases.

- Integrate path planning technologies into the broader command and control software stack and development lifecycle.

- Translate technical progress into clear insights for diverse stakeholders.

## Required Qualifications

- Proficiency in algorithm design, software development, and mathematical modeling with expertise in C/C++, Python, and Matlab.

- Strong knowledge of path planning techniques, such as graph-based search, sampling-based planners, and lattice or grid-based planners.

- Demonstrated experience in guidance, navigation, and control (GNC); trajectory optimization; and/or predictive models for vehicle dynamics and weapon system effectiveness.

- Solid understanding of applied mathematics, including linear algebra, optimization, computational geometry, and graph theory.

- Knowledge of vehicle dynamics, kinematic constraints, and control theory as applied to autonomous platforms.

- Familiarity with map representations, terrain reasoning, and efficient handling of large-scale geospatial data within distributed C2 architectures.

- Background in machine learning as applied to planning and decision-making.

- Ability to engineer robust systems for planning under uncertainty and multi-agent coordination.

- Demonstrated ability to work across development lifecycles, from prototyping to optimizing production systems.

- Eligible to obtain and maintain an active U.S. Top Secret security clearance.

## Benefits

Anduril offers a comprehensive benefits package, including:

- Competitive salary

- Equity grants

- Top-tier benefits for full-time employees

## Salary Information

The salary range for this role is $146,000-$194,000 USD per year.

## Protecting Yourself from Recruitment Scams

Please be aware of potential recruitment scams and verify communications directly from Anduril.

## Skills

### Required
- algorithm design
- software development
- mathematical modeling
- C/C++
- Python
- Matlab
- path planning techniques
- guidance, navigation, and control (GNC)
- trajectory optimization
- applied mathematics
- linear algebra
- optimization
- computational geometry
- graph theory
- vehicle dynamics
- kinematic constraints
- control theory
- machine learning

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