C++ Software Engineer - Mission Systems
Applied IntuitionWashington, District of Columbia, United States$150k – $200kPosted 2 April 2026
Job Description
About Applied Intuition
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co .
We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.
About the Role
Applied is seeking a Software Engineer with strong foundations in algorithmic optimization and multi-agent systems. You’ll tackle complex computational challenges in our real-time decision-making platform, designing and implementing advanced algorithms that push the boundaries of autonomous coordination and planning. We seek motivated engineers driven by core mission values to join our Acuity Autonomous Behaviors and Agents engineering team focused on next-generation human-machine teaming and real-time decision-making systems. You’ll help build intelligent agents that plan and execute missions using advanced reasoning, planning, and optimization algorithms. Our work spans real-time scenarios, heterogeneous platforms (air, space, land, surface, sub-surface), and software that integrates seamlessly with human operators. If you thrive in fast-moving, mission-oriented environments and love building autonomy that help our warfighters, this is your role.
Your work will support our Axion and Acuity family of systems, enabling breakthrough capabilities in human-machine teaming across all domains. If you’re passionate about applying cutting-edge algorithms to solve mission-critical coordination problems, this role offers exceptional opportunities to advance the state of the art in autonomous decision making systems.
At Applied, you will:
Design and implement multi-agent optimization algorithms for distributed decision-making and coordination problems
Develop temporal logic frameworks and constraint-satisfaction algorithms for complex planning scenarios
Create mathematical models for sequential decision process under uncertainty with probabilistic outcomes
Build computational frameworks that balance algorithmic efficiency with human-interpretable reasoning
Implement and evaluate quantitative metrics for algorithm performance, solution quality, and computational efficiency
Translate theoretical approaches into production-ready software implementations with rigorous testing methodologies
Collaborate with cross-disciplinary teams to refine algorithmic approaches based on empirical evaluation
Communicate technical details effectively across stakeholders, sponsors, and leadership
We’re looking for someone who has:
BS or MS in Computer Science, Software Engineering, Robotics, or a related technical field, OR 5+ years of professional experience developing intelligent agents or planning systems.
5+ years of hands-on experience developing performant modern C++ (we use C++ 20).
Strong theoretical understanding and practical experience implementing algorithms for constraint satisfaction, scheduling, and optimization problems.
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