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$123.5K

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How much do autonomous systems engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for autonomous systems engineer in Maryland is $123,467.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,100.00 and $152,400.00 per year, depending on experience, location, and employer.

What does an autonomous systems engineer do?

An Autonomous Systems Engineer designs, develops, and optimizes self-governing systems that operate with minimal human intervention. This includes robotics, self-driving vehicles, drones, and AI-driven automation. They work with sensors, machine learning, control systems, and software to ensure reliable decision-making and adaptability in dynamic environments. Their role often involves testing, simulation, and integration of hardware and software components to improve system efficiency and safety.

What are the key skills and qualifications needed to thrive as an autonomous systems engineer?

To thrive as an Autonomous Systems Engineer, you need strong expertise in robotics, control systems, sensor integration, and proficiency in languages such as Python or C++, often supported by a relevant engineering degree. Familiarity with simulation tools like ROS (Robot Operating System), MATLAB/Simulink, and experience deploying algorithms on embedded hardware are typically required. Effective problem-solving, strong teamwork, and clear communication are standout soft skills in this field. These competencies are crucial for designing reliable autonomous solutions, troubleshooting complex issues, and collaborating across multidisciplinary teams for successful system deployment.

What are some common challenges faced by autonomous systems engineers and how are they addressed?

Autonomous Systems Engineers often encounter challenges such as integrating diverse hardware and software components, achieving robust sensor fusion, and ensuring system reliability in unpredictable environments. Addressing these challenges typically involves rigorous simulation testing, iterative algorithm development, and close collaboration with cross-functional teams in software, hardware, and systems engineering. Engineers must also stay updated with advancements in machine learning and robotics to continually refine and enhance system capabilities. Employers support these efforts through ongoing training, collaborative problem-solving sessions, and investment in advanced prototyping resources.

What are the most commonly searched types of Autonomous Systems Engineer jobs in Maryland?

The most popular types of Autonomous Systems Engineer jobs in Maryland are:

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For Autonomous Systems Engineer jobs in Maryland, the most frequently searched job titles are:

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Autonomous Systems Engineer jobs near you
Infographic showing various Autonomous Systems Engineer job openings in Maryland as of August 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution, with an average salary of $123,467 per year, or $59.4 per hour.

Autonomous Systems Engineer

Tata Technologies

Frederick, MD • On-site

Other

Posted 10 days ago

Job description

We are seeking a highly skilled Tech Lead with deep expertise in autonomous systems algorithm development and high-performance C++ implementation to lead the design, development, and deployment of advanced autonomy solutions for autonomous platforms. This role is strongly implementation-driven, requiring hands-on experience in C++ for real-time, safety-critical systems, combined with strong foundations in mathematics, estimation theory, control systems, and optimization.


Key Responsibilities:

  • Act as the technical authority for autonomy algorithm development in C++
  • Design and implement high-performance, real-time algorithms in C++ for perception, localization, sensor fusion, and control
  • Develop Kalman Filters, SLAM, and sensor fusion algorithms
  • Optimize algorithms for real-time embedded platforms
  • Drive motion planning algorithms (A*, RRT, optimization-based)
  • Perform algorithm validation using MIL, SIL, HIL
  • Define performance KPls such as latency, accuracy, and robustness
  • Integrate algorithms into embedded and ROS-based systems
  • Mentor teams and conduct algorithm and code reviews

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