Field Application Engineer

About the role

The will own the design and execution of on site solutions that translate advanced AI capabilities into reliable robotics deployments. You will serve as the primary technical interface between the engineering organization and customers who operate complex robotic systems in demanding environments. This position requires the ability to deconstruct intricate robotic behaviors into understandable and actionable insights for stakeholders with varying technical backgrounds. You will be responsible for validating that risk-aware and dependable AI systems function as intended when integrated with physical robotic platforms. A core part of this role involves documenting observed system performance and translating field data into improvements for the underlying AI models. You will lead controlled experiments to isolate edge cases and refine the interaction between perception, decision making, and motion in robotic workflows. Collaboration with research and product teams will ensure that real world observations directly inform the strategic direction of the AI systems roadmap. Ultimately, you will ensure that the deployed solutions meet stringent standards for safety, reliability, and performance in the Abilene test environment and beyond.

Key facts

What you'll do

Investigate the emergent behaviors of robotics systems to identify root causes of instability in AI driven control loops.

Design and implement test protocols that rigorously evaluate the robustness of transformer based models when exposed to noisy sensor inputs.

Partner with mechanical and electrical engineers to align the physical constraints of actuators with the expectations of high level AI policies.

Construct simulation environments that mirror real world operational conditions to validate algorithmic changes before field deployment.

Analyze telemetry data to detect patterns that indicate degradation in autonomous decision making over extended operational periods.

Champion the adoption of verifiable safety constraints that govern how risk is managed across multi robot operations.

Translate abstract research concepts in embodied intelligence into concrete integration steps for production grade robotic platforms.

Lead cross functional workshops where the team dissects complex challenges in robotics using data driven debugging methodologies.

Establish benchmarks for system performance by quantifying the reliability of AI modules under diverse and unpredictable scenarios.

Configure monitoring dashboards that provide the Field Ai team with real time visibility into the health of deployed AI systems.

Coordinate with customer success to ensure that stakeholders understand the capabilities and limitations of the implemented robotic solutions.

Perform root cause analysis on site when anomalies occur, leveraging a deep understanding of both software architecture and mechanical design.

Mentor junior engineers by providing detailed code reviews and guidance on best practices for building dependable AI enabled machines.

Document every investigation and modification to ensure that institutional knowledge is preserved and can be audited by regulatory parties.

Requirements

You must possess relevant experience in robotics and AI that demonstrates a clear understanding of how intelligent systems interact with physical hardware.

You must be able to work onsite in Abilene, TX for the duration of your employment with Field Ai.

You must show a track record of solving problems where theoretical models do not fully capture the behavior of real world systems.

You must be comfortable working with transformer based architectures and understand the implications of attention mechanisms on robotic decision making.

You must have the ability to read and interpret complex technical documentation to quickly master new libraries and frameworks.

You must be able to communicate intricate technical concepts to non technical audiences without sacrificing accuracy or nuance.

You must be disciplined in your approach to testing, ensuring that every hypothesis about system behavior is validated through repeatable experiments.

You must adhere to the ethical guidelines that govern the responsible deployment of AI in safety critical robotics applications.

Nice to have

Experience with embodied intelligence that allows you to anticipate how learning algorithms scale with real world sensory streams.

Knowledge of transformer based architectures that highlights how different model scales affect latency and reliability in field conditions.

Practical notes

Salary range dependent on experience.

Compensation package includes full benefits, equity, and generous time off.

Flexible working hours to support work-life balance.

Candidates must be able to work onsite in Abilene, TX.

Field Ai is an equal opportunity employer and encourages applicants from all backgrounds to apply.