Senior Simulation Software Engineer
At a glance
Senior Software Engineering role at PlusAI. Santa Clara · full-time · $130,000–$200,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Build AI-agent workflows that review simulation runs, find failures and likely causes, then give engineers useful next steps
- Create scalable simulation pipelines and metric evaluation, using road-test and simulation data to track trends and autonomy performance
- Compare simulated behavior with real-world results, then reduce gaps in sensors, actor behavior and system dynamics
- Improve platform speed, scale, testing and reliability, and work with engineering and data science teams to resolve road-test issues
- Help shape the system’s long-term architecture and keep technical work aligned with customer, regulatory and quality requirements
What you bring
- A relevant master’s degree plus at least three years of experience, or a PhD as a new graduate, in a listed technical field
- Hands-on experience creating LLM-based or agentic workflows for analysis, evaluation or automation
- Experience measuring or improving simulation-to-real-world fidelity, such as sensor models or scenario generation
- Proficiency in Python or C++, with experience building scalable data-processing systems or evaluation frameworks
Who this fits
This senior role suits an engineer who can tackle open-ended technical problems and communicate across teams. It is based in Santa Clara and is hybrid.
From the employer
We are looking for a Senior Simulation Software Engineer to build and scale the simulation infrastructure that powers our autonomous vehicle development. You will architect simulation pipelines, leverage AI agentic workflows to analyze simulation results, and drive improvements in simulation fidelity to validate autonomy systems and enable critical program milestones. This role offers the opportunity to drive immediate user value while building advanced, AI-powered frameworks for long-term safety validation.
Responsibilities
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Design and build AI agentic workflows that automatically analyze simulation results, identify failures, and surface root causes across large-scale test runs, translating them into actionable insights for engineering teams.
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Architect and build scalable simulation pipelines, including the metric evaluation framework, and conduct in-depth analysis of road test and simulation data to identify trends, anomalies, and key performance indicators that enhance model accuracy and validate autonomy performance.
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Identify, quantify, and reduce sources of divergence between simulation and real-world behavior, leveraging AI and data-driven techniques to increase simulation fidelity across actor behaviors, sensor models, and system dynamics.
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Monitor and improve the performance, scalability, testing, and reliability of the platform.
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Collaborate with cross-disciplinary teams—including simulation, onboard, systems engineering, and data science—to reproduce and resolve issues encountered during real-world road tests.
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Work with other engineers to design the long-term architecture of the system.
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Ensure that technical work meets customer requirements, regulatory standards, and company quality policies, in accordance with the company's Quality Management System (QMS), and contribute to continuous improvement efforts.
Required Skills:
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MS with 3+ years of relevant experience, or a PhD new grad, in mechanical, electrical, aerospace engineering, physics, computer science, or a related field.
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Experience building LLM-powered or agentic workflows for data analysis, evaluation, or automation.
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Experience quantifying or improving simulation fidelity / closing the sim-to-real gap (e.g., sensor modeling, actor behavior modeling, scenario generation).
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Proficient in Python or C++, with experience building scalable data processing systems or evaluation frameworks.
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Ability to solve open-ended technical challenges.
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Excellent communication skills and the ability to collaborate effectively in a team setting.
Preferred Skills:
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Experience with autonomous vehicle or robotics simulation and related concepts.
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Familiarity with LLM agent frameworks (e.g., LangChain, LangGraph, or similar) and prompt/tool-orchestration patterns.
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Experience developing metrics, user-facing APIs, or interfaces between system components.
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