Principal AI Engineer

Stratus

2d•Remote

About The Position

The Principal AI Engineer joins our team as a senior technologist building foundation models, agentic AI systems, and generative AI tools for our products. You will work collaboratively to create and interpret design data that enhances design and engineering workflows, turning large-scale, multi-modal datasets into representations that power production ML systems. As a Principal Engineer, you will set technical direction, mentor engineers, and drive best practices across cross-functional teams of ML research scientists and engineers, translating research ideas into production-grade systems.

Requirements

MSc or PhD in Computer Science, Engineering, or a related field.
5–8+ years of experience in, Engineering, Machine Learning, or related fields.
Deep programming and software engineering experience strong computer science fundamentals (data structures, algorithms, system design) and a proven track record of shipping and maintaining production-grade code, not just prototypes or notebooks.
Proven technical leadership in complex projects and guiding technical direction across cross-functional teams.
Strong experience in geometric data modeling and processing, including complex 2D/3D representations, computational geometry, and data architectures.
Familiarity with machine learning concepts and frameworks and how data is represented for training.
Ability to translate research ideas into production-grade systems.
Excellent communication skills with the ability to influence and guide technical decisions.

Nice To Haves

Hands-on experience building agentic systems with frameworks such as CrewAI, LangGraph, or AutoGen, including tool calling, structured outputs, and eval frameworks.
Proficiency in C# and strong software development practices.
Passion for solving problems for our customers by applying machine learning techniques.
Comfort working in newly forming, ambiguous areas where learning and adaptability are key.
Ability to collaborate easily with others and work effectively with minimal direction.
Drive to continually learn new technologies and methodologies and seek new ways to solve hard problems.
Bias toward putting your ideas out there and failing fast.

Responsibilities

Build foundation models and generative AI tools alongside a team of technologists.
Design and build agentic workflows — multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen), tool use, multi-step planning, and human-in-the-loop checkpoints — to automate complex engineering tasks.
Establish evaluation, guardrails, and failure-mode analysis for agent systems to ensure they are safe, reliable, and production-ready.
Develop scalable data pipelines for diverse data sources used in production ML systems, including BIM, CAD, and infrastructure design data.
Work with large-scale, multi-modal datasets — including text and geometric data — to design novel preprocessing, augmentation, analysis, and content understanding.
Transform unstructured infrastructure and design data into representations suitable for machine learning.
Lead cross-functional collaboration with ML Research Scientists and Engineers to align data formats with downstream training and fine-tuning of LLMs.
Apply deduplication, normalization, and validation techniques to ensure high-quality data in production environments.
Architect and optimize pipelines for scalability, reproducibility, and cloud deployment.
Mentor junior engineers and provide technical guidance on complex data challenges.
Drive technical decision-making and influence best practices across the team.
Perform requirements analysis with senior stakeholders, ensuring technical solutions meet both immediate project goals and long-term research objectives.
Communicate findings and technical insights through quantitative analysis, visualizations, and clear documentation.
Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs.
Participate in technical planning and roadmap development.