Senior Backend Software Engineer

As a Senior Backend Software Engineer at Endeavor, you will design and implement the foundational architecture for our agentic ERP system, focusing on APIs, state management, and data flows. You will collaborate closely with Applied AI Researchers to turn AI reasoning models into effective infrastructure, ensuring scalability and robustness. Additionally, your expertise will be essential in bridging the gap between developed systems and operational needs within customer environments.

To be considered for the Senior Backend Software Engineer role at Endeavor, candidates should possess 3–10 years of experience in backend system development, preferably in dynamic startup environments. A strong foundation in coding practices, system design, and debugging is essential. Additionally, familiarity with AI/LLM workflows and experience in legacy-heavy domains will provide a competitive edge as you navigate complex engineering challenges.

At Endeavor, the Senior Backend Software Engineer is expected to collaborate extensively with both Applied AI Researchers and Forward Deployed Engineers. This collaborative environment aims to ensure your backend engineering work aligns closely with research insights and customer-facing needs, allowing for seamless module deployment into real-world ERP systems. Effective communication and translation of technical concepts across diverse teams are critical for success.

For the Senior Backend Software Engineer role at Endeavor, proficiency in programming languages such as Python, Java, or Go is highly valuable. Equally important is a strong understanding of API design, data architecture, and the ability to write clean, maintainable code. Debugging complex workflows across distributed systems is also a key skill that will enable you to navigate the challenges presented by large-scale enterprise applications.

In the position of Senior Backend Software Engineer at Endeavor, you will be involved in multifaceted projects that include designing APIs, creating task runners, and managing data observability for our advanced ERP systems. Your work will focus on integrating AI workflows, developing interfaces for LLM agents, and ultimately facilitating the transition from early customer implementations to a more robust, enterprise-ready solution.

When facing debugging challenges in distributed systems, it’s crucial to start with a clear understanding of the overall architecture. I focus on isolating variables by using logging and monitoring tools to identify points of failure. Asking specific questions about system behavior helps gauge whether components interact as expected. By tracing workflows from one end to the other, I can break down complex interactions and find solutions more systematically.

In a previous role, I facilitated communication between researchers developing a new AI model and the engineering team responsible for its integration. I organized regular workshops where both teams could present their insights and challenges. This mutual understanding enabled us to align the AI’s capabilities with practical engineering constraints, ensuring smooth implementation while enhancing overall product quality.

My experience in API development has involved both designing RESTful services and integrating third-party APIs. I'm adept at defining endpoints, input/output data structures, and ensuring robust authentication and security measures. By focusing on scalability and ease of use, I ensure that developers find the APIs intuitive while meeting the underlying technical requirements seamlessly.

In high-pressure situations, I prioritize maintaining a clear perspective and systematic approach to problem-solving. I break tasks down into manageable parts and focus on key deliverables. Leveraging strong communication within my team is vital, as it allows us to stay aligned and pivot quickly when needed, ensuring that critical objectives are met despite the pace.

To ensure code quality, I employ a combination of coding standards, peer reviews, and automated tests. During the development process, I advocate for writing clean, modular code that adheres to best practices. Regular code reviews not only foster a collaborative atmosphere but help catch potential issues early. Automated testing, including unit and integration tests, provides an additional layer of confidence that our code remains robust.

My experience with AI workflows involves architecting systems that support both data processing and model inference. For instance, I have integrated machine learning models into existing backend infrastructures to facilitate real-time data analysis. This requires a deep understanding of how AI components interact with traditional systems, ensuring that they remain performant without compromising stability.

In a fast-paced setting, I utilize a prioritization framework that balances urgency and importance. I regularly assess tasks based on their impact on project goals, stakeholder needs, and potential risks involved. Leveraging tools for time management and progress tracking helps me stay organized, allowing me to shift priorities dynamically while ensuring that critical deadlines are met.

I have significant experience working with legacy systems, particularly in transforming and integrating them with modern tech stacks. Understanding their limitations while ensuring they remain functional is key. I’ve successfully migrated data and streamlined processes by building wrappers around older systems, progressively enhancing their capabilities without overwhelming users or disrupting operations.

In a recent project, I implemented state management in a distributed application that required multi-user interactions. I utilized state machines to track user state across various components, which allowed for predictable handling of data flow. This not only improved system reliability but also enhanced the user experience by allowing for clear transitions and state tracking.

For effective system integration, I apply techniques such as API-driven architecture, microservices, and event-driven programming. Defining clear contracts between services ensures minimal dependencies and facilitates seamless communication. I also leverage middleware to handle orchestration between disparate systems while ensuring that they can scale independently.