Founding Research Engineer

As a Founding Research Engineer at Letta, you will develop core agentic reasoning loops, enhance the performance of our agents framework through open models and new AI capabilities, and integrate various LLM API providers. Your work will involve implementing innovative solutions for memory management, tool execution, and systems coordination, making you a vital part of our mission to advance AI technology.

Candidates for the Founding Research Engineer role at Letta should possess strong programming skills in Python, a solid understanding of AI/ML fundamentals, and the ability to comprehend complex research papers in the agents and memory space. Experience in software engineering and a passion for developing innovative AI solutions are also crucial to thrive in this role.

At Letta, a Founding Research Engineer will work on exciting projects such as adding memory management capabilities to voice agents, implementing constrained decoding for better tool use in open models, and benchmarking model performance. You'll be involved in pioneering work that pushes the boundaries of AI and LLM capabilities.

Letta's hiring process for Founding Research Engineers is thorough to ensure we find the right fit for our tight-knit team. It includes an initial phone interview to understand your background, a technical take-home assessment to gauge your skills, and a paid workday where you'll simulate real tasks while collaborating with our team. We value your insights and experiences, ensuring every step of the process is beneficial for you and us.

The Founding Research Engineer position at Letta is based in San Francisco. We prioritize in-person collaboration, as working from our office allows team members to engage closely and understand what it will be like to join our innovative journey. Relocating to the Bay Area is recommended for candidates keen on being part of our vibrant team.

When responding to questions about your Python programming experience in AI/ML, highlight specific projects where you successfully utilized libraries like TensorFlow or PyTorch. Discuss the complexity of the algorithms you implemented, any challenges faced, and how you overcame them to deliver results. Show your enthusiasm for continuous learning and how you keep pace with evolving technologies.

In interviews, when asked about understanding research papers, explain your methodical approach: start by skimming the abstract, introduction, and conclusion to grasp the main idea, then delve into the methodology and results. Share an example of a paper you analyzed and how it influenced your work, showcasing your ability to distill complex ideas into actionable insights.

Use the STAR method to structure your response about a challenging software engineering problem. Discuss the situation, the task, the actions you took, and the results achieved. Emphasize how your problem-solving skills and technical expertise made a tangible impact, and express what you learned from the experience to illustrate how you grow from challenges.

When asked about optimizing AI models, share specific strategies you've employed in the past, such as hyperparameter tuning, model pruning, or employing transfer learning techniques. Discuss particular models you've worked on and the improvements you achieved through optimization, demonstrating your hands-on experience and understanding of best practices in AI.

In response to this question, outline your proactive approach to staying informed about AI and ML trends. Discuss resources like academic journals, online courses, webinars, and communities you engage with. Mention any conferences you've attended and how they’ve contributed to your knowledge, demonstrating your commitment to continuous learning in a rapidly evolving field.

When addressing potential disagreements in a team, emphasize the importance of open communication and collaboration. Share examples where you navigated differing viewpoints through constructive dialogue, seeking to understand different perspectives before striving for a consensus. Show your ability to foster a positive team environment even in challenging discussions.

When asked why you are interested in working at Letta, express your enthusiasm for the unique mission of the company and its cutting-edge research on LLMs. Discuss how your skills and passion align with the objectives at Letta, and illustrate your excitement for contributing to innovations that advance AI technology.

When discussing agentic systems, clarify that they are designed to perform tasks autonomously while learning and adapting to their environment. Illustrate your understanding with examples of how agentic systems can improve user experiences or solve real-world problems. Show your enthusiasm for exploring this area further within the role at Letta.

Respond to this question by highlighting any projects or research you've undertaken that involved memory management in AI systems. Discuss specific techniques or frameworks you used and detail the successes of implementing these strategies in improving system efficiency, reliability, or user experience.

In answering how you prioritize tasks, emphasize your organizational strategies—such as using tools for task management, setting deadlines, and assessing project impact. Share an example of a situation where you successfully balanced multiple projects, illustrating your time-management skills and ability to adapt when priorities shift.