As a Research Engineer at Dawn Labs, you will implement state-of-the-art machine learning research and adapt recent techniques such as LLMs, embeddings, and diffusion models to enhance the responses and behaviors of our autonomous agents. You’ll design pipelines for evaluating agent performance and collaborate with backend engineering to optimize system architecture. Your role involves enhancing observability, creating scalable data handling processes, and ensuring our systems are top-notch in performance and reliability.

To become a Research Engineer at Dawn Labs, strong backend engineering skills are essential, including proficiency in writing clean code, building APIs, and deploying models. Candidates should have experience managing end-to-end machine learning systems and a solid background in data analysis. Knowledge of the latest ML techniques and a track record in collaborating with research and product teams are also critical for success in this role.

Having hands-on experience with machine learning research will significantly enhance your application for the Research Engineer position at Dawn Labs. Our team values candidates who can translate theoretical knowledge into practical applications, enabling us to implement cutting-edge techniques that drive our product forward. Demonstrated contributions to open-source ML projects or relevant publications can also set you apart as an ideal fit for our innovative environment.

At Dawn Labs, the work environment is both fast-paced and collaborative, featuring a hybrid setup that allows for flexibility between in-person and remote work. The team you’ll be joining is highly capable and comes from diverse backgrounds, bringing creativity and expertise to each project. With our commitment to cutting-edge technology, you'll thrive in a setting where your work shapes production immediately, ensuring that your contributions have a direct and positive impact.

Research Engineers at Dawn Labs should be familiar with a range of advanced technologies, including TypeScript, Postgres, Kafka, and Python. Additionally, a solid understanding of machine learning frameworks, along with recent developments in techniques like LLM fine-tuning, retrieval-augmented generation, and diffusion models, is crucial for success in this role. Mastery in these areas will enable you to optimize backend operations effectively.

In your response, detail specific algorithms you have implemented, focus on the challenges faced, and how you've adapted them to real-world scenarios. Consider highlighting your knowledge of the machine learning process from data preprocessing through to deployment and monitoring.

Talk about the methodologies you use, such as A/B testing and evaluation pipelines, to validate model accuracy. Highlight your collaborative efforts with others in research and engineering to refine models post-deployment.

Discuss your familiarity with indexing, caching strategies, and performance tuning specific to PostgreSQL. Provide examples of how these strategies improved the efficiency of data retrieval and model performance in your previous roles.

Share your approach to continuous learning—mention specific conferences you attend, papers you read, or communities you are involved in. This helps demonstrate your passion for the field and commitment to applying cutting-edge techniques.

Provide details on a specific project, outlining your role in various stages, such as data collection, model building, and deployment. Discuss the tools you used and the outcomes of the project to show your capability.

Highlight your experience with tools and methodologies for monitoring model performance, including logging procedures and how you've utilized performance metrics to inform your further iterations.

Discuss the systematic approach you took to identify the issue. Explain how you diagnosed the problem, the steps you took to resolve it, and the importance of timely intervention for business functionality.

Talk about your organizational strategies, such as the use of task management tools or prioritization frameworks. Share specific examples of how these strategies have helped you meet deadlines without sacrificing quality.

Discuss your specific contributions—whether they are code, documentation, or community engagement. Highlight how these experiences not only have enhanced your skills but also have kept you in tune with industry standards and practices.

Explain your strategies for effective communication and collaboration with colleagues from different domains, including specific techniques you've used to ensure alignment on goals and expectations throughout the project lifecycle.