As a Machine Learning Engineer at Decagon, your primary responsibilities include evaluating and fine-tuning large language models, deploying these models to enhance our conversational AI agents, and developing intelligent systems that can autonomously deliver high-quality support. Additionally, you'll collaborate with a diverse team to brainstorm innovative solutions and improve our customer experience.

To qualify for the Machine Learning Engineer position at Decagon, you should have at least 5 years of experience in AI/ML engineering or research, a strong track record of moving AI/ML projects from concept to production, and demonstrable skills in fine-tuning and deploying large language models. Exceptional coding skills and a passion for innovative technology are also key requirements.

At Decagon, we value engineers who write amazing code quickly. For the Machine Learning Engineer role, proficiency in programming languages commonly used in AI/ML, such as Python, is essential. Experience with relevant frameworks and libraries also helps in effectively developing and deploying models.

Working as a Machine Learning Engineer at Decagon means joining a dynamic, fast-paced environment filled with talented professionals. You will collaborate with like-minded individuals who are passionate about pushing the boundaries of AI technology, all while building exceptional products that improve customer experiences.

Decagon provides a comprehensive benefits package for its Machine Learning Engineers, including health, dental, and vision insurance, an unlimited vacation policy that encourages work-life balance, and promising career growth opportunities within a rapidly expanding AI company.

When answering this question, emphasize specific projects where you've successfully applied large language models. Discuss the models you fine-tuned or deployed, the challenges you faced, and the outcomes of your work.

Discuss your methodology for data preprocessing, including techniques you utilize to clean, normalize, and prepare data for model training. Highlight any tools you've used and your understanding of their importance in achieving model accuracy.

Articulate your familiarity with the AI/ML lifecycle, from data collection to model deployment and monitoring. Share examples of how you have applied lifecycle management in past projects to ensure successful deployment and system performance.

Provide a structured narrative of a project, detailing your leadership role, objectives, methodologies, and results. Focus on the technical and team dynamics that contributed to the project's success.

Highlight your problem-solving skills and adaptability. Describe strategies for troubleshooting model errors, experimenting with different approaches, and how you learned from setbacks to improve performance over time.

Mention your strongest programming languages, particularly those relevant to machine learning, such as Python or R. Provide examples of how you have utilized these languages in your projects.

Discuss your commitment to continuous learning by mentioning resources you follow, such as online courses, journals, or conferences. Emphasize how staying informed helps you innovate and apply new techniques in your work.

Choose a specific algorithm and explain where it was used and why you chose it. Provide insight into the implementation process and the results derived from it, demonstrating your technical expertise.

Describe your approach to teamwork in AI projects, focusing on communication tools and methods you use. Stress the importance of cross-functional collaboration and feedback loops to enhance project outcomes.

Discuss the metrics and methods you utilize to evaluate model performance, such as accuracy, precision, and recall. Mention the importance of validating models through techniques like cross-validation and A/B testing.