Senior Machine Learning Engineer

The Senior Machine Learning Engineer at Kraken will be responsible for solving complex problems related to energy management using AI and machine learning. You’ll develop and optimize LLMs, refine models through techniques like RAG or finetuning, and collaborate with engineering teams to deploy systems in production using Python and Kubernetes.

To be considered for the Senior Machine Learning Engineer position at Kraken, candidates should have strong experience in deploying LLMs in production environments, a solid understanding of traditional ML techniques, and familiarity with technologies such as Python, Kubernetes, and various ML libraries. A passion for energy innovation is also essential.

Kraken believes in unleashing potential, so as a Senior Machine Learning Engineer, you’ll have access to a dynamic work environment that encourages learning and growth. The company supports ongoing education and offers resources to help you navigate and excel in the fast-evolving energy landscape.

A Senior Machine Learning Engineer at Kraken should be well-versed in Python and has experience with ML libraries such as PyTorch, Hugging Face, and Scikit-learn. Familiarity with Kubernetes for deploying systems, SQL for dataset preparation, and natural language processing (NLP) techniques is highly beneficial.

At Kraken, the culture is rooted in empathy, collaboration, and innovation. The environment is inclusive, where diverse perspectives are celebrated, allowing Senior Machine Learning Engineers to thrive as part of a dedicated team aiming to make a significant impact in the energy sector.

When answering this question, be prepared to discuss specific projects where you've implemented LLMs. Provide details on the challenges faced during deployment, the techniques you used for fine-tuning, and the impact the models had on the outcome.

In your response, emphasize your ability to systematically tackle ambiguous challenges. Talk about breaking down the problem, researching potential solutions, and iterating based on feedback—highlighting any successful projects that illustrate your approach.

Discuss the traditional ML techniques you are familiar with, such as regression or classification models. Give examples of projects where you've used these techniques effectively, detailing how they contributed to project success and any metrics or results achieved.

Explain your process for validating and testing models, including how you handle overfitting, cross-validation methods, and your approach to monitoring model performance post-deployment.

Share your insights on the transformative potential of AI in energy management, such as optimizing resource allocation, improving consumer experiences, and facilitating the transition to sustainable energy practices, linking it back to current trends you follow.

Illustrate your collaborative experience by sharing a specific project where teamwork was key. Highlight your role in fostering communication and aligning goals to meet project deadlines, as well as any hurdles overcome in the process.

Talk about the resources you utilize to stay informed, such as academic papers, online courses, and industry conferences. Mention any communities you engage with or mentors who influence your learning.

Provide a detailed narrative of a specific complex problem you tackled, explaining the steps you took in analyzing data, designing the solution, and implementing it, concluding with the measurable impact it had.

Express your enthusiasm for Kraken’s mission in the energy sector and how your skills align with the company’s objectives. Highlight specific values or aspects of the company culture that resonate with you.

Discuss your familiarity with Kubernetes, sharing specific instances where you've deployed ML systems, the challenges you faced, and how Kubernetes facilitated scalability and management of those systems.