Machine Learning Engineer

At Payhawk, a Machine Learning Engineer is tasked with designing and implementing autonomous AI agents utilizing large language models (LLMs) for reasoning and decision-making. This role involves optimizing and deploying LLM-based agents in production, building evaluation pipelines, and collaborating with cross-functional teams to integrate machine learning solutions into scalable products.

To be considered for the Machine Learning Engineer position at Payhawk, candidates should have an academic background in Computer Science, Mathematics, or a related discipline. Hands-on experience with LLMs, proficiency in SQL and Python, and familiarity with major ML libraries like TensorFlow and PyTorch are essential. A strong understanding of text representation techniques and model development is also beneficial.

The working environment at Payhawk is dynamic and inclusive, emphasizing collaboration and innovation. As a Machine Learning Engineer, you'll work alongside a team of talented professionals, participate in team-wide events, and contribute to a culture that values feedback and continuous improvement. We offer a flexible working schedule and opportunities for remote work to help you balance your personal and professional life.

Payhawk fosters an atmosphere of continuous learning and professional development. Machine Learning Engineers are encouraged to stay updated with the latest advancements in AI and LLMs. Opportunities for knowledge sharing and collaboration on innovative projects allow engineers to grow their skills while contributing to meaningful solutions. Additionally, our benefits include access to resources designed to support your learning journey.

As a Machine Learning Engineer at Payhawk, you can expect a competitive compensation package, stock options, 30 days of paid holiday, and flexible working hours. We believe in supporting our team's well-being with additional medical care, company-funded MultiSport cards, and even office massages. Plus, you'll have the opportunity to work from our different offices around Europe!

To effectively answer this question, share specific projects or experience you've had that involved LLMs. Discuss the techniques used, challenges faced, and the impact of your work. Highlight your knowledge of models like GPT and how you've applied them to solve real-world problems in previous roles.

Your response should outline your process for optimizing models, such as evaluating performance metrics, implementing A/B testing, and using systematic debugging methods. Discuss any tools you use for deployment and monitoring of model performance over time.

In your answer, focus on a specific challenge, walk the interviewer through your problem-solving approach, and showcase how you applied machine learning techniques to develop a solution. Include any relevant results or outcomes that demonstrate your impact.

Discuss various feature engineering techniques you’ve employed, such as normalization, one-hot encoding, or using LLMs for text feature extraction. Highlight how these techniques improved the model's performance in your past projects.

Talk about your methods for ongoing learning, including online courses, following industry leaders, attending conferences, and contributing to or reviewing publications. Mention any specific resources or communities that you find particularly valuable.

If applicable, explain any projects where you worked with multi-agent frameworks. Describe how collaborating agents solve tasks together and how you managed the interactions between them to achieve desired outcomes.

Here, outline your experience working with GCP for machine learning deployments. Discuss the specific services you've used, like Google Cloud Storage or AI Platform, and how those services helped streamline your deployment process.

Share insights on common deployment pitfalls like overfitting or failing to monitor model performance. Discuss the strategies you've implemented to mitigate these risks, such as ensuring robust cross-validation or implementing continuous monitoring systems.

Emphasize the importance of collaboration and communication. Describe how you actively seek feedback from other teams, encourage open discussion about the model’s performance, and incorporate their insights into your iterative development process.

In your answer, express your enthusiasm for Payhawk’s mission and values. Discuss how the role aligns with your passion for leveraging machine learning to drive innovation in the fintech space and your eagerness to contribute to building impactful solutions.