Founding Machine Learning Engineer

As a Founding Machine Learning Engineer at FERMÀT, you'll be responsible for shaping our long-term ML/AI strategy and ensuring alignment with our core product roadmap. This role involves designing, developing, and maintaining end-to-end MLOps pipelines, collaborating closely with Data, Product, and Engineering teams, and experimenting with machine learning architectures. You’ll lead research projects and provide technical guidance on ML initiatives, while also optimizing model performance and scalability.

To qualify for the Founding Machine Learning Engineer role at FERMÀT, candidates should have at least 3 years of experience in traditional machine learning, including exposure to large language models (LLMs), and a minimum of 4 years in software engineering. Proficiency in programming languages like Python and Java, along with expertise in data modeling and visualization, is essential. A bachelor’s or master’s degree in computer science or a related field is also required.

A Founding Machine Learning Engineer can significantly impact FERMÀT's growth by developing innovative machine learning solutions that enhance our product offerings. Your contributions will steer the direction of our AI capabilities, directly influencing the personalized shopping experiences we provide to customers. By implementing effective MLOps strategies and optimizing model architectures, you’ll help to streamline operations and drive the success of our eCommerce platform on a larger scale.

FERMÀT fosters a collaborative, fast-paced environment where innovation thrives. As a Founding Machine Learning Engineer, you’ll work alongside talented individuals who share your passion for AI and technology. We encourage creative thinking and provide the freedom to explore complex challenges, allowing your ideas to shape the future direction of our product and company. It’s an exciting time to join us as we scale and redefine the eCommerce landscape!

A Founding Machine Learning Engineer at FERMÀT can expect a competitive salary paired with an equity package, comprehensive health benefits, a 401(k) plan with matching, and generous paid parental leave. Additionally, we offer an unlimited PTO policy, a stipend for remote work setups, and a wellness stipend aimed at supporting your overall well-being. We believe in creating a supportive and enriching work environment that empowers our team members.

When asked about your experience with machine learning and large language models, explain the specific projects you’ve worked on, detailing the algorithms and tools you used. Highlight your understanding of both traditional ML techniques and how you’ve incorporated LLMs into your workflows, stressing your hands-on experience in deploying models successfully.

In your response, discuss your methodology in designing MLOps pipelines, including the stages from data collection to model deployment and monitoring. Emphasize your focus on scalability, model performance, and continuous integration. Illustrate with examples where you’ve implemented structured approaches to improve efficiency.

Talk about your use of techniques such as cross-validation, grid search, and performance metrics tailored to your specific models. Explain how you analyze results after model training, and mention any experience with pipelines for automated model evaluation. Providing examples of how you’ve refined models based on these evaluations can strengthen your answer.

When responding, be sure to describe the problem clearly and the approach you took to solve it. Discuss the modeling techniques and tools you utilized, as well as any challenges you faced. Show how your analytical thinking and problem-solving skills helped you to achieve a successful outcome.

Express your dedication to continuous learning by mentioning resources such as online courses, seminars, academic journals, or professional meetups. Discuss how you apply new insights or technologies to your work, showcasing your understanding of current trends and your commitment to growth.

Discuss your proficiency in programming languages such as Python and Java, along with your experience in libraries or frameworks like TensorFlow or PyTorch. Provide examples of projects where you utilized these languages to build or train machine learning models effectively.

Share insights about your adaptability and how you thrive in dynamic settings. Talk about your ability to manage multiple projects, collaborate with teams, and contribute to rapid product iteration. Highlight specific achievements during your time in previous startups to illustrate your success in similar environments.

Discuss your communication style and how it ensures productive collaboration with Data, Product, and Engineering teams. Share examples of successful projects where you aligned on goals, contributed technical insights, and fostered an environment of cooperation to achieve shared outcomes.

When responding, emphasize your problem-solving skills and your focus on constructively resolving conflicts. Discuss your willingness to listen to other viewpoints and your ability to facilitate discussions that lead to consensus, ensuring that the best ideas prevail regardless of their source.

Express your enthusiasm for the transformative potential of AI in creating personalized shopping experiences. Discuss how AI can enhance customer interactions and improve conversion rates. Your passion for pioneering advancements within the eCommerce space will demonstrate your alignment with FERMÀT’s mission.