Machine Learning Engineer

As a Machine Learning Engineer at Baselayer, your primary responsibilities include model development and integration, ML system design, data processing, and feature engineering. You will architect core ML services to support KYC/KYB processes, implement advanced ML techniques, maintain robust data pipelines, and ensure model governance and compliance with industry standards.

To qualify for the Machine Learning Engineer role at Baselayer, you should have 1-3 years of experience in machine learning development, proficiency in Python, and a solid grasp of AI/ML fundamentals. Familiarity with large-scale data, model governance in regulated environments, and a passion for optimizing performance are also essential.

Baselayer is dedicated to fostering an environment where Machine Learning Engineers can grow their careers. Through feedback-driven culture, hands-on learning opportunities, and the chance to work on cutting-edge AI solutions, you will have the tools needed to enhance your skills, make impactful contributions, and advance within the company.

At Baselayer, you will experience a high-trust, ownership-focused culture that encourages experimentation and feedback. The team is composed of intelligent and ambitious individuals who value collaboration, innovation, and taking initiative. We promote a healthy work-life balance with flexible PTO and hybrid work options.

The salary for the Machine Learning Engineer position at Baselayer ranges from $150k to $225k, alongside equity options between 0.05% and 0.25%. This competitive compensation package reflects our commitment to attracting top talent in the tech industry.

When answering this question, highlight specific projects where you developed machine learning models. Discuss the methodologies used, challenges faced, and how you optimized the models for performance. Be sure to include technologies, libraries, and frameworks you've worked with, such as Python or TensorFlow.

The interviewer will be looking for your understanding of the data pipeline. Discuss your steps for cleaning data, handling missing values, and feature extraction. Mention any tools or techniques you've used effectively in previous roles, particularly for large datasets.

Discuss innovative methods, such as reinforcement learning from human feedback (RLHF) or parameter-efficient fine-tuning techniques. Share an example where you applied these techniques and the outcomes achieved, showcasing your ability to apply advanced concepts in real-world scenarios.

Focus on the importance of responsible AI practices. Provide examples of how you have incorporated fairness, explainability, and compliance with industry regulations, especially in sensitive contexts like KYC/KYB. Showing your awareness of ethical considerations in AI will resonate well with the interviewer.

Break down your systematic approach to identifying and resolving issues in ML models. Discuss specific techniques used, such as visualization of results, analyzing loss functions, or running experiments to analyze performance. Demonstrating critical thinking and problem-solving skills is key here.

Highlight your expertise in programming languages, particularly Python, which is crucial for machine learning. Discuss libraries and frameworks you commonly use, like Scikit-learn or PyTorch, and express your willingness to learn new languages or tools as needed.

Emphasize your commitment to continuous learning by discussing how you read research papers, participate in online courses, or attend conferences. Mention any relevant communities or forums where you engage with others to share insights and discuss trends.

Share your methods for optimizing model performance, which could include hyperparameter tuning, model pruning, or using more efficient algorithms. Provide examples from your past work where these optimizations resulted in significant improvements.

Discuss how feedback is integral to your development process. Give examples of how you've sought input from colleagues and incorporated it into improving models or processes. Express your belief in the value of radical candor and continuous improvement.

Highlight your understanding of the ethical implications related to AI, particularly in financial applications. Discuss the importance of responsible AI practices, fairness, and transparency, showcasing your commitment to developing systems that uphold ethical standards.