Machine Learning Engineer

As a Machine Learning Engineer at Latent, you'll be responsible for working on groundbreaking challenges in medical AI, particularly in enhancing our multi-modal search platform. This involves fine-tuning large open-source models, conducting extractive highlighting over patient notes, and developing multi-modal clinical embeddings. You'll manage the entire lifecycle of machine learning models from inception through training and validation to serving at scale, ensuring that our solutions provide accurate and timely data for healthcare systems.

To be considered for the Machine Learning Engineer position at Latent, you should have a Master's or PhD in Computer Science or an adjacent STEM field, or equivalent experience with deploying language models. Additionally, experience with Python and machine learning frameworks such as PyTorch or TensorFlow is essential. Knowledge in fine-tuning large language models and deploying ML systems from training to production is also necessary. Bonus points if you have experience in fast-paced environments and contributing to ML research papers!

In this role, you'll be engaged in a variety of impactful software projects that may include fine-tuning large models for medical question answering, optimizing extractive techniques over patient notes, and creating multi-modal embeddings to make EHR data more accessible. Your work will directly contribute to reducing operational overhead in healthcare, ultimately enabling better patient outcomes through innovation.

Latent fosters a collaborative and innovative work culture where teamwork and communication are highly valued. You'll be part of a best-in-class technical team that is mission-driven and is committed to excellence. We encourage a proactive approach to problem-solving and provide ample opportunities for personal and professional growth, believing that every team member can have a substantial impact on our goals.

As a Machine Learning Engineer at Latent, you can expect a competitive salary and equity compensation package. Our benefits include versatile health, dental, and vision coverage, unlimited PTO, and paid parental leave. Additionally, you'll work in a supportive and dynamic environment that allows for professional development and career growth.

When asked about your experience with large language models, explain specific instances where you fine-tuned or deployed them. Highlight your familiarity with frameworks like PyTorch or TensorFlow and any challenges you overcame. Emphasize how these experiences aligned with real-world applications or projects, especially in healthcare if relevant.

For this question, detail your methodology for taking a model from training through validation and into production. Talk about your experiences with monitoring its performance post-deployment and ensuring reliability. Mention any tools you use for version control and continuous integration that help streamline this process.

Explain your fine-tuning process, including data preparation, model selection, hyperparameter tuning, and evaluation metrics. Share a specific example where this process led to significant improvements in a project, emphasizing your ability to optimize for both performance and scalability.

Discuss your approach to validation and testing procedures that ensure models are robust and reliable. Talk about cross-validation, performance metrics, and any specific methodologies you follow to maintain high standards in model performance, particularly in sensitive applications like healthcare.

When answering this, describe a specific, challenging problem you've encountered and the steps you took to solve it. Highlight your analytical thinking and creativity, as well as the technologies and frameworks you utilized to resolve the issue, showcasing your technical prowess.

Share your methods for staying current in the field, such as following reputable journals, attending conferences, participating in webinars, or engaging with online communities. Mention specific topics or advancements that excite you and how this knowledge influences your work.

Describe how you value collaboration in your work. Provide examples of successful team projects where communication and teamwork significantly contributed to achieving the desired results, illustrating your ability to work well with others in pursuit of a common goal.

Express your passion for the medical AI field and its transformative potential in healthcare. Discuss specific aspects that inspire you, like improving patient outcomes, making healthcare more efficient, or using technology to solve real-world problems, relating it back to your personal motivations.

Illustrate your analytical approach to problem-solving by walking through a specific project. Explain how you assess the situation, identify potential solutions, and implement strategies to address challenges, emphasizing critical thinking and adaptability.

Discuss the metrics you prioritize based on the project's goals, such as accuracy, precision, recall, F1-score, or AUC-ROC for classification tasks. Explain why you believe these metrics are important, particularly in the context of healthcare applications where implications are significant.