Machine Learning Engineer

As a Machine Learning Engineer at Across.ai, you will be responsible for the development and deployment of ML/AI-based algorithms. This includes collaborating with both product and engineering teams to design, implement, and refine AI algorithms for enterprise applications. Your day-to-day tasks will involve creating large language models, conducting code reviews to ensure quality, and optimizing algorithms for performance and scalability.

To apply for the Machine Learning Engineer position at Across.ai, you need a Bachelor's or Master's degree in Computer Science or a related field. Additionally, you should have at least 2 years of hands-on experience developing and deploying AI applications, strong programming skills in Python, and familiarity with ML libraries. A solid understanding of machine learning algorithms, as well as experience with Foundation models like LLMs, is essential.

In the Machine Learning Engineer role at Across.ai, strong programming skills in Python are crucial, as it is widely used for developing and deploying machine learning models. Familiarity with various ML/AI libraries will greatly enhance your ability to succeed in this position, as you will be expected to analyze and iterate over ML models effectively.

At Across.ai, collaboration is key! As a Machine Learning Engineer, you will work closely with product managers and engineering teams to design and implement AI algorithms. You will also have opportunities to mentor others and engage with both internal stakeholders and external partners to ensure that our solutions meet diverse needs.

The company culture at Across.ai is one of innovation, mentorship, and collaboration. With a team rooted in a deep understanding of AI and sales, you will be part of a dynamic environment where your input is valued. We encourage quick iterations and creative problem-solving while maintaining high standards for quality, making it an exciting place for a Machine Learning Engineer.

In response to this question, focus on highlighting specific projects you have worked on that involved machine learning algorithms. Discuss the algorithms you implemented, the challenges you faced, and how you overcame them. Mention the impact your contributions had on the project's success, and be prepared to provide examples of optimizations you made.

Share your systematic approach to evaluating machine learning models. Discuss the metrics you use for assessing model performance, such as accuracy, precision, recall, and F1-score. Explain how you iterate on model design based on the evaluation results and provide examples of situations where you improved a model's performance significantly.

In answering this question, emphasize your proficiency in Python and any relevant libraries, such as TensorFlow, PyTorch, or scikit-learn. Discuss how you have utilized these tools in past projects and how they helped in achieving your goals, particularly in developing and deploying AI models.

Provide a specific example of a challenging problem, detailing the context and the steps you took to resolve it. Explain your thought process and the technical approaches you employed, emphasizing how your solution had a positive impact on the overall project or product.

Discuss your methods for maintaining high code quality, such as conducting regular code reviews, writing unit tests, and adhering to coding best practices. Talk about how these practices contribute to team collaboration and project scalability, ensuring that your output is reliable and maintainable.

Describe the strategies you use to keep your knowledge fresh and relevant, such as following industry blogs, attending conferences, or participating in online courses. This shows your commitment to continuous learning, which is critical in the rapidly evolving field of AI and machine learning.

Provide examples of machine learning models you've deployed into production environments. Discuss the challenges you faced, how you overcame them, and the learnings you gained from these experiences. This will illustrate your practical knowledge and real-world application of your skills.

Share specific examples of how you've effectively worked with product managers, designers, and engineers in the past. Highlight your communication skills and the importance of ensuring that everyone is aligned towards common objectives, and how you approach gathering feedback for iterative improvement.

In your response, talk about your hands-on experience with LLMs, detailing the projects you've worked on that involved these models. Discuss the techniques you've used for prompting, benchmarking, or fine-tuning them in order to achieve optimal outputs, thereby showcasing your technical proficiency in the area.

Explain your thought process when you encounter ambiguous problems. Describe how you break down the problem, explore different solutions, and utilize data-driven approaches to reach a conclusion. Highlight any specific situations where your initiative led to a successful outcome amid uncertainty.