Senior Machine Learning Engineer (4+ years) | Nepal

As a Senior Machine Learning Engineer at Docsumo, you will play a vital role in designing, developing, and implementing advanced machine learning systems. Your responsibilities will include leading a team of data scientists, conducting research on machine learning applications, managing the full lifecycle of ML projects, and ensuring the successful delivery of innovative solutions. You'll be actively involved in the integration of cutting-edge techniques, particularly focusing on Large Language Models, which are essential to enhance our document processing capabilities.

To apply for the Senior Machine Learning Engineer position at Docsumo, it's essential that you have at least four years of industry experience in machine learning, deep learning, NLP, and computer vision. Additionally, you should have experience leading a team of ML engineers and scientists, proficiency with PyTorch and TensorFlow, and strong programming skills in Python. A bachelor's degree in computer science or a related field is also required to ensure you have the foundational knowledge necessary for this role.

A Senior Machine Learning Engineer at Docsumo should be proficient in essential tools and technologies like PyTorch, TensorFlow, Scikit-learn, Numpy, and Pandas. Additionally, experience with cloud platforms such as AWS, containerization with Docker and Kubernetes, and familiarity with version control using Git are critical. Being knowledgeable in advanced techniques involving Large Language Models and having insights into computer vision applications will set you apart in your application.

The work culture at Docsumo for a Senior Machine Learning Engineer is centered around collaboration, innovation, and continuous improvement. You'll work alongside a talented team in a flexible environment that supports remote work options. Regular Agile practices such as stand-ups and sprint planning shape your experience, ensuring that you can contribute effectively and manage your projects efficiently. We encourage a growth mindset, where mentoring and knowledge sharing are paramount.

As a Senior Machine Learning Engineer at Docsumo, you will directly contribute to our mission of revolutionizing how businesses handle data. By developing advanced machine learning systems that transform unstructured documents into actionable insights, your work will empower clients to make smarter decisions and enhance efficiency within their operations. This role allows you to lead groundbreaking projects that not only benefit our clients but also drive innovation in the document AI space.

When discussing a machine learning project during your interview, focus on the objectives, your specific role, and the technologies used. Detail the challenges, such as data quality, algorithm selection, or team dynamics, and highlight how you overcame them. Emphasizing teamwork and problem-solving will demonstrate your leadership capabilities.

In your response, be honest about your experiences with frameworks like PyTorch and TensorFlow. Discuss the strengths and weaknesses of each, particularly in relation to specific projects. Mention factors such as ease of prototyping, community support, or deployment capabilities to show a deep understanding of the tools.

Explain your methodology by discussing the importance of domain knowledge and the insights it provides for feature selection. Talk about your experience using techniques like PCA, recursive feature elimination, or your use of domain-specific features. Show how you analyze the impact of features on model performance through validation metrics.

To answer this question, mention techniques like cross-validation, ensemble methods, and bias-variance tradeoff management. Discuss your practices in varying training data, regularization, and hyperparameter tuning. This shows your commitment to building reliable models for production environments.

Share specific examples of projects where you applied deep learning techniques. Discuss the architecture of models you built, the type of data you worked with, and the outcomes. Highlight the challenges faced and what you learned from those experiences to demonstrate your depth of knowledge.

In your answer, mention specific projects where you implemented NLP techniques, such as text classification or sentiment analysis. Discuss the libraries you used, like NLTK or SpaCy, and elaborate on how you processed and interpreted linguistic data to deliver successful outcomes.

Discuss your experience using Agile methodologies, such as daily standups and sprint retrospectives, to keep teams aligned and projects on track. Emphasize communication strategies, role delegation, and ways you motivate your team to achieve goals on time.

Talk about issues like data drift, scalability, and deployment environment mismatches. Share specific strategies you've utilized, such as continuous monitoring and robust testing procedures, to ensure models perform well in production. This highlights your practical experience in the field.

Discuss the resources you utilize, such as research papers, online courses, and conferences. Highlight your engagement in the ML community, whether it’s through forums, blogs, or contributing to open-source projects, showing your dedication to continuous learning.

This is a chance to demonstrate your adaptability. Share a specific scenario where insights led to a change in approach, detail the reasons for the pivot, and discuss how it ultimately affected the project's success. Highlight your decision-making process and willingness to learn from experiences.