Senior Machine Learning Engineer - Computer Vision

A Senior Machine Learning Engineer at Zefr focuses on developing state-of-the-art computer vision classifiers, using advanced machine learning algorithms to analyze vast amounts of social media data. You're tasked with building sophisticated AI systems, enhancing our ability to provide advertisers with precise brand safety and suitability solutions.

Candidates for the Senior Machine Learning Engineer position at Zefr should hold a Bachelor's or Master's degree in Computer Science or a related field and have at least 2 years of experience in training computer vision classifiers and developing ML algorithms. Experience with Python and SQL, as well as familiarity with distributed systems, is essential.

At Zefr, you'll work with a variety of cutting-edge technologies including Python, SQL (specifically Snowflake), Apache Kafka, and PyTorch, among others. Familiarity with MLOps tools like Triton Inference Server and Weights and Biases will be beneficial as you collaborate on advanced machine learning projects.

The working culture at Zefr is collaborative and innovative. As a Senior Machine Learning Engineer, you will be encouraged to share your insights while also learning from your colleagues. With flexible work options, professional development opportunities, and a strong commitment to diversity, Zefr is a place where everyone can thrive.

Zefr offers an extensive benefits package for U.S.-based employees, including flexible PTO, comprehensive medical, dental, and vision insurance, paid parental leave, and a retirement plan with company match. Additional perks include in-office lunches and optional social events to celebrate our successes together.

As a Senior Machine Learning Engineer at Zefr, expect to be deeply engaged in projects that involve building and optimizing computer vision models, utilizing large datasets from major social media platforms. You’ll also collaborate with teams leveraging large language models to enhance our AI systems comprehensively.

Zefr is committed to the professional growth of its employees. As a Senior Machine Learning Engineer, you’ll have access to professional development opportunities such as training workshops, conferences, and mentorship programs, allowing you to continuously enhance your skills and stay at the forefront of machine learning technology.

When discussing your experience, be specific about the types of classifiers you have worked on. Highlight any projects where you built classifiers from scratch or improved existing ones, mentioning frameworks or libraries used, such as PyTorch or TensorFlow.

Identify a few key algorithms you have experience with, such as Convolutional Neural Networks (CNNs) for image processing, and elaborate on how you've applied them in real-world projects, especially within computer vision.

Discuss your approach to preprocessing and managing large datasets, including any tools or techniques used to optimize data handling. Mention experience with data stores like Snowflake and data processing frameworks like Apache Kafka.

Explain your model development process, which may include data collection, cleaning, feature extraction, model training, validation, and optimization. Highlight how you incorporate feedback and iterate on your models.

Share insights from experiences using large language models in conjunction with your projects, explaining how you integrated them with computer vision tasks, mentioning any specific architectures or implementations.

Be honest about your preferred tools, whether it’s TensorFlow, PyTorch, or others, and discuss why these tools suit your needs, perhaps focusing on their ease of use, flexibility, or community support.

Highlight your commitment to robust testing and validation methods, discussing techniques like cross-validation, thorough code reviews, and utilizing tools such as Weights and Biases for tracking model performance.

Provide a detailed description of a recent project, emphasizing its goals, challenges faced, methodologies employed, and the impact or results achieved, showcasing your problem-solving skills and technical expertise.

Share how you engage with the machine learning community through blogs, online courses, research papers, or conferences, emphasizing your proactive approach to continuous learning in this fast-evolving field.

Discuss a specific challenge, focusing on the steps you took to address the problem, the tools and techniques you employed, and the outcome of your efforts, highlighting your analytical and critical thinking skills.