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Synapse Analytics
As a Machine Learning Engineer II at Synapse Analytics, you will be a key individual contributor responsible for guiding the development of robust, scalable, and high-value models for our core products. You'll move beyond assisting with model implementation to taking ownership of more complex ML components and contributing significantly to the roadmap and strategy. You will also begin to mentor junior team members.
Design, implement, and manage the end-to-end lifecycle for significant ML models and features.
Ensure the value and feasibility of model requirements by collaborating closely with design and engineering teams.
Optimize model pipelines for maximum value, speed, and alignment with strategic goals.
Explore and visualize data to identify distributions that could affect performance, and define robust validation strategies.
Analyze model errors and design effective strategies to overcome them.
Collaborate closely with stakeholders, designers, and the development team to define requirements and deliver integrated solutions.
Clearly and regularly communicate model decisions, progress, and challenges with management and cross-functional teams.
Lead model refinement sessions for peers and junior engineers, providing in-depth constructive feedback.
Research and stay up-to-date with the latest market trends and user needs, actively proposing and evaluating new opportunities for the product.
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent work experience.
2-4 years of experience in a machine learning-focused role.
Excellent knowledge and extensive experience in Python and machine learning frameworks such as PyTorch, Tensorflow, and scikit-learn.
Strong understanding of and experience with computer vision techniques, including image/video processing, object detection, and recognition.
Excellent knowledge and experience in data preprocessing, feature extraction, data augmentation, and dimensionality reduction techniques.
Expertise in visualizing and manipulating big datasets.
Strong experience with UNIX/Linux environments, Git, and Docker.
Excellent troubleshooting skills for resolving requirement ambiguities and model performance conflicts.
Excellent understanding of the product development lifecycle, from discovery to deployment.
Experience with Kubernetes is a strong plus.
