SeniorFull TimeEN
The Role:
The Inbound team develops highly-scalable solutions to determine item attribute values (e.g., color, material, style) and to verify merchandise authenticity. Senior Applied Scientists own the development and deployment of machine learning solutions and influence the technical direction of their team. They will work closely with tech-leads, Product and Engineering partners in the development of the solution.
Responsibilities:
Develop and deploy Computer Vision and Machine Learning solutions to solve business problems.
Maintain clean, efficient, and scalable code that meets industry standards
Analyze large datasets to extract actionable insights and make informed decisions.
Employ state-of-the-art Machine Learning methodologies and frameworks to develop robust and scalable models.
Influence technical direction and take ownership of key components of systems and solutions, ensuring that they meet the needs of the business.
Collaborate with key stakeholders in the development of data-driven solutions and deployable products.
Mentor other team members to help establish team domain expertise.
Contribute to the company's intellectual property and technical leadership through patents and publications at top-tier conferences and journals.
Minimum Requirements:
5+ years of industry experience in Computer Vision and applied Machine Learning
Masters Degree or PhD in CS / ML, statistics, or related field, or 8+ years of industry experience.
3+ years experience in building, deploying, and managing machine learning and deep learning models in production environments at scale
Deep understanding of Computer Vision and ML algorithms/techniques (CNNs, transformers, GANs, optimizers, regularization) and experiment design and best practices (A/B testing, training/serving pipelines, feature engineering).
Extensive experience in scientific libraries in Python (numpy, pandas) and Machine Learning tools and frameworks (PyTorch, Tensorflow, Keras, Scikit-Learn)
Strong data engineering skills and experience working with large scale datasets
Experience with experiment automation frameworks (Ray Tune, W&B, Kubeflow)
Experience with cloud technologies AWS, GCP or Azure
Fluency in Python
Preferred Requirements:
PhD preferred (CS, ML, AI, Stats, OR or related field)
Background in applying ML techniques to solve real-world business problems in the retail sector.
Familiarity with MLOps tools and pipelines.
Impact-focused and passionate about delivering high-quality models
