Senior machine learning engineer

Collectors is the leading creator of innovative technology that provides value-added services for collectors worldwide. We grade, authenticate, vault, and sell millions of record-setting collectibles, all while modernizing and digitalizing the process to further our mission of helping collectors pursue their passions. We’re always on the lookout for talented people to join our growing team.

Our services span collectible coins, trading cards, Funko Pops!, video games, event tickets, autographs, and memorabilia. Our subsidiaries include PSA, PCGS, SGC, and Card Ladder.

Since our founding in 1986, we have graded and authenticated millions of items. We employ more than 1,900 people across our headquarters in Santa Ana and offices in Jersey City, Tokyo, Shanghai, Hong Kong, Toronto, Guadalajara, Dallas, and Paris.

Our AI/ML team is building tools to enhance and move forward a digital and physical Collectors ecosystem. We create innovative and groundbreaking tools that enhance the experience of internal stakeholders and employees as well as a diverse range of customers and businesses that depend on our tools and services.

We're looking for an experienced Senior Machine Learning Engineer that is ambitious, quick to learn, practical and outcomes driven. We value people who can quickly obtain a deep technical understanding of new domains, and enjoy working highly independently to solve our most important problems. Some of the nearest term projects they work on will entail using computer vision to drive digital grading, and building of ML models for card value estimation and recommendations.

What You’ll Do:

• Design, develop, and deploy machine learning and deep learning models for classification, regression, computer vision, and NLP/LLM use cases

• Lead end-to-end ML projects, including data collection, labeling strategies, feature engineering, model training, evaluation, deployment, and iteration

• Build and productionize scalable ML pipelines for training and inference

• Perform hyperparameter optimization, model selection, and performance tuning across large-scale datasets

• Implement production ready pipelines for ML prediction and training

Who You Are:

• Bachelor's degree in computer science, data science, mathematics, or a related field (Master's degree is a plus)

• 5+ years of dedicated experience as a Machine Learning Engineer or similar role

• Extensive knowledge of ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn)

• Familiarity with open-source models and ecosystems (e.g., Hugging Face)

• Ability to write production-quality, maintainable code and follow strong software engineering best practices in Python (experience with Go is a plus)

• Experience building computer vision, image recognition, and NLP models using state-of-the-art deep learning architectures

• Nice-to-have: Experience building and deploying end-to-end ML applications

• Understanding of data structures, data modeling and software architecture

• In-depth knowledge of mathematics, probability statistics, and algorithms

• Experience building computer vision, NLP, and transformer-based models, including LLM fine-tuning and prompt engineering

• Nice-to-have: Solid understanding of fine-tuning LLMs, retrieval-augmented generation (RAG), and agentic systems

• Superb analytical and problem-solving abilities

• Excellent communication and collaboration skills

• Passion for learning, a growth mindset, and the ability to work independently in a fast-moving, remote-first environment

Candidates must be authorized to work in the United States.

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We are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, national origin, gender, sex, gender identity or expression, sexual orientation, age, citizenship, marital or parental status, disability, veteran status, or other class protected by applicable law. We believe that a team that represents a variety of backgrounds, perspectives, and skills will better service the diverse community of collectors we support.

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[email protected]

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