Job description

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 trading cards, autographs, comic books, coins, video games, event tickets, and memorabilia. Our subsidiaries include PSA, PCGS, Beckett, SGC, and Card Ladder. Since our founding in 1986, we have graded and authenticated millions of items. We employ more than 3000 people across our headquarters in Santa Ana, California and offices in New Jersey, Texas, Florida, Japan, Shanghai, Hong Kong, Canada, Mexico, Germany, the UK, and France. As part of our interview process, we request that candidates have their cameras on during video interviews. This helps foster meaningful conversation and allows us to create an experience that closely resembles our standard working environment. Certain interview steps may take place by phone. For remote roles, and at our discretion, candidates may be asked to participate in an on-site interview as part of the final stages of the process. We understand there may be occasional circumstances requiring accommodation and are happy to discuss them as needed. Your recruiter will be able to clarify expectations and answer any questions you have. Collectors is the leading creator of innovative technology that provides value-added services for collectors worldwide. We grade, authenticate, vault, and help people research and manage the items they care about most. Our products bring authentication, grading, and trading into the modern era and make complex collector data more accessible. Our engineering mission is to build trusted, scalable systems that improve the experience for every collector. We’re looking for a Senior Machine Learning Engineer to join our AI/ML team and build applied machine learning systems from prototype through production. This role is ideal for an engineer who is equally comfortable training and evaluating models, designing production-ready pipelines, and shipping tools that help internal teams make better decisions. You’ll work across computer vision, structured data, and agentic AI workflows, with direct impact on products used by graders, researchers, and collectors. You’ll partner closely with product, engineering, operations, and domain experts to turn ambiguous problems into measurable, reliable ML solutions. What You’ll Do Design, build, and deploy end-to-end machine learning systems that support AI/ML product initiatives Own projects from problem framing through implementation, evaluation, launch, and post-launch monitoring, with clear accountability for outcomes Develop production-grade computer vision, multimodal, and agentic pipeline solutions that can ingest images and other inputs, reason over uncertainty, and return structured, reliable outputs Build and improve data pipelines, model evaluation frameworks, and feedback loops that ensure training and inference systems remain accurate, scalable, and maintainable over time Partner with annotation, operations, product, and domain experts to define data requirements, labeling standards, success metrics, and rollout plans Translate experimental findings into clear technical recommendations, balancing model quality, latency, cost, and operational complexity Contribute high-quality code, documentation, and technical design artifacts, and collaborate effectively through design reviews, code reviews, and cross-functional discussions What We’re Looking For 5 years of experience in machine learning, applied AI, or a closely related software engineering field Strong hands-on experience building and shipping machine learning systems in production, including data preparation, model training, evaluation, deployment, and monitoring Proficiency in Python and common ML tooling, along with solid software engineering fundamentals such as testing, modular design, observability, and version control Experience in one or more of the following areas: computer vision, multimodal modeling, information extraction, search/retrieval, or LLM-based systems Demonstrated ability to define statistically sound evaluation methodologies and make data-driven decisions about model readiness and trade-offs Strong judgment in system design, including how to build solutions that are scalable, maintainable, and practical for real-world operations Clear written and verbal communication skills, with the ability to explain technical decisions to both technical and non-technical stakeholders A high-ownership mindset with the ability to operate independently, navigate ambiguity, and drive work to completion Nice to Have Experience working on image-heavy or quality-sensitive domains such as computer vision inspection, authentication, fraud detection, or document/image understanding Experience with agentic systems, tool-using LLMs, verification loops, or workflows that combine models with retrieval and structured outputs Familiarity with cloud ML infrastructure, experiment tracking, and production inference patterns Experience collaborating with human-in-the-loop operations, annotation teams, or domain specialists to improve model quality Interest in collectibles, trading cards, marketplaces, or trust-and-safety style problems Collectors uses e-Verify to validate your ability to work legally in the United States. We are aware that there are instances where individuals are receiving job offers that fraudulently allege to be from Collectors or one of our business units. This type of fraud can be carried out through false websites, through fake e-mails claiming to be from the company or through social media. We never ask for personal information such as your bank account, Social Security numbers or National IDs, nor do we send or request payments for the purchase of business-related equipment. If you suspect fraud, please reach out to interviewfraud collectors.com. 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. If you require an accommodation to apply or interview with us due to a disability or special need, please email interviewaccommodation collectors.com U.S. residents: for disclosures relating to personal information we collect during the employment application and recruitment process, please see our Privacy Notice for U.S. Applicants .

Requirements

Must-haves

Our services span collectible trading cards, autographs, comic books, coins, video games, event tickets, and memorabilia. Our subsidiaries include PSA, PCGS, Beckett, SGC, and Card Ladder.
We understand there may be occasional circumstances requiring accommodation and are happy to discuss them as needed. Your recruiter will be able to clarify expectations and answer any questions you have.
Our engineering mission is to build trusted, scalable systems that improve the experience for every collector.
What You’ll Do Design, build, and deploy end-to-end machine learning systems that support AI/ML product initiatives
Own projects from problem framing through implementation, evaluation, launch, and post-launch monitoring, with clear accountability for outcomes
Develop production-grade computer vision, multimodal, and agentic pipeline solutions that can ingest images and other inputs, reason over uncertainty, and return structured, reliable outputs
Build and improve data pipelines, model evaluation frameworks, and feedback loops that ensure training and inference systems remain accurate, scalable, and maintainable over time
Partner with annotation, operations, product, and domain experts to define data requirements, labeling standards, success metrics, and rollout plans
Translate experimental findings into clear technical recommendations, balancing model quality, latency, cost, and operational complexity
Contribute high-quality code, documentation, and technical design artifacts, and collaborate effectively through design reviews, code reviews, and cross-functional discussions
What We’re Looking For 5 years of experience in machine learning, applied AI, or a closely related software engineering field
Strong hands-on experience building and shipping machine learning systems in production, including data preparation, model training, evaluation, deployment, and monitoring
Proficiency in Python and common ML tooling, along with solid software engineering fundamentals such as testing, modular design, observability, and version control
Experience in one or more of the following areas: computer vision, multimodal modeling, information extraction, search/retrieval, or LLM-based systems
Demonstrated ability to define statistically sound evaluation methodologies and make data-driven decisions about model readiness and trade-offs
Strong judgment in system design, including how to build solutions that are scalable, maintainable, and practical for real-world operations
Clear written and verbal communication skills, with the ability to explain technical decisions to both technical and non-technical stakeholders
A high-ownership mindset with the ability to operate independently, navigate ambiguity, and drive work to completion
Experience with agentic systems, tool-using LLMs, verification loops, or workflows that combine models with retrieval and structured outputs
Familiarity with cloud ML infrastructure, experiment tracking, and production inference patterns
Experience collaborating with human-in-the-loop operations, annotation teams, or domain specialists to improve model quality
Interest in collectibles, trading cards, marketplaces, or trust-and-safety style problems
Collectors uses e-Verify to validate your ability to work legally in the United States.

Nice-to-haves

Nice to Have Experience working on image-heavy or quality-sensitive domains such as computer vision inspection, authentication, fraud detection, or document/image understanding

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlpythonllm

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Vision