Senior Machine Learning Engineer

What You’ll Do

What We’re Looking For

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 [email protected].
• 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 [email protected]
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Qualifications

• 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

Responsibilities

• 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
• 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