Senior Machine Learning Engineer, Collectors

• 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

Desirable

• 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
• 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

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