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Lead AI/ML Engineer

Job

CRATE & BARREL

Remote

Full-Time

Posted 3 days ago (Updated 14 hours ago) • Actively hiring

Expires 7/11/2026

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Job Description

We are seeking a highly experienced and passionate Lead AI/ML Engineer to guide our core data science initiatives. With 8+ years of dedicated experience in the AI/ML domain, you won't just be optimizing algorithms; you'll be setting the technical vision for our next generation of intelligent products. This role offers the unique opportunity to bridge the gap between cutting-edge research and real-world impact, overseeing the entire ML lifecycle—from ideation and experimentation to This position is fully remote This role is an Individual Contributor position A day in the life as a Lead AI/ML Engineer... Provide strong technical leadership and guidance to the machine learning engineering team, setting the technical vision and ensuring alignment with product goals Lead the design, implementation and rigorous evaluation of highly scalable and performant ML models (e.g., deep learning, NLP, computer vision) to solve complex business problems Oversee and drive the full machine learning lifecycle, including data ingestion, model training, validation, deployment, and monitoring in production environments Actively participate in and champion team ceremonies contributing to the successful delivery of sprint goals and continuous process improvement Collaborate effectively with product managers, data scientists and other stakeholders to define requirements, author user and technical stories, and translate business needs into technical specifications Champion and implement best practices for MLOps (e.g., CI/CD, feature stores, model versioning) to ensure reproducible and reliable deployments Set coding standards, lead code reviews, and ensure best practices in source control management Mentor and guide junior and mid-level engineers, fostering their technical growth, providing constructive feedback, and promoting a collaborative team environment Establish and champion high standards for knowledge management within the team, ensuring clear, comprehensive, and easily accessible documentation for all developed features and solutions, significantly enhancing team efficiency and codebase maintainability Stay up-to-date with the latest technologies and trends, proactively identifying opportunities for improvement and innovation within the engineering processes and technology stack Identify and mitigate technical risks, ensuring the timely and successful delivery of features and solutions What you'll bring to the table... Deep expertise with Python and standard ML/scientific computing libraries (e.g., TensorFlow, PyTorch, scikit-learn, Pandas) Deep theoretical and practical understanding of various machine learning algorithms, including modern deep learning architectures and methodologies Demonstrated expertise with MLOps tools and cloud-native services (e.g., Kubeflow, MLflow, Docker, Kubernetes) Strong experience designing and deploying scalable ML solutions on major cloud platforms (AWS, Azure, or GCP) Solid understanding of data warehousing, ETL processes, and familiarity with distributed computing frameworks (e.g., Spark) Ability to design, articulate, and implement complex, highly available, and fault-tolerant AI production systems Proven ability to set coding standards, lead code reviews, and manage software development lifecycles Exceptional problem-solving, analytical, and strategic thinking skills Strong technical leadership skills with the ability to set technical vision and guide a team Deep understanding of agile software development methodologies and the software development lifecycle We'd love to hear from you if you have... 8+ years of progressive experience in machine learning engineering Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field Demonstrated experience successfully taking multiple ML models from research/prototype phase into large-scale production environments Prior experience working with big data technologies and designing robust data pipelines for ML training