Senior Manager, Machine Learning

7 days ago

Boston, United States DataRobot Full time

DataRobot is the leader in Value-Driven AI, a unique and collaborative approach to generative and predictive AI that combines an open platform, deep expertise and broad use-case experience to improve how organizations run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with an organization’s existing investments in data, applications and business processes, and can be deployed on prem or on any cloud environment. Global organizations, including 40% of the Fortune 50, rely on DataRobot to drive greater impact and value from AI.

As a Senior Manager of Machine Learning Engineering, you will lead teams focused on building and optimizing machine learning systems and data pipelines that power our AI products. This role is responsible for shaping the strategic and technical direction of the machine learning engineering team, ensuring the development of scalable and reliable ML models and systems. You will be hands-on in building a robust infrastructure that supports model training, deployment, and monitoring at scale.

Key Responsibilities:
  1. Lead, mentor, and manage a team of machine learning engineers specializing in model development, deployment, and optimization.
  2. Foster a collaborative and innovative team environment, upholding our Engineering Operating Principles.
  3. Drive team performance, professional development, and skills advancement.
  4. Conduct regular performance reviews, set goals, and provide constructive feedback.
  5. Provide technical direction on ML system architecture, model deployment pipelines, and scaling solutions.
  6. Oversee the design and implementation of machine learning solutions that meet requirements for scalability, performance, and reliability.
  7. Collaborate with cross-functional partners, including product and design, to prioritize and plan the machine learning team’s work.
  8. Lead the team’s project execution, ensuring alignment with business goals and timely delivery.
  9. Ensure quality of deliverables, upholding standards for code quality, model performance, and system reliability.
  10. Own and maintain the ML services and platforms managed by the team.
  11. Accountable for system availability, setting up monitoring and alerting for key model metrics, and ensuring robust runbooks are in place.
  12. Act as the subject matter expert on machine learning infrastructure, helping address tactical issues as they arise.
  13. Work closely with product managers, data scientists, and other stakeholders to understand requirements and deliver ML solutions that meet business needs.
  14. Continuously seek opportunities to improve ML system performance, reduce model training time, and increase deployment efficiency.
  15. Stay updated with the latest trends and technologies in machine learning engineering, MLOps, and data science tools.
Knowledge, Skills & Abilities:
  1. Hands-on experience in building ML pipelines, deploying models to production, and optimizing ML systems.
  2. Proficiency in programming languages such as Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-Learn.
  3. Strong understanding of data engineering, MLOps, API development, and cloud-based ML environments (AWS, Google Cloud, Azure).
  4. Proven experience leading and mentoring a team of ML or software engineers.
  5. Track record of delivering complex ML solutions on time with effective team management.
  6. Excellent communication skills, with the ability to explain ML concepts to non-technical stakeholders.
  7. Demonstrated experience building cross-functional relationships and fostering consensus.
  8. Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field.
  9. 7+ years of experience in machine learning engineering, software engineering.
  10. 3+ years of experience in a technical leadership or management role.
Nice to Have:
  1. Experience with distributed computing and handling large-scale datasets.
  2. Knowledge of modern ML engineering and data science tools, including MLflow, Kubeflow, Spark, and containerization tools like Docker and Kubernetes.

DataRobot is committed to providing a safe and secure environment for all job applicants. We encourage all job seekers to be vigilant and protect themselves against recruitment scams by verifying the legitimacy of any job offer before providing personal information or paying any fees.

Thank you for your interest in DataRobot, and we look forward to receiving your application through our official channels.

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