Job Description:
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. The ideal candidate has a deep understanding of machine learning engineering, cloud platforms, and data pipelines, coupled with strong technical leadership experience. As a thought leader, you will collaborate with cross-functional teams to develop innovative, data-driven solutions and seamlessly switch between strategic planning and tactical execution.
Key Responsibilities:
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Lead, mentor, and manage a team of machine learning engineers specializing in model development, deployment, and optimization.
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Foster a collaborative and innovative team environment, upholding our Engineering Operating Principles.
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Drive team performance, professional development, and skills advancement.
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Conduct regular performance reviews, set goals, and provide constructive feedback.
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Provide technical direction on ML system architecture, model deployment pipelines, and scaling solutions.
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Oversee the design and implementation of machine learning solutions that meet requirements for scalability, performance, and reliability.
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Collaborate with cross-functional partners, including product and design, to prioritize and plan the machine learning team’s work.
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Lead the team’s project execution, ensuring alignment with business goals and timely delivery.
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Ensure quality of deliverables, upholding standards for code quality, model performance, and system reliability.
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Own and maintain the ML services and platforms managed by the team.
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Accountable for system availability, setting up monitoring and alerting for key model metrics, and ensuring robust runbooks are in place.
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Act as the subject matter expert on machine learning infrastructure, helping address tactical issues as they arise.
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Continuously seek opportunities to improve ML system performance, reduce model training time, and increase deployment efficiency.
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Stay updated with the latest trends and technologies in machine learning engineering, MLOps, and data science tools.
Knowledge, Skills & Abilities:
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Hands-on experience in building ML pipelines, deploying models to production, and optimizing ML systems.
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Proficiency in programming languages such as Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-Learn.
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Strong understanding of data engineering, MLOps, API development, and cloud-based ML environments (AWS, Google Cloud, Azure).
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Proven experience leading and mentoring a team of ML or software engineers.
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Track record of delivering complex ML solutions on time with effective team management.
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Excellent communication skills, with the ability to explain ML concepts to non-technical stakeholders.
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Demonstrated experience building cross-functional relationships and fostering consensus.
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Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field.
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7+ years of experience in machine learning engineering, software engineering
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3+ years of experience in a technical leadership or management role.
Nice to Have:
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Experience with distributed computing and handling large-scale datasets.
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Knowledge of modern ML engineering and data science tools, including MLflow, Kubeflow, Spark, and containerization tools like Docker and Kubernetes.
The talent and dedication of our employees are at the core of DataRobot’s journey to be an iconic company. We strive to attract and retain the best talent by providing competitive pay and benefits with our employees’ well-being at the core. Here’s what your benefits package may include depending on your location and local legal requirements: Medical, Dental & Vision Insurance, Flexible Time Off Program, Paid Holidays, Paid Parental Leave, Global Employee Assistance Program (EAP) and more!
DataRobot Operating Principles:
- Wow Our Customers
- Set High Standards
- Be Better Than Yesterday
- Be Rigorous
- Assume Positive Intent
- Have the Tough Conversations
- Be Better Together
- Debate, Decide, Commit
- Deliver Results
- Overcommunicate
Research shows that many women only apply to jobs when they meet 100% of the qualifications while many men apply to jobs when they meet 60%. At DataRobot we encourage ALL candidates, especially women, people of color, LGBTQ+ identifying people, differently abled, and other people from marginalized groups to apply to our jobs, even if you do not check every box. We’d love to have a conversation with you and see if you might be a great fit.
DataRobot is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. DataRobot is committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. Please see the United States Department of Labor’s EEO poster and EEO poster supplement for additional information.
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