As the Machine Learning Enablement Engineering Manager within Ford’s Data Platforms and Engineering (DP&E) organization, you are a key leader responsible for guiding and developing a team of engineers focused on delivering high-impact, scalable machine learning solutions to address critical business challenges within DP&E. Your primary focus will be on building and maintaining the platform infrastructure and processes that empower data scientists and ML engineers to rapidly deploy and scale their solutions into production. You will work closely with Product Managers, Architects, Data Scientists, and other key stakeholders to drive engineering excellence, promote innovation, and uphold best practices. This role is less about building individual ML models and more about creating robust, reliable, and scalable solutions that allow others to deliver value effectively.
Your leadership is crucial in driving the success of our machine learning initiatives. Your ability to guide and develop a team of engineers, while maintaining alignment with Ford’s strategic goals, will be key to delivering world-class, production-ready ML solutions that power Ford’s transformation into a data-driven enterprise.
You should be a highly hands-on engineering leader with a proven track record of delivering complex, scalable solutions. While a deep understanding of ML concepts is beneficial, your primary focus will be on platform engineering, DevOps, and building robust, maintainable infrastructure. You will define processes for technical platforms, conceive application prototypes, and mentor your team in best practices. Your day-to-day responsibilities will involve designing and managing the organization's ML infrastructure architecture, ensuring data is efficiently processed, stored, and accessed to support ML model development and deployment. You will be pivotal in delivering these solutions on time and within budget.
I. Engineering Leadership & Management:
- Proven experience (7+ years) in a leadership role managing engineering teams, ideally with a focus on platform engineering, MLOps, or similar areas. Experience managing remote teams is a plus.
- Experience leading and mentoring engineering teams, fostering a culture of innovation, continuous learning, and technical excellence. Demonstrated ability to drive strategic technical decisions and ensure alignment with broader organizational goals.
- Proven ability to build and maintain high-performing teams, promoting accountability, ownership, and collaboration. Experience with performance management, including conducting performance reviews and providing constructive feedback.
- Excellent communication and interpersonal skills, with a proven ability to cultivate cross-functional collaboration and build strong relationships with stakeholders at all levels.
II. Agile & Scrum Practices:
- Deep understanding and practical experience with Agile methodologies (Scrum, Kanban), including facilitating daily stand-ups, sprint planning, backlog grooming, and sprint retrospectives.
- Experience working closely with Product Managers to align engineering efforts with product goals, ensure well-defined user stories, and manage priorities effectively.
- Proven ability to ensure engineering rigor in story hygiene, including clear acceptance criteria, well-defined dependencies, and a focus on deliverability within the sprint.
III. Technical Expertise & Accountability:
- Deep understanding of platform engineering principles and experience designing, building, and maintaining scalable and reliable infrastructure for ML workloads.
- Expertise in DevOps practices, including CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions), infrastructure-as-code (Terraform, Ansible, CloudFormation), and automation.
- Proficiency in at least one programming language (e.g., Python, Java) sufficient to effectively communicate with and guide your engineering team. You won’t be expected to contribute to team capacity by coding, but you need to be able to speak the language of your engineers.
- Strong understanding of cloud solutions and offerings (preferably GCP services – Compute Engine, Kubernetes Engine, Cloud Functions, BigQuery, Pub/Sub, Cloud Storage, Vertex AI). Experience with other major cloud providers (AWS, Azure) is also valuable.
- Experience with designing and implementing microservices and serverless architectures. Experience with containerization (Docker, Kubernetes) is highly beneficial.
- Experience with monitoring and optimizing platform performance, ensuring systems are running efficiently and meeting SLAs. Proven ability to lead incident management efforts and implement continuous improvements to enhance reliability.
- Commitment to best engineering practices, including code reviews, testing, and documentation. A focus on building maintainable and scalable systems is essential.
IV. Operational Excellence & Cost Optimization:
- Proven ability to drive cost optimization initiatives, particularly in cloud infrastructure and resource usage, aligning with Ford’s broader cost-reduction goals.
- Experience tracking and reporting key metrics for your domain/platform related to team performance, including quality and operational efficiency.
ML Enablement Engineering