$ cat jobs/senior-staff-machine-learning-engineer-tekion-92788350befd.json
Senior Staff Machine Learning Engineer
ABOUT TEKION: Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe. Summary: Responsible for leading the strategic design and development of scalable machine learning infrastructure and systems across the organization. As a top-tier individual contributor, this role provides deep technical expertise, sets engineering standards, and drives the long-term architecture of ML platforms. The Senior Staff ML Engineer partners with cross-functional leaders, mentors engineering teams, and delivers reliable, high-impact ML solutions at scale. Duties & Responsibilities: • Architect and lead the development of large-scale machine learning platforms and services that support model training, deployment, and lifecycle management. • Collaborate with Data Science, Applied Science, and Product teams to productionize ML models with performance, reliability, and compliance in mind. • Define and implement MLOps best practices, including model versioning, automated retraining, monitoring, and CI/CD workflows. • Lead technical decision-making and platform evolution to ensure scalability, security, and maintainability of ML infrastructure. • Optimiz
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