Machine Learning Architect

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

On Site:  4 days a week in Pleasanton, CA office

  * Internally this role is called Associate Principal Machine Learning Engineer

The Machine Learning Architect will help shape the future of Automotive Industry. At Tekion we transform the end to end experience of customers in automotive. Are you motivated by solving tough problems, business-focused and have a passion for data driven decisions? The successful candidate will be a self-starter, comfortable with ambiguity, have an affinity for building from scratch, demonstrate strong attention to detail, and have the ability to work in a fast-paced, complex and ever-changing environment. 

You’ll be able to bring your unique skills and expertise to shape the direction of our Data Science and Machine Learning team. You will have an opportunity to work with state-of-the-art machine learning algorithms on large datasets.  

Key Responsibilities

• Serve as a liaison between R&D and stakeholders to ensure technical alignment with business objectives.
• Contribute to the development, scaling, and optimization of ML/AI solutions in alignment with company goals.
• Support shaping the R&D roadmap by collaborating with stakeholders to understand business needs.
• Develop APIs and microservices to enable seamless integration of ML models into production systems.
• Collaborate on designing and building feature pipelines for ML models.
• Ensure integration of ML models with front-end applications, databases, and back-end services.
• Collaborate with the team to publish findings and advance field knowledge in key areas.
• Work with machine learning engineers and cross-functional teams to support team growth and collaboration.
• Assist in building and maintaining MLOps pipelines for data collection, model training, validation, and monitoring.
• Implement version control, testing, and model governance practices as part of the ML lifecycle.
• Identify and address basic bottlenecks in ML models and services, with guidance from senior staff.
• Apply techniques like model optimization and distributed training under mentorship.
• Track metrics and support post-deployment optimization efforts.
• Collaborate with cloud architects and DevOps teams to design scalable ML infrastructure.
• Assist in the deployment and management of compute and storage resources for ML workflows.
• Partner with applied scientists and analysts to translate model requirements into production-ready solutions.
• Contribute to establishing monitoring and alerting systems for deployed models.
• Assist in creating and maintaining documentation for ML architecture and best practices.
• Stay informed about ML technologies and suggest relevant enhancements.

Required Qualifications

• Bachelor’s/Master’s in Computer Science or a related field.
• 7–10 years of hands-on experience as a Machine Learning Engineer or in a similar role, with a strong portfolio of deployed ML models.
• Strong problem-solving and analytical skills.
• Proficient in Python for model development and data manipulation.
• Familiarity with Java or Scala for production systems and microservices.
• Experience with messaging queues like Kafka or SQS.
• Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow).
• Working knowledge of cloud platforms (AWS, Google Cloud, Azure) and containerization (Docker, Kubernetes).
• Basic understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn).
• Familiarity with data stores like Elasticsearch, Postgres, MongoDB, or Redis.
• Experience with data processing tools (e.g., Apache Spark, Kafka).
• Familiarity with monitoring tools such as Grafana or Prometheus.

Preferred Qualifications

• Some experience with large-scale production systems or distributed computing.
• Understanding of data engineering practices and data warehousing solutions.
• Participation in open-source projects or the ML community is a plus.
• Strong interpersonal and communication skills for collaborating with cross-functional teams.
• A proactive mindset, with the ability to learn and adapt to new challenges

Perks and Benefits

• Competitive compensation and generous stock options   
• 100% employer-paid top-of-the-line medical, dental and vision coverage  
• Great benefits including unlimited PTO, parental leave and free snacks and beverages  
• The opportunity to work with some of the brightest minds from Silicon Valley’s most dominant and successful companies   
• Be part of an early stage, hyper-growth start-up with the opportunity to grow and prosper    
• Work on the latest and coolest technologies – everything is home-grown and built ground-up   
• A dynamic work environment with a strong sense of community and collaboration   
• The open and transparent culture that encourages innovation, rewards performance and discourages hierarchy   
• Exciting opportunities for career growth and development   

Current Tekion Employees – Please apply via Greenhouse Internal Job Board

Tekion is proud to be an Equal Employment Opportunity 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, victim of violence or having a family member who is a victim of violence, the intersectionality of two or more protected categories, or other applicable legally protected characteristics. 

For more information on our privacy practices, please refer to our Applicant Privacy Notice here.