• Take a leadership role in shaping the strategic direction of machine learning infrastructure • Identify opportunities for innovation and improvement • Drive the development of cutting-edge solutions for performance, scalability, and reliability of ML systems • Collaborate with Data Science team to ensure seamless deployment of models • Architect and oversee development of complex machine learning pipelines • Manage both real-time and batch inference systems • Mentor and guide mid-level Machine Learning Engineers • Conduct comprehensive code reviews and identify areas for improvement • Communicate effectively with both technical and non-technical stakeholders • Lead integration of new technologies into the infrastructure

• Research, design, and develop new machine learning solutions and algorithms for driverless technology company • Explore, prototype, validate, and iterate new algorithms and strategies • Drive efforts to optimize, supervise and refine on-road performance for models in production • Be considered one of the technical leaders of the department who guides current and future technology choices

• Collaborate with scientists, domain experts, software engineers and product teams to define requirements. • Support product owners in refining user stories and defining acceptance criteria. • Translate business requirements into technical AI solutions. • Communicate complex technical concepts to technical and non-technical stakeholders. • Provide technical guidance and mentorship to junior engineers. • Lead technical planning and execution of AI/ML initiatives. • Review code and ML implementations from team members. • Identify state-of-the-art AI technique (ML, Deep Learning, genAI etc.) and the needed data sets, to solve business problems. • Design and develop AI and ML models to solve complex business problems and drive automation and efficiency improvements in workflows. • Prepare data for AI models, including cleansing, transformation and feature engineering. • Train and fine-tune AI models, including the preparation of data sets, and incorporating pre-processing and post-processing steps to further refine AI model output. • Measure model accuracy. • Collaborate with solution and application architects to ensure successful integration of AI models with enterprise platform and use in production. • Identify and address issues related to scalability, performance, security, privacy, and reliability of ML models. • Work on deploying models in production environments, managing data pipelines, and ensuring scalability. • Contribute to the company’s intellectual property through innovative solutions. • Publish and present work at conferences.

• Design, train and improve upon cutting-edge models. • Help us develop new techniques to train and serve models safer, better, and faster. • Train extremely large-scale models on massive datasets. • Explore continual and active learning strategies for streaming data. • Learn from experienced senior machine learning technical staff. • Work closely with product teams to develop solutions.