Sr. Machine Learning Platform Engineer

Lucid is the new generation of EV. Our relentless focus on innovation, luxury, and sustainability drives us into a reality where you no longer have to choose between doing great things, doing the right thing, and doing everything with the highest regard for efficiency and design. There were luxury cars, then EVs, now there’s Lucid.  

At Lucid, we set out to introduce the most captivating, luxury electric vehicles that elevate the human experience and transcend the perceived limitations of space, performance, and intelligence. Vehicles that are intuitive, liberating, and designed for the future of mobility.

 

We plan to lead in this new era of luxury electric by returning to the fundamentals of great design – where every decision we make is in service of the individual and environment. Because when you are no longer bound by convention, you are free to define your own experience.

 

Come work alongside some of the most accomplished minds in the industry. Beyond providing competitive salaries, we’re providing a community for innovators who want to make an immediate and significant impact. If you are driven to create a better, more sustainable future, then this is the right place for you.

The Role

· Build a world-class ML platform for the Lucid engineering and data teams to use, supporting all aspects of future car design

· Design the next generation of ML architecture that will power large-scale data science projects

· Develop large-scale ML pipelines using PySpark, Scala, SQL, and Python.

· Deploy pipelines using open source technologies (e.g. Kubernetes, MLFlow, Spark, Airflow) and modern software design principles to promote modularity, repeatability, scalability, and fault tolerance

· Drive predictive analytics from billions of time series data points

· Articulate business questions and use mathematical and statistical techniques such as hypothesis testing to arrive at an answer using data. Translate analysis results into business recommendations for company leadership.

· Adapt machine learning, deep learning, and data mining algorithms to solve problems across the Lucid engineering enterprise

· Enable users to perform model training, hyperparameter tuning, and model parallelization (e.g. using scikit-learn, Spark MLlib, PyTorch, and TensorFlow) and distributed training to achieve top performance for accuracy and latency

· Lead projects with hands-on analysis and modeling, drawing from multiple analytical methods to choose the right tool and right level of complexity appropriate for the challenge.

· Monitor and streamline continuous model performance in production on live vehicles, and respond to critical events

· Use industry best practice tools and processes such as Data Lake, Delta Lake, S3, Spark ETL, Airflow, Hive Catalog, Ranger, Redshift, Spline, Kafka, MQTT, Timeseries Database, Cassandra, Redis, Presto, Kubernetes, Docker, CI/CD, DevOps

· Contribute to the overall architecture, implementation and ongoing maintenance of our codebase

· Translate big data and analytics requirements into data models that will operate at a large scale and high performance and guide the data analytics engineers on these data models.