Principal Machine Learning Platform Engineer

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

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.

Principal Machine Learning Platform Engineer is responsible to define and lead the Data and Machine learning Architecture, Performance, and Scalability of lucid ML operations, for both Vehicle and Operational data, ingesting, processing, and storing Trillions of rows of data per day. This hands-on role helps solve real big data problems, which most of the standard tools on the market are not capable of handling. You will be designing solutions, writing codes and automation, defining standards, and establish best practices across the company. 

The Role

• Lead the design and implementation of ML training and inference frameworks
• Design, implement and lead Data and Machine learning Architecture, Performance, and Scalability of Lucid ML operations. 
• Lead the design and deployment of large scale ML pipeline using open source technologies such as Kubeflow, MLFlow, Airflow
• Design and implement robust, automated, production-level software using horizontally scalable components
• Work effectively with cross-functional teams of engineers, product managers, and domain experts
• Design and build the next generation of ML architecture that will power large-scale data science projects
• Present project metrics and complex ML concepts to both technical and non-technical audiences
• Deep understanding of data design systems and experience handling large data sets
• Implement and manage industry best practice tools and processes such as Data Lake, S3, Spark ETL, Airflow, Kafka, MQTT, Timeseries Database, Presto, Kubernetes, Docker, CI/CD, DevOps
• Contribute to the overall architecture, implementation and ongoing maintenance of our codebase
• Optimize the performance and scale our data ingestion and processing infrastructure to server ever-increasing volume.
• 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.
• Provide direction and focus in areas of high ambiguity
• Mentoring junior team members

Qualifications

• MS. or PhD in Computer Science, or equivalent.
• 10+ years of hands-on experience in ML pipeline, ETL, data modeling processing
• 5+ years of hands-on experience in productionizing and deploying Big Data platforms and applications, Hands-on experience working with: Relational/SQL, distributed columnar data stores/NoSQL databases, time-series databases, Spark streaming, Kafka, Hive, Parquet, Avro, and more
• Expert in Spark, Kafka, Presto, Kubeflow, Airflow, or similar technologies. 
• Experience with Kubernetes-based ML Architecture. 
• Proven hands-on experience building solutions for large-scale data infrastructures and ML pipelines
• Have hands-on experience with Python based ML platform to implement large-scale data flows.
• Experience developing in a highly concurrent, multi-processor, and multi-threaded environment
• Experience with heterogeneous computing and GPGPU programming. 
• Strong knowledge and understanding of machine learning pipelines from standardization, normalization, clustering, modeling, scoring, validation
• Understanding of ETL engineering and tools so you can interface with data integration teams
• Experience working with various data infrastructure technologies such as SQL and data streaming/processing (Spark, Kafka, Airflow) is preferred