Sr. Principal Machine Learning Platform Engineer

Lucid Motors

5500

Newark, CA, United States

Posted on: 2022-07-19

Category: emobility

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Expired

Employment type:

Full time

Experience required:

Senior

Salary

Salary not provided

About the company:

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.

Leading the future in luxury electric and mobility

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 Sr. 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.

At Lucid, we don’t just welcome diversity - we celebrate it! Lucid Motors is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, national or ethnic origin, age, religion, disability, sexual orientation, gender, gender identity and expression, marital status, and any other characteristic protected under applicable State or Federal laws and regulations.

Notice regarding COVID-19 protocols

At Lucid, we prioritize the health and wellbeing of our employees, families, and friends above all else. In response to the novel Coronavirus all new Lucid employees, whose job will be based in the United States may or may not be required to provide original documentation confirming status as having received the prescribed inoculation (doses). Vaccination requirements are dependent upon location and position, please refer to the job description for more details.

Individuals in positions requiring vaccinations may seek a medical and/or religious exemption from this requirement and may be granted such an accommodation after submitting a formal request to and the subsequent review and approval thereof by our dedicated Covid-19 Response team.

To all recruitment agencies: Lucid Motors does not accept agency resumes. Please do not forward resumes to our careers alias or other Lucid Motors employees. Lucid Motors is not responsible for any fees related to unsolicited resumes.

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 team members

Qualifications

15+ years of hands-on experience in ML pipeline, ETL, data modeling processing

8+ 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