Sr. Data, ML 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.

Senior Machine Learning and Data Engineer is responsible to build Machine learning applications, for both Vehicle and Operational data, building, tarining and running ML models. 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. 

• Design and develop ML pipeline using PySprak, Scala, SQL or Python.
• Deployment of large scale ML pipeline using open source technologies such as Kubeflow, MLFlow, Airflow
• Drive predictive analytics from billions of time series data points
• Articulate business questions and use mathematical techniques to arrive at an answer using data. Translate analysis results into business recommendations.
• Design and build the next generation of ML architecture that will power large-scale data science projects
• Adapt machine learning and data mining algorithms to solve problems across several teams
• Perform model training, hyper parameter tuning and model parallelization 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.
• Perform and streamline continuous model performance monitoring and debugging in production
• Perform research and utilize state-of-the-art and best practices for model compression, quantization and optimization for deployment
• 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
• 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

The Role

• Design and develop ML pipeline using PySprak, Scala, SQL or Python.
• Deployment of large scale ML pipeline using open source technologies such as Kubeflow, MLFlow, Airflow
• Drive predictive analytics from billions of time series data points
• Articulate business questions and use mathematical techniques to arrive at an answer using data. Translate analysis results into business recommendations.
• Design and build the next generation of ML architecture that will power large-scale data science projects
• Adapt machine learning and data mining algorithms to solve problems across several teams
• Perform model training, hyper parameter tuning and model parallelization 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.
• Perform and streamline continuous model performance monitoring and debugging in production
• 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.

Qualifications

• BS or MS degree n Computer Science, or equivalent.
• 6+ years of hands-on experience in ML pipeline, ETL, data modeling processing
• 3+ years of hands-on experience in productionizing and deploying ML applications. 
• Expert in Spark, Kafka, Presto, Kubeflow, Airflow, or similar technologies. 
• Proficiency with machine/deep learning frameworks such as TensorFlow, Keras, Pytorch, Caffe, MXNet, etc
• Experience in creating production level ML models for training, validation, and inference leveraging real-time systems
• Experience with Kubernetes-based ML Architecture. 
• 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

Preferred Qualifications

• Experience using Timeseries data
• Experience with automotive data
• Experience working with cloud-based accelerated computing, GPU/TPU, CUDA, parallel computing