About Team:
Target’s ML platform provides a unified experience and collection of toolsets to data scientist and ML engineers to build, train, deploy, monitor and manage machine learning models consistently and reliably. The integrated workflow and pipelines enable designing long running process using multiple models and services. We use the latest in open source technology and as committers on some of these projects, we are pushing the envelope.
About the Role:
The key to the success of this position is having strong & innovative approach to problem solving, great technical leadership, excellent communication (written and verbal, formal and informal), flexibility, and a self-motivated working style with attention to detail.
Use your skills, experience, and talents to be a part of groundbreaking thinking and visionary goals. As a Principal Engineer, you are/have/can…
- Create architecture for ML platform across the entire lifecycle from data management to predict/ inferencing (model in prod) working with partner product teams. Lead the design and build with production first mindset.
- Hands-on engineer in multiple programming languages & frameworks.
- Excellent System/Platform Engineer.
- Experience in building Data Engineering systems / pipelines.
- Deep understanding on model registry and associated metrics to for data linage and optimal performance.
- Understanding of feature management and associated platforms.
- Apply the science of utilizing data to solve performance and processing problems of dealing with big data and build web scale systems with high reliability.
- Collaborate with internal teams, data-scientists & larger Target tech community to extend existing capabilities at enterprise scale.
- Build network in industry, follow emerging trends from established enterprises and startup ecosystem and continuously innovate and drive POCs/ POTs
Requirements:
- Experience with large scale enterprise system
- Hands on experience with one or more programming language (java, python, node, golang, micronaut, etc.)
- Experience with ML platform and have build ML tools and products
- Exposure to open source libraries - Tensorflow, OpenCV, Fastai, Pytorch, MLFlow, KubeFlow, Hadoop, Spark, Flink, Kafka, MLLib, DataBricks etc. Demonstrated proficiency with performance computing systems such as Hadoop/Spark/Flink, and working knowledge of ETL systems, and data models.
- Experience with using and debugging highly distributed, fault tolerant, and fast streaming
systems and real time data pipelines.
