Impinj
Impinj is a leading RAIN RFID provider and Internet of Things pioneer. We’re inventing ways to connect every thing to the Internet — including retail apparel, retail general merchandise, healthcare items, automobile parts, airline baggage, food and much more. With more than 100 billion items connected to date, and multiple Fortune 500 enterprises around the world using our platform, we solve for a better understanding of our world. If it’s a thing, we’re working to connect it. Join Impinj and help us realize our vision of a boundless IoT — connecting trillions of everyday items to the Internet.
We are seeking a
Staff Data Engineer
with deep experience in managing and processing high-volume IoT data to enable the cloud-based training of machine learning models that power real-time inference on edge devices. In this role, you will architect and maintain cloud-based data infrastructure and pipelines that support ML workflows for training, validation, and deployment of machine learning models optimized for deployment in edge environments. This is a multi-functional role requiring close collaboration with ML engineers, systems engineers, cloud architects, and embedded systems teams to deliver high-quality, efficient, and scalable data solutions that power intelligent behavior on resource-constrained devices such as fixed and handheld RFID readers. What you Will Do:
Design data workflows to support model training, evaluation, and retraining cycles for deployment on edge devices Work closely with ML engineers to align data formats, labeling standards, feature extraction for edge-compatible models, and feedback loops for model improvement Architect and maintain scalable data pipelines to ingest, process, store, and access large volumes of structured and semi-structured RFID time-series data from edge networks Develop automated systems for data versioning, labeling, augmentation, and quality assurance Establish and maintain data APIs and interfaces to query, consume, and update datasets Manage large datasets using distributed storage and compute frameworks (e.g., Apache Spark, Hadoop, or Dask) Ensure data security, compliance, and consistency across the full data lifecycle Drive improvements in data performance and reliability, especially for low-latency ML inference use cases Implement robust ETL/ELT workflows for preparing data for cloud-based ML model training and evaluation Collaborate and coordinate with large scale data collection projects Monitor and optimize data pipelines for performance, reliability, and cost across edge-to-cloud infrastructure Optimize data flow and compute for performance, cost, and latency in hybrid edge-cloud environments What You Will Bring:
Bachelor’s degree in Data Engineering, Electrical Engineering or a related field and 8 years of related experience, or equivalent combination of education and experience 8+ years of experience in data engineering working with Machine Learning pipelines Deep understanding of data pipeline design, ETL/ELT processes, automated workflow orchestration (e.g. Apache Airflow) Strong programming skills in Python (especially for data workflows), with experience building scalable, maintainable pipelines. (e.g. Pandas, numpy) Strong experience with structured and unstructured databases (SQL, MongoDB, DuckDB) Strong understanding of cloud infrastructure (AWS, Azure, or GCP), especially cloud storage, compute, and ML tools (e.g., SageMaker, Vertex AI, Azure ML) Experience with data lake/data warehouse technologies (e.g., S3 + Glue, BigQuery, Snowflake, Delta Lake) Knowledge of machine learning model lifecycles, including training, validation, and deployment Understanding of data versioning, feature engineering, and ML lifecycle management Understanding of machine learning data needs, including labeling, versioning, and model-ready dataset preparation Familiar with distributed data systems and big data tools (e.g., Spark, Kafka, Hadoop) Compensation & Benefits:
The typical base pay range for this role across the US is $129,000 - $200,000. Individual base pay depends on various factors such as complexity and responsibility of role, job duties, requirements, and relevant experience and skills. Equal Employment Opportunity:
We are an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
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Staff Data Engineer
with deep experience in managing and processing high-volume IoT data to enable the cloud-based training of machine learning models that power real-time inference on edge devices. In this role, you will architect and maintain cloud-based data infrastructure and pipelines that support ML workflows for training, validation, and deployment of machine learning models optimized for deployment in edge environments. This is a multi-functional role requiring close collaboration with ML engineers, systems engineers, cloud architects, and embedded systems teams to deliver high-quality, efficient, and scalable data solutions that power intelligent behavior on resource-constrained devices such as fixed and handheld RFID readers. What you Will Do:
Design data workflows to support model training, evaluation, and retraining cycles for deployment on edge devices Work closely with ML engineers to align data formats, labeling standards, feature extraction for edge-compatible models, and feedback loops for model improvement Architect and maintain scalable data pipelines to ingest, process, store, and access large volumes of structured and semi-structured RFID time-series data from edge networks Develop automated systems for data versioning, labeling, augmentation, and quality assurance Establish and maintain data APIs and interfaces to query, consume, and update datasets Manage large datasets using distributed storage and compute frameworks (e.g., Apache Spark, Hadoop, or Dask) Ensure data security, compliance, and consistency across the full data lifecycle Drive improvements in data performance and reliability, especially for low-latency ML inference use cases Implement robust ETL/ELT workflows for preparing data for cloud-based ML model training and evaluation Collaborate and coordinate with large scale data collection projects Monitor and optimize data pipelines for performance, reliability, and cost across edge-to-cloud infrastructure Optimize data flow and compute for performance, cost, and latency in hybrid edge-cloud environments What You Will Bring:
Bachelor’s degree in Data Engineering, Electrical Engineering or a related field and 8 years of related experience, or equivalent combination of education and experience 8+ years of experience in data engineering working with Machine Learning pipelines Deep understanding of data pipeline design, ETL/ELT processes, automated workflow orchestration (e.g. Apache Airflow) Strong programming skills in Python (especially for data workflows), with experience building scalable, maintainable pipelines. (e.g. Pandas, numpy) Strong experience with structured and unstructured databases (SQL, MongoDB, DuckDB) Strong understanding of cloud infrastructure (AWS, Azure, or GCP), especially cloud storage, compute, and ML tools (e.g., SageMaker, Vertex AI, Azure ML) Experience with data lake/data warehouse technologies (e.g., S3 + Glue, BigQuery, Snowflake, Delta Lake) Knowledge of machine learning model lifecycles, including training, validation, and deployment Understanding of data versioning, feature engineering, and ML lifecycle management Understanding of machine learning data needs, including labeling, versioning, and model-ready dataset preparation Familiar with distributed data systems and big data tools (e.g., Spark, Kafka, Hadoop) Compensation & Benefits:
The typical base pay range for this role across the US is $129,000 - $200,000. Individual base pay depends on various factors such as complexity and responsibility of role, job duties, requirements, and relevant experience and skills. Equal Employment Opportunity:
We are an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
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