Remote
ML Data Pipeline Engineer (Remote)
We’re looking for an ML Data Pipeline Engineer for our Harvard-partnered client, that pioneers in LLM building. In this role, you’ll build and optimize data pipelines that handle scientific data from domains like X-ray imaging, seismic analysis, and medical diagnostics. Your work will directly contribute to advancing AI that understands and interprets real-world physical data.
Up to $100,000 USD per year
100% Remote
Full-time
CoE / B2B / EoR Requirements
5+ years of experience designing and building scalable data pipelines
High proficiency in Python
Solid grasp of data workflows tailored for machine learning applications
Proficiency in working with scientific data types such as imaging, time-series, and sensor outputs, along with related storage systems
Practical experience building and executing data quality checks and validation pipelines
Knowledge of processing and transforming data from multiple modalities
Experience or familiarity with scientific fields like healthcare, energy, or materials science is an advantage Responsibilities
Design and refine scalable data pipelines for diverse scientific data sources, including X-ray, seismic, medical imaging, and materials analysis
Convert unprocessed physical measurements into well-structured, machine learning-ready datasets
Create metadata enrichment processes to enhance the contextual value of training data
Establish robust validation procedures to maintain the integrity and quality of datasets
Work hand-in-hand with ML engineers to ensure data formats align with model training requirements
Maintain clear, accessible documentation and data catalogs to support cross-functional collaboration
Improve data storage and access systems to support efficient training of large-scale ML models Please include GDPR consent
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Remote
ML Data Pipeline Engineer (Remote)
7 000 - 8 300 USD
net/month - B2B
net/month - B2B
apply via email
7,000 - 8,300 USD
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