Blend360 LLC

Blend360 LLC

AI and Data Engineer

Edmonton, AB, Canada · Mid

Sponsorship not specifiedDetected 88 days ago
PythonCode ReviewSnowflakeVector DatabasesAWSGCPAzureKafkaMachine LearningSparkAirflowdbtData EngineeringData ScienceNLPLLMsRAGLangGraphResearch

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odds of building a lasting career here

40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Employer immigration record

from this employer's Department of Labor filings

Green-card intent detected

Blend360, LLC obtained a prevailing wage determination for Data Scientists in Columbia, MARYLAND on 2026-06-24. No matching green-card filing appears in our data yet. The determination expires in 15 days (2026-09-21), and a green-card filing must follow before then or the employer starts over.

Files H-1B transfers

2 transfer filings in the last year, covering 2 workers. Median labor-condition decision: 8 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people.
  • The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy.
  • We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients.

Responsibilities

  • Design and build a scalable platform and the data pipelines that ingest, transform, and serve structured and unstructured data to AI models
  • Develop and iterate on AI/ML components - including LLM-based agents, embedding models, and behavioral simulation layers - that power synthetic persona generation
  • Architect and maintain the data infrastructure underpinning persona modeling: feature stores, vector databases, data lakes, and real-time serving layers
  • Collaborate with researchers to translate persona logic and behavioral frameworks into working system components
  • Contribute to system design discussions and help shape the technical roadmap
  • Experience building and deploying ML models or AI components in a production environment
  • Solid understanding of data modeling, schema design, and the tradeoffs between different storage paradigms (relational, document, vector, columnar)

Requirements

  • Experience with APIs, microservices, or event-driven architectures (Kafka, Pub/Sub, or similar)
  • Comfort working across the stack - from raw data ingestion through to model serving and API exposure
  • you can move forward when the requirements are still forming
  • Must be able to commute to our Edmonton, AB, Canada office
  • Proficiency in Python and at least one data processing framework (Spark, dbt, Airflow, Prefect, or similar)
  • Experience with cloud data infrastructure - AWS, GCP, or Azure - and comfort operating in a cloud-native environment

Nice to have

  • Experience with Snowflake
  • Experience with behavioral modeling, simulation, or agent-based systems
  • Background in NLP, computational social science, or user modeling
  • Contributions to open-source AI or data tooling
  • Familiarity with synthetic data generation techniques or privacy-preserving ML
  • Experience working in a startup or early-stage product environment
  • Synthetic personas are one of the most technically interesting and practically consequential challenges in applied AI right now.
  • You will have direct influence over architectural decisions, meaningful ownership of your components, and a front-row seat to a research area that is evolving fast.
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This listing is sourced directly from Blend360 LLC's careers page and normalized into a canonical job model.