Data & AI Engineer (Remote)
Quick Summary
We’re seeking a Data & AI Engineer to develop intelligent data pipelines and analytics solutions that power smarter decisions across silicon design, verification, and manufacturing. You’ll transform engineering data into actionable insights through automation, modeling, and visualization.
Build and maintain data pipelines to support machine learning and analytics workflows. Collect, clean, and transform large, complex datasets from engineering environments.
Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Strong skills in data manipulation (Pandas, NumPy, SQL). Experience with workflow orchestration (Airflow, Spark, or similar).
We’re seeking a Data & AI Engineer to develop intelligent data pipelines and analytics solutions that power smarter decisions across silicon design, verification, and manufacturing. You’ll transform engineering data into actionable insights through automation, modeling, and visualization.
Responsibilities
~1 min read- →Build and maintain data pipelines to support machine learning and analytics workflows.
- →Collect, clean, and transform large, complex datasets from engineering environments.
- →Develop and train predictive models for yield, performance, and anomaly detection.
- →Automate recurring data analysis tasks and integrate models into engineering processes.
- →Collaborate with design and software teams to embed AI-driven insights into products.
- →Create dashboards and visualization tools for reporting and decision-making.
- →Document code, models, and processes for transparency and reproducibility.
Requirements
~1 min read- Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Strong skills in data manipulation (Pandas, NumPy, SQL).
- Experience with workflow orchestration (Airflow, Spark, or similar).
- 3–5 years of experience in data engineering or applied AI.
- Bachelor’s degree in Electrical Engineering, Computer Science, or related field.
Nice to Have
~1 min read- Familiarity with semiconductor design, verification, or manufacturing datasets.
- Understanding of statistical modeling and predictive maintenance.
- Experience with cloud environments (AWS, Azure, GCP) and version control (Git).
- Knowledge of MLOps principles (deployment, monitoring, CI/CD).
Location & Eligibility
Listing Details
- First seen
- April 3, 2026
- Last seen
- May 23, 2026
Posting Health
- Days active
- 49
- Repost count
- 0
- Trust Level
- 30%
- Scored at
- May 23, 2026
Signal breakdown
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