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Data & AI Engineer (Remote)
FortifyIQ · Remote · Remote · Active · Greenhouse
Job facts
| Field | Value |
|---|---|
| Company | FortifyIQ |
| Title | Data & AI Engineer (Remote) |
| Normalized title | - |
| Department / team | IT Support |
| Location | United States |
| Work model | Remote / Remote |
| Employment type | - |
| Salary | - |
| Status | active |
| ATS provider | Greenhouse |
| Posted / first seen | 2026-02-12 / 2026-05-29 |
| Changed / last seen | 2026-06-03 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from FortifyIQ. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Greenhouse. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| Department jobs | Active postings in IT Support. | Open |
| Work model jobs | Active Remote postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | FortifyIQ |
| Source | 46d1a2af-70cf-4ada-aafe-ed8b714a27da |
| ATS provider | Greenhouse |
Description
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
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.
Qualifications
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.
Preferred / Plus
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).
Full job record
| Job ID | e91814d3989e6354cb6b4dfa5c8b6290f140c370 |
| Org ID | f4fabd70-af36-4304-86b9-4a5b4552bfe9 |
| Source ID | 46d1a2af-70cf-4ada-aafe-ed8b714a27da |
| Board ID | 46d1a2af-70cf-4ada-aafe-ed8b714a27da |
| Provider | greenhouse |
| Provider Job Key | 4661408005 |
| Title | Data & AI Engineer (Remote) |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | Remote |
| Department | IT Support |
| Team | — |
| Employment Type | — |
| Workplace Type | remote |
| Remote Policy | remote |
| Country | United States |
| Region | — |
| City | — |
| Salary Raw | — |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://job-boards.greenhouse.io/fortifyiq/jobs/4661408005 |
| Apply URL | https://job-boards.greenhouse.io/fortifyiq/jobs/4661408005 |
| First Seen At | 2026-05-29 22:58:18Z |
| Last Seen At | 2026-06-06 20:05:25Z |
| Last Checked At | 2026-06-06 20:05:25Z |
| Last Changed At | 2026-06-03 10:41:08Z |
| Inactive At | — |
| Source Posted At | 2026-02-12 19:13:33Z |
| Source Updated At | 2026-06-02 18:08:02Z |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=greenhouse/board=fortifyiq/date=2026-06-06/2026-06-06T20-05-25-703Z-2d9e7d5c025ac8d16b41ad56d0b3d2cfda41ad437f80ec1bcdb5c1fd875bcac9.json |
Event Fields
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