Data AI Engineer (Remote)

Job Description

📋 Description
• Own the end-to-end lifecycle of data and AI systems that turn terabytes of raw silicon-design telemetry into crystal-clear insights for hardware, verification, and manufacturing teams worldwide. You will be the single-threaded owner who takes ideas from whiteboard sketches to production-grade services that influence billion-dollar product decisions.

• Architect and maintain scalable, cloud-native data pipelines that ingest simulation logs, EDA tool outputs, wafer-test results, and supply-chain feeds—processing millions of events per hour with sub-minute latency. Your designs must gracefully auto-scale from zero to thousands of cores during tape-out crunch periods and shrink back to save costs during quiet weeks.

• Design and productionize machine-learning models that predict yield excursions, flag potential reliability issues, and recommend design-for-manufacturability improvements before first silicon tape-out. These models will directly reduce costly respins and accelerate time-to-market for next-generation chips used in AI accelerators and automotive ECUs.

• Build self-service analytics layers (feature stores, semantic catalogs, governed APIs) so that design, verification, and operations engineers can explore data and run experiments without waiting for data-science tickets. Democratizing data access will unlock a 10× increase in hypothesis testing across the company.

• Create interactive, real-time dashboards and notebooks that surface KPIs such as power-performance-area trends, regression pass-rates, defect density heat-maps, and on-time delivery metrics—translating complex data into decisions that save weeks of schedule and millions of dollars. Stakeholders from VPs to junior engineers will rely on your visualizations daily.

• Implement robust MLOps practices—CI/CD for models, automated retraining, A/B testing, and drift detection—to ensure every algorithm deployed in production remains accurate, explainable, and compliant with ISO 26262 and other safety standards. You will define the golden path that turns Jupyter notebooks into bullet-proof micro-services.

• Partner closely with RTL designers, DV engineers, and foundry partners to understand domain-specific pain points, then translate those needs into reusable data products and micro-services that accelerate innovation across the company. Your ability to speak both “hardware” and “data” will be the bridge that shortens feedback loops.

• Champion data quality and governance by establishing schemas, lineage tracking, and anomaly-detection rules that keep our data lake pristine and audit-ready for both internal stakeholders and external customers. You will author the policies that let us pass SOC 2 and automotive customer audits without last-minute fire drills.

• Drive continuous performance tuning—leveraging Spark, Ray, or Dask for distributed compute, and optimizing storage formats (Parquet, Iceberg, Delta) to cut query costs and improve user experience. Expect to shave seconds off critical dashboards and reduce cloud spend by double-digit percentages quarter over quarter.

• Stay ahead of the curve by evaluating emerging AI techniques (graph neural networks for netlist analysis, transformer models for log parsing, reinforcement learning for test-pattern optimization) and running proof-of-concepts that can leapfrog our competitive edge. You will publish findings internally and, when appropriate, externally at venues like DAC or NeurIPS.

• Mentor junior engineers and data scientists, fostering a culture of experimentation, peer code reviews, and knowledge sharing through brown-bag sessions and internal tech talks. Your mentorship will shape the next generation of data leaders at FortifyIQ.

• Contribute to open-source communities and represent FortifyIQ at conferences, amplifying our reputation as a thought leader at the intersection of semiconductors and data science. Your talks and GitHub repos will attract top-tier talent to the team.