Hardware Engineer, Normal Computing
Design Verification
$140-180k
Offers Equity
Generative AI for enterprise
Be an early applicant
Generative AI for enterprise
21-100 employees
Be an early applicant
$140-180k
Offers Equity
21-100 employees
Company mission
To bridge artificial intelligence to the most sensitive industrial and advanced manufacturing applications around the globe.
Role
Who you are
- Experience: 5+ years of experience in Digital Verification at a major semiconductor or EDA tool company
- Technical Stack: Advanced proficiency in SystemVerilog, UVM methodology, EDA verification tools (vManager, Xcelium, Jasper), and proficiency and application of Python or Perl scripting
- Domain Knowledge: Proven expertise in end-to-end design verification, including test plan creation, stimulus generation, and feature extraction
- Communication: Excellent written and spoken communication skills
What the job involves
- You will bring your expertise in the end-to-end design verification flow to support our Verification AI team. This is a hybrid verification and product-shaping role
- You will verify internal hardware (Physics inspired ASICs) while simultaneously reviewing the collateral generated by our AI to help refine product strategy and tool usability
- You will act as the bridge between raw verification data and our Machine Learning models, ensuring our AI learns from high-quality, curated, and synthesized data
- AI Product Refinement: Review AI-generated collateral to help shape product strategy and refine AI outputs in collaboration with the ML team
- Thermodynamic ASIC Verification: Provide design verification for internal hardware projects
- Tool Usability: Set up and evaluate EDA tools, ensuring internal tool usability and effective deployment on shared computing resources
- Testbench Development: Verification collateral development: create testbench environments, assertions, and coverage, from design documents, to support product development, functional coverage, and coverage closure
- Dataset Annotation: Curate and annotate datasets to make it easier to associate specific parts of a chip specification with specific test cases
- Quality Control: Establish rigorous quality criteria for verification data and implement continuous refinement processes
- Automated QA: Implement data augmentation methods and automated quality assurance checks to ensure high-fidelity data for ML training
- Synthetic Data Creation: Generate synthetic data using AI-based methods to supplement real datasets
- ML Collaboration: Collaborate with ML teams to ensure synthetic data effectively challenges verification models
- Pipeline Automation: Build automated pipelines to annotate test data and link it explicitly to chip specifications
- Document Parsing: Automate document parsing (e.g., datasheets, protocol specifications) for contextual tagging and traceability
Our take
As the rate of AI adoption accelerates, Normal Computing is positioning itself to make advanced AI more usable and scalable for enterprises, where ris,, reliability and energy constraints remain major barriers. While generative AI has so far flourished in consumer settings, enterprise use has lagged due to concerns around control, auditability and cost. Normal's approach hopes to unlock these more complex use cases by rethinking how AI systems are built and deployed from the ground up.
At the core of the company's strategy is its Probabilistic AI framework, which gives customers greater control over reliability, adaptivity and auditability when working with private data. This software vision is now being reinforced by a major hardware milestone: the successful tape-out of CN101, the world's first thermodynamic computing chip. By harnessing natural physical dynamics such as stochasticity and noise, CN101 is designed to dramatically improve energy efficiency on AI and scientific workloads, allowing tasks like complex underwriting, probabilistic sampling and large-scale optimization to run faster and within fixed data centre energy budgets.
By tackling both the trust and energy bottlenecks that limit enterprise AI adoption, Normal is expanding its ambition from AI software into next-generation compute infrastructure. With a clear roadmap from CN101 to more advanced chips over the next few years, and substantial funding behind its R&D efforts, the company has the potential to play a meaningful role in shaping how high-performance AI scales in practice.
Freddie
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Nov 2024
$66.8m
GRANT
Jun 2023
$8.5m
SEED
Total funding: $83.8m
Company HQ
Civic Center, New York, NY
Leadership
Faris Sbahi
(Co-Founder & CEO)Previously worked as a Research Scientist at X, the moonshot factory, a Software Engineer at Google, and as a Forward Deployed Engineer at Palantir Technologies.
Matthias Tan
(Co-Founder & COO)Venture Partner at NextGen Venture Partners. Former Principal of Strategy and Partnerships at X, the moonshot factory, and Director of Product and Partnerships at Pony.ai.
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