Hardware Engineer, Normal Computing
Design Verification
$140-180k
+ Equity
Generative AI for enterprise
Open for applications
Generative AI for enterprise
21-100 employees
Open for applications
$140-180k
+ 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 sets out to make it more usable and scalable for enterprises, where risk is a central barrier to its implementation. The hope is that this will allow enterprises to make use of technologies such as Generative AI which has so far only been considered appropriate for early consumer applications.
The company's Probabilistic AI gives control over reliability, adaptivity, and auditability to AI models powered by its customers' private data. It boasts the ability to support such applications as automating complex underwriting processes, generating and validating specialized code, and mitigating risks in situations where the conditions are dynamic and fluctuating.
By targeting a major roadblock to enterprise AI adoption, Normal Computing has the chance to make a major (and profitable) contribution to accelerating complex AI use cases. The company has raised considerable funding to support its endeavours, which it will use to double down on the R&D of its application development platform and Probabilistic AI technology.
Freddie
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Nov 2024
$67.3m
GRANT
Jun 2023
$8.5m
SEED
Total funding: $84.3m
Company HQ
Civic Center, New York, NY
Leadership
Faris Sbahi
(Co-Founder & CEO)Has 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)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.
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 6 more jobs at Normal Computing