Sr. Staff Formal Verification Engineer at Groq

Toronto, Ontario, Canada

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Semiconductors, Artificial IntelligenceIndustries

Requirements

BS degree in electrical engineering, or related fields, or equivalent practical experience; advanced degrees (MS or PhD) is a plus
10+ years in ASIC verification with 5+ years focused on formal verification methods
Mastery of SystemVerilog Assertions (SVA) and formal property verification
Proficient on at least one popular formal verification tool in the industry (JasperGold, VC Formal, etc.)
Strong analytical skills and attention to detail when debugging complex issues
Good scripting skills for flow automation (tcl, python, etc.)
Good written and oral communication skills
Must be authorized to work in the United States or Canada
Preferred Qualifications
Proven success in full-cycle formal sign-off for complex compute blocks
Expertise in formal apps: sequential equivalence checks, datapath, connectivity, etc
Deep understanding of LPU or GPU architecture/design

Responsibilities

Verify hardware features of Language Process Unit (LPU)
Cross-functional collaboration: Partner with architecture/RTL teams to specify properties, resolve deep design issues, and influence micro-architecture decisions
Formal verification execution: Leverage formal verification to rigorously verify critical design properties, ensure compliance with specifications, minimize spec ambiguities; debug findings and collaborate with stakeholders efficiently
Support silicon bring-up and debug using formal methods where applicable
Methodology leadership: Develop and implement advanced formal verification environments and methodologies for complex ASIC designs, including automated flows for scalability and efficiency
Mentorship: Train and coach junior engineers on formal techniques and best practices; help on methodology/FAQ documentation
Innovate: Contribute to developing future verification strategies for validating future accelerator chips and hardware architectures for ML workloads

Skills

Formal Verification

ASIC Verification

RTL

Property Specification

Automated Flows

Formal Proofs

Silicon Bring-up

Debugging

Groq

AI inference technology for scalable solutions

About Groq

Groq specializes in AI inference technology, providing the Groq LPU™, which is known for its high compute speed, quality, and energy efficiency. The Groq LPU™ is designed to handle AI processing tasks quickly and effectively, making it suitable for both cloud and on-premises applications. Unlike many competitors, Groq's products are designed, fabricated, and assembled in North America, which helps maintain high standards of quality and performance. The company targets a variety of clients across different industries that require fast and efficient AI processing capabilities. Groq's goal is to deliver scalable AI inference solutions that meet the growing demands for rapid data processing in the AI and machine learning market.

Mountain View, CaliforniaHeadquarters

2016Year Founded

$1,266.5MTotal Funding

SERIES_DCompany Stage

AI & Machine LearningIndustries

201-500Employees

Benefits

Remote Work Options

Company Equity

Risks

Increased competition from SambaNova Systems and Gradio in high-speed AI inference.

Geopolitical risks in the MENA region may affect the Saudi Arabia data center project.

Rapid expansion could strain Groq's operational capabilities and supply chain.

Differentiation

Groq's LPU offers exceptional compute speed and energy efficiency for AI inference.

The company's products are designed and assembled in North America, ensuring high quality.

Groq emphasizes deterministic performance, providing predictable outcomes in AI computations.

Upsides

Groq secured $640M in Series D funding, boosting its expansion capabilities.

Partnership with Aramco Digital aims to build the world's largest inferencing data center.

Integration with Touchcast's Cognitive Caching enhances Groq's hardware for hyper-speed inference.

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