Quinnel Soft LLC
Lead AI Engineer – LLM Systems for Hardware Design
Quinnel Soft LLC, Trenton, New Jersey, United States
Lead AI Engineer – LLM Systems for Hardware Design
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We’re building next-generation AI systems that help hardware engineers
write, verify, and optimize Verilog and RTL code
using large language models (LLMs).
As a
Lead AI Engineer , you’ll guide the design and deployment of specialized LLMs that make chip design smarter, faster, and more secure. You’ll lead a high-impact team that combines advanced AI modeling with enterprise‑grade privacy and compliance principles.
What You’ll Do
Architect and lead LLM development
for hardware and EDA workflows—from fine‑tuning and evaluation to production rollout.
Design secure ML infrastructure
on AWS using Bedrock, SageMaker, and EKS, ensuring data protection, encryption, and compliance.
Experiment with advanced fine‑tuning techniques
(LoRA/QLoRA, PEFT, RLAIF) to adapt foundation models for HDL code generation and analysis.
Establish performance benchmarks
using compile, simulate, and synthesis‑based metrics to measure functional accuracy and reliability.
Integrate AI tools
into developer environments (IDEs, CI pipelines, and internal retrieval systems).
Mentor engineers
in ML best practices, reproducibility, and privacy‑first development.
Collaborate across teams —working closely with hardware, EDA, and security experts to manage data, compliance, and delivery.
What You’ll Bring
10+ years in software or ML engineering, including 5+ in applied ML and 3+ with LLM or transformer‑based systems.
Expertise with
PyTorch, Hugging Face Transformers, PEFT, TRL, DeepSpeed, FSDP , and large‑scale training.
Proven experience building on
AWS ML infrastructure —Bedrock, SageMaker, EKS, S3, IAM, KMS, PrivateLink, CloudTrail.
Solid programming experience in
Python
with strong CI/CD and MLOps practices.
Strong leadership, problem‑solving, and communication skills to align multi‑disciplinary teams.
Nice to Have
Background in
Verilog, SystemVerilog, or RTL
design and EDA workflows (linting, synthesis, simulation).
Knowledge of
grammar‑constrained decoding ,
RAG , or
AST‑aware code modeling .
Familiarity with
vLLM, TensorRT‑LLM , or other inference optimization frameworks.
Experience with
AI governance, DLP, or data anonymization
for enterprise environments.
What Success Looks Like
90 Days:
Secure and compliant AWS LLM training environment established; baseline model deployed.
6 Months:
Multi‑model fine‑tuning and retrieval integration for spec‑to‑RTL translation.
12 Months:
Demonstrated productivity gains—faster code validation, improved lint outcomes, and measurable design acceleration.
Modeling:
PyTorch, Transformers, DeepSpeed, TensorRT‑LLM, vLLM
Security:
S3 + KMS, IAM, PrivateLink, CloudTrail, Step Functions
#J-18808-Ljbffr
We’re building next-generation AI systems that help hardware engineers
write, verify, and optimize Verilog and RTL code
using large language models (LLMs).
As a
Lead AI Engineer , you’ll guide the design and deployment of specialized LLMs that make chip design smarter, faster, and more secure. You’ll lead a high-impact team that combines advanced AI modeling with enterprise‑grade privacy and compliance principles.
What You’ll Do
Architect and lead LLM development
for hardware and EDA workflows—from fine‑tuning and evaluation to production rollout.
Design secure ML infrastructure
on AWS using Bedrock, SageMaker, and EKS, ensuring data protection, encryption, and compliance.
Experiment with advanced fine‑tuning techniques
(LoRA/QLoRA, PEFT, RLAIF) to adapt foundation models for HDL code generation and analysis.
Establish performance benchmarks
using compile, simulate, and synthesis‑based metrics to measure functional accuracy and reliability.
Integrate AI tools
into developer environments (IDEs, CI pipelines, and internal retrieval systems).
Mentor engineers
in ML best practices, reproducibility, and privacy‑first development.
Collaborate across teams —working closely with hardware, EDA, and security experts to manage data, compliance, and delivery.
What You’ll Bring
10+ years in software or ML engineering, including 5+ in applied ML and 3+ with LLM or transformer‑based systems.
Expertise with
PyTorch, Hugging Face Transformers, PEFT, TRL, DeepSpeed, FSDP , and large‑scale training.
Proven experience building on
AWS ML infrastructure —Bedrock, SageMaker, EKS, S3, IAM, KMS, PrivateLink, CloudTrail.
Solid programming experience in
Python
with strong CI/CD and MLOps practices.
Strong leadership, problem‑solving, and communication skills to align multi‑disciplinary teams.
Nice to Have
Background in
Verilog, SystemVerilog, or RTL
design and EDA workflows (linting, synthesis, simulation).
Knowledge of
grammar‑constrained decoding ,
RAG , or
AST‑aware code modeling .
Familiarity with
vLLM, TensorRT‑LLM , or other inference optimization frameworks.
Experience with
AI governance, DLP, or data anonymization
for enterprise environments.
What Success Looks Like
90 Days:
Secure and compliant AWS LLM training environment established; baseline model deployed.
6 Months:
Multi‑model fine‑tuning and retrieval integration for spec‑to‑RTL translation.
12 Months:
Demonstrated productivity gains—faster code validation, improved lint outcomes, and measurable design acceleration.
Modeling:
PyTorch, Transformers, DeepSpeed, TensorRT‑LLM, vLLM
Security:
S3 + KMS, IAM, PrivateLink, CloudTrail, Step Functions
#J-18808-Ljbffr