Compu-Vision Consulting, Inc.
Staff Machine Learning Engineer – SMLENGG 25-33679
Compu-Vision Consulting, Inc., San Jose, California, United States
Staff Machine Learning Engineer, LLM Fine-Tuning (Verilog/RTL Applications)
Level:
Staff
Location:
San Jose, CA
Cloud:
AWS (primary: Bedrock SageMaker)
Why this role exists You will architect and lead privacy-preserving LLM capabilities that support hardware design teams working with Verilog/SystemVerilog and RTL artifacts. This includes code generation, refactoring, lint explanation, constraint translation, and spec-to-RTL assistance. You ll lead a small, high-leverage team focused on fine-tuning and productizing LLMs in a strict enterprise data-privacy environment.
You
do not
need deep RTL expertise to start curiosity, LLM craftsmanship, and strong engineering rigor matter most. Exposure to HDL/EDA tooling is a plus.
Responsibilities Technical Leadership & Roadmap
Own the end-to-end roadmap for Verilog/RTL-focused LLM capabilities, covering model selection, fine-tuning, evals, deployment, and continuous improvement.
Lead a hands-on team of applied ML engineers/scientists, unblock technically, review designs and code, and drive experimentation velocity and reliability.
Model Training & Customization
Fine-tune and customize models using modern techniques (LoRA/QLoRA, PEFT, instruction tuning, RLAIF/preference optimization).
Build HDL-aware evaluation workflows:
Compile/lint/simulate-based pass rates
Pass@k for code generation
Constrained decoding enforcing HDL syntax
Does-it-synthesize? checks
Privacy-First AWS ML Pipelines
Design secure training & inference environments using AWS services such as:
Amazon Bedrock (incl. Anthropic models)
SageMaker or EKS KServe/Triton/DJL for bespoke training
Implement strict privacy controls:
Artifacts in S3 with KMS CMKs
VPC-only infrastructure with PrivateLink (incl. Bedrock endpoints)
IAM least-privilege, CloudTrail auditing
Secrets Manager for credential handling
Full encryption in transit/at rest
No public egress for customer/RTL corpora
Inference & Deployment
Stand up scalable, reliable LLM serving:
Bedrock model invocation where applicable
Low-latency self-hosted inference (vLLM/TensorRT-LLM)
Autoscaling and canary/blue-green rollouts
Evaluation Culture & Tooling
Build automated regression suites running HDL compilers/simulators to measure correctness and detect hallucinations.
Track experiments and produce model cards using MLflow/W&B.
Cross-Functional Collaboration
Work with hardware design teams, CAD/EDA, Security, and Legal to:
Prepare/anonymize datasets
Define acceptance gates
Meet licensing, compliance, and security requirements
Productization
Integrate models into engineering workflows: IDE plugins, CI bots, code review assistants, retrieval over internal HDL repos/specs, and safe function-calling.
Mentorship
Develop team capabilities in LLM training, reproducibility, secure pipelines, and research literacy.
Minimum Qualifications
10 years total engineering experience; 5 years in ML/AI or large-scale distributed systems; 3 years with transformers/LLMs.
Proven record shipping LLM-powered features and leading cross-functional technical initiatives at Staff level.
Deep, hands-on experience with:
PyTorch, Hugging Face Transformers/PEFT/TRL
Distributed training (DeepSpeed/FSDP)
LoRA/QLoRA, grammar-guided decoding
Strong AWS expertise:
Bedrock (model customization, Guardrails, Knowledge Bases, VPC endpoints)
SageMaker (Training/Inference/Pipelines)
S3, EC2/EKS/ECR, IAM, VPC, KMS, CloudWatch/CloudTrail, Step Functions, Secrets Manager
Strong Python engineering fundamentals (testing, CI/CD, observability, performance tuning).
Excellent technical communication and ability to set vision across teams.
Preferred Qualifications
Familiarity with Verilog/SystemVerilog/RTL workflows (lint, simulation, synthesis, timing closure, test benches).
Experience with static-analysis/AST-aware tokenization and grammar-constrained decoding.
RAG over code/spec repos; tool-use/function-calling for code transformation.
Inference optimization (TensorRT-LLM, KV-cache tuning, speculative decoding).
Experience with enterprise model governance and security frameworks (SOC2/ISO 27001/NIST).
Background in data anonymization, DLP scanning, and code de-identification.
What success looks like 90 Days
Stand up HDL-aware eval harness with compile/simulate checks.
Establish secure AWS training & inference environments (VPC-only, KMS encryption, no public egress).
Deliver initial fine-tuned model with measurable performance gains.
180 Days
Expand training coverage using Bedrock SageMaker/EKS.
Add constrained decoding and retrieval over design specs.
Productionize inference with SLOs and rollout to pilot teams.
12 Months
Reduce RTL review/iteration cycles using measurable metrics: lint-clean time, defect reductions, suggestion acceptance rates.
Establish a stable MLOps pathway for continuous improvements.
Security & Privacy by Design
All sensitive data remains within private AWS VPCs with IAM-controlled access and CloudTrail auditing.
Bedrock access via VPC PrivateLink endpoints only.
Strict data minimization, tagging, retention, reproducibility, and DLP scanning.
Model cards, lineage, and evaluation artifacts for each release.
Tech Stack Modeling:
PyTorch, HF Transformers/PEFT/TRL, DeepSpeed/FSDP, vLLM, TensorRT-LLM
AWS/MLOps:
Bedrock, SageMaker, ECR, EKS/KServe/Triton, MLflow/W&B, Step Functions
Platform/Security:
S3 KMS, IAM, VPC/PrivateLink, CloudWatch/CloudTrail, Secrets Manager
Bonus:
HDL toolchains, vector stores (pgvector/OpenSearch), GitHub/GitLab CI
#J-18808-Ljbffr
Staff
Location:
San Jose, CA
Cloud:
AWS (primary: Bedrock SageMaker)
Why this role exists You will architect and lead privacy-preserving LLM capabilities that support hardware design teams working with Verilog/SystemVerilog and RTL artifacts. This includes code generation, refactoring, lint explanation, constraint translation, and spec-to-RTL assistance. You ll lead a small, high-leverage team focused on fine-tuning and productizing LLMs in a strict enterprise data-privacy environment.
You
do not
need deep RTL expertise to start curiosity, LLM craftsmanship, and strong engineering rigor matter most. Exposure to HDL/EDA tooling is a plus.
Responsibilities Technical Leadership & Roadmap
Own the end-to-end roadmap for Verilog/RTL-focused LLM capabilities, covering model selection, fine-tuning, evals, deployment, and continuous improvement.
Lead a hands-on team of applied ML engineers/scientists, unblock technically, review designs and code, and drive experimentation velocity and reliability.
Model Training & Customization
Fine-tune and customize models using modern techniques (LoRA/QLoRA, PEFT, instruction tuning, RLAIF/preference optimization).
Build HDL-aware evaluation workflows:
Compile/lint/simulate-based pass rates
Pass@k for code generation
Constrained decoding enforcing HDL syntax
Does-it-synthesize? checks
Privacy-First AWS ML Pipelines
Design secure training & inference environments using AWS services such as:
Amazon Bedrock (incl. Anthropic models)
SageMaker or EKS KServe/Triton/DJL for bespoke training
Implement strict privacy controls:
Artifacts in S3 with KMS CMKs
VPC-only infrastructure with PrivateLink (incl. Bedrock endpoints)
IAM least-privilege, CloudTrail auditing
Secrets Manager for credential handling
Full encryption in transit/at rest
No public egress for customer/RTL corpora
Inference & Deployment
Stand up scalable, reliable LLM serving:
Bedrock model invocation where applicable
Low-latency self-hosted inference (vLLM/TensorRT-LLM)
Autoscaling and canary/blue-green rollouts
Evaluation Culture & Tooling
Build automated regression suites running HDL compilers/simulators to measure correctness and detect hallucinations.
Track experiments and produce model cards using MLflow/W&B.
Cross-Functional Collaboration
Work with hardware design teams, CAD/EDA, Security, and Legal to:
Prepare/anonymize datasets
Define acceptance gates
Meet licensing, compliance, and security requirements
Productization
Integrate models into engineering workflows: IDE plugins, CI bots, code review assistants, retrieval over internal HDL repos/specs, and safe function-calling.
Mentorship
Develop team capabilities in LLM training, reproducibility, secure pipelines, and research literacy.
Minimum Qualifications
10 years total engineering experience; 5 years in ML/AI or large-scale distributed systems; 3 years with transformers/LLMs.
Proven record shipping LLM-powered features and leading cross-functional technical initiatives at Staff level.
Deep, hands-on experience with:
PyTorch, Hugging Face Transformers/PEFT/TRL
Distributed training (DeepSpeed/FSDP)
LoRA/QLoRA, grammar-guided decoding
Strong AWS expertise:
Bedrock (model customization, Guardrails, Knowledge Bases, VPC endpoints)
SageMaker (Training/Inference/Pipelines)
S3, EC2/EKS/ECR, IAM, VPC, KMS, CloudWatch/CloudTrail, Step Functions, Secrets Manager
Strong Python engineering fundamentals (testing, CI/CD, observability, performance tuning).
Excellent technical communication and ability to set vision across teams.
Preferred Qualifications
Familiarity with Verilog/SystemVerilog/RTL workflows (lint, simulation, synthesis, timing closure, test benches).
Experience with static-analysis/AST-aware tokenization and grammar-constrained decoding.
RAG over code/spec repos; tool-use/function-calling for code transformation.
Inference optimization (TensorRT-LLM, KV-cache tuning, speculative decoding).
Experience with enterprise model governance and security frameworks (SOC2/ISO 27001/NIST).
Background in data anonymization, DLP scanning, and code de-identification.
What success looks like 90 Days
Stand up HDL-aware eval harness with compile/simulate checks.
Establish secure AWS training & inference environments (VPC-only, KMS encryption, no public egress).
Deliver initial fine-tuned model with measurable performance gains.
180 Days
Expand training coverage using Bedrock SageMaker/EKS.
Add constrained decoding and retrieval over design specs.
Productionize inference with SLOs and rollout to pilot teams.
12 Months
Reduce RTL review/iteration cycles using measurable metrics: lint-clean time, defect reductions, suggestion acceptance rates.
Establish a stable MLOps pathway for continuous improvements.
Security & Privacy by Design
All sensitive data remains within private AWS VPCs with IAM-controlled access and CloudTrail auditing.
Bedrock access via VPC PrivateLink endpoints only.
Strict data minimization, tagging, retention, reproducibility, and DLP scanning.
Model cards, lineage, and evaluation artifacts for each release.
Tech Stack Modeling:
PyTorch, HF Transformers/PEFT/TRL, DeepSpeed/FSDP, vLLM, TensorRT-LLM
AWS/MLOps:
Bedrock, SageMaker, ECR, EKS/KServe/Triton, MLflow/W&B, Step Functions
Platform/Security:
S3 KMS, IAM, VPC/PrivateLink, CloudWatch/CloudTrail, Secrets Manager
Bonus:
HDL toolchains, vector stores (pgvector/OpenSearch), GitHub/GitLab CI
#J-18808-Ljbffr