Staff Machine Learning Engineer – SMLENGG 25-33679

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

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