Machine Learning Engineer (San Jose)

Job Title : Staff Machine Learning Engineer, LLM FineTuning (Verilog / RTL Applications)

Level : Staff

Location : San Jose, CA (USA)

Cloud : AWS (primary; Bedrock + SageMaker)

Why this role exists :

Were building privacypreserving LLM capabilities that help hardware design teams reason over Verilog / SystemVerilog and RTL artifactscode generation, refactoring, lint explanation, constraint translation, and spectoRTL assistance. Were looking for a Stafflevel engineer to technically lead a small, highleverage team that finetunes and productizes LLMs for these workflows in a strict enterprise dataprivacy environment.

You dont need to be a Verilog / RTL expert to start; curiosity, drive, and deep LLM craftsmanship matter most. Any HDL / EDA fluency is a strong plus.

What youll do (Responsibilities) :

• Own the technical roadmap for Verilog / RTLfocused LLM capabilitiesfrom model selection and adaptation to evaluation, deployment, and continuous improvement.
• Lead a handson team of applied scientists / engineers : set direction, unblock technically, review designs / code, and raise the bar on experimentation velocity and reliability.
• Finetune and customize models using stateoftheart techniques (LoRA / QLoRA, PEFT, instruction tuning, preference optimization / RLAIF) with robust HDLspecific evals :
• Compile / lint / simulatebased pass rates, pass@k for code generation, constrained decoding to enforce syntax, and doesitsynthesize checks.
• Design privacyfirst ML pipelines on AWS :
• Training / customization and hosting using Amazon Bedrock (including Anthropic models) where appropriate; SageMaker (or EKS + KServe / Triton / DJL) for bespoke training needs.
• Artifacts in S3 with KMS CMKs; isolated VPC subnets & PrivateLink (including Bedrock VPC endpoints), IAM leastprivilege, CloudTrail auditing, and Secrets Manager for credentials.
• Enforce encryption in transit / at rest, data minimization, no public egress for customer / RTL corpora.
• Stand up dependable model serving : Bedrock model invocation where it fits, and / or lowlatency selfhosted inference (vLLM / TensorRTLLM), autoscaling, and canary / bluegreen rollouts.
• Build an evaluation culture : automatic regression suites that run HDL compilers / simulators, measure behavioral fidelity, and detect hallucinations / constraint violations; model cards and experiment tracking (MLflow / Weights & Biases).
• Partner deeply with hardware design, CAD / EDA, Security, and Legal to source / prepare datasets (anonymization, redaction, licensing), define acceptance gates, and meet compliance requirements.
• Drive productization : integrate LLMs with internal developer tools (IDEs / plugins, code review bots, CI), retrieval (RAG) over internal HDL repos / specs, and safe tooluse / functioncalling.
• Mentor & uplevel : coach ICs on LLM best practices, reproducible training, critical paper reading, and building securebydefault systems.

What youll bring (Minimum qualifications) :

Nice to have (Preferred qualifications)