Job Role – Staff Machine Learning Engineer, LLM Fine‑Tuning (Verilog/RTL Applications)
Location – San Jose, CA – Day 1 Onsite
Job Type – C2C Contract
We’re building privacy‑preserving LLM capabilities that help hardware design teams reason over Verilog/SystemVerilog and RTL artifacts—code generation, refactoring, lint explanation, constraint translation, and spec‑to‑RTL assistance. We’re looking for a Staff‑level engineer to technically lead a small, high‑leverage team that fine‑tunes and productizes LLMs for these workflows in a strict enterprise data‑privacy environment.
You don’t 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 you’ll do (Responsibilities)
Own the technical roadmap for Verilog/RTL‑focused LLM capabilities—from model selection and adaptation to evaluation, deployment, and continuous improvement.
Lead a hands‑on team of applied scientists/engineers: set direction, unblock technically, review designs/code, and raise the bar on experimentation velocity and reliability.
Fine‑tune and customize models using state‑of‑the‑art techniques (LoRA/QLoRA, PEFT, instruction tuning, preference optimization/RLAIF) with robust HDL‑specific evals:
Compile‑/lint‑/simulate‑based pass rates, pass@k for code generation, constrained decoding to enforce syntax, and “does‑it‑synthesize” checks.
Design privacy‑first 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 least‑privilege, 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 low‑latency self‑hosted inference (vLLM/TensorRT‑LLM), autoscaling, and canary/blue‑green 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/plug‑ins, code review bots, CI), retrieval (RAG) over internal HDL repos/specs, and safe tool‑use/function‑calling.
Mentor & uplevel: coach ICs on LLM best practices, reproducible training, critical paper reading, and building secure‑by‑default systems.
What you’ll bring (Minimum qualifications)
10+ years total engineering experience with 5+ years in ML/AI or large‑scale distributed systems; 3+ years working directly with transformers/LLMs.
Proven track record shipping LLM‑powered features in production and leading ambiguous, cross‑functional initiatives at Staff level.
Deep hands‑on skill with PyTorch, Hugging Face Transformers/PEFT/TRL, distributed training (DeepSpeed/FSDP), quantization‑aware fine‑tuning (LoRA/QLoRA), and constrained/grammar‑guided decoding.
AWS expertise to design and defend secure enterprise deployments, including:
Amazon Bedrock (model selection, Anthropic model usage, model customization, Guardrails, Knowledge Bases, Bedrock runtime APIs, VPC endpoints)
SageMaker (Training, Inference, Pipelines), S3, EC2/EKS/ECR, VPC/Subnets/Security Groups, IAM, KMS, PrivateLink, CloudWatch/CloudTrail, Step Functions, Batch, Secrets Manager.
Strong software engineering fundamentals: testing, CI/CD, observability, performance tuning; Python a must (bonus for Go/Java/C++).
Demonstrated ability to set technical vision and influence across teams; excellent written and verbal communication for execs and engineers.
Nice to have (Preferred qualifications)
Familiarity with Verilog/SystemVerilog/RTL workflows: lint, synthesis, timing closure, simulation, formal, test benches, and EDA tools (Synopsys/Cadence/Mentor).
Experience integrating static analysis/AST‑aware tokenization for code models or grammar‑constrained decoding.
RAG at scale over code/specs (vector stores, chunking strategies), tool‑use/function‑calling for code transformation.
Inference optimization: TensorRT‑LLM, KV‑cache optimization, speculative decoding; throughput/latency trade‑offs at batch and token levels.
Model governance/safety in the enterprise: model cards, red‑teaming, secure eval data handling; exposure to SOC2/ISO 27001/NIST frameworks.
Data anonymization, DLP scanning, and code de‑identification to protect IP.
