Machine Learning Engineer

Job Title: Staff Machine Learning Engineer, LLM Fine‑Tuning (Verilog/RTL Applications)

Level: Staff

Location: San Jose, CA (USA)

Cloud: AWS (primary; Bedrock SageMaker)

Why this role exists:

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.