Machine Learning Engineer, LLM Fine-Tuning (San Jose)

Machine Learning Engineer, LLM Fine‑Tuning

We are actively hiring for a Machine Learning Engineer focused on LLM fine‑tuning for Verilog/RTL applications.

Location: San Jose, CA (Onsite)

Skills: LLM fine‑tuning, Verilog/RTL, AWS, Bedrock, SageMaker

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, View email address on apply.placed-app.com 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 and 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.

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: Bedrock, SageMaker, 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.

Seniority Level

Mid‑Senior level

Employment Type

Full‑time

Job Function

Engineering and Information Technology

Industries

IT Services and IT Consulting