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

• 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, 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.

• 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.

Mid Senior level

Full time

Engineering and Information Technology

IT Services and IT Consulting