Machine Learning Engineer, LLM Fine-Tuning

We are actively hiring for Machine Learning Engineer

Location: San Jose, CA (Onsite)

Skills: LLM Fine‑Tuning (Verilog/RTL Applications) AWS (primary; 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, 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.
  
   

• 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.