Machine Learning Engineer, LLM Fine-Tuning

San Jose, CA - USA

Monthly Salary: Not Disclosed

Posted on: 22-10-2025

Vacancies: 1 Vacancy

Job Summary

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 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.

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 dep...

We are actively hiring for Machine Learning Engineer

Location: San Jose CA (Onsite)

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 (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.