Job Description
Staff Machine Learning Engineer
Level: Staff
Location: San Jose, CA (Onsite)
Cloud Platform: AWS (Bedrock & SageMaker)
Role Overview
We are building privacy-preserving large language model (LLM) capabilities to support hardware design workflows involving Verilog/SystemVerilog and RTL artifacts. These models enable advanced use cases such as code generation and refactoring, lint explanation, constraint translation, and spec-to-RTL assistance—while operating within strict enterprise security and data-privacy boundaries.
This Staff-level role will provide technical leadership for fine-tuning, evaluating, and deploying LLMs in production environments. While experience with Verilog/RTL is a strong plus, success in this role is driven primarily by deep LLM expertise, strong engineering fundamentals, and the ability to lead high-impact initiatives.
Responsibilities
Own the technical roadmap for RTL-focused LLM capabilities, from model selection and fine-tuning through deployment and continuous improvement.
Lead and mentor a small team of applied ML engineers and scientists; review designs and code, remove technical blockers, and drive execution.
Fine-tune and customize transformer models using modern techniques such as LoRA/QLoRA, PEFT, instruction tuning, and preference optimization (RLAIF).
Design and operate HDL-aware evaluation frameworks, including:
Compile, lint, and simulation pass rates
Pass@k metrics for code generation
Constrained/grammar-guided decoding
Synthesis-readiness checks
Build and maintain secure, privacy-first ML pipelines on AWS, including:
Amazon Bedrock for managed foundation models
SageMaker and/or EKS for bespoke training and inference
Encrypted storage (S3 + KMS), private VPCs, IAM least privilege, CloudTrail auditing
Deploy and operate low-latency, production inference using Bedrock and/or self-hosted stacks (vLLM, TensorRT-LLM), with autoscaling and safe rollout strategies.
Establish a strong evaluation and MLOps culture with automated regression testing, experiment tracking, and model documentation.
Partner with hardware engineering, EDA, security, and legal stakeholders to ensure compliant data sourcing, anonymization, and governance.
Drive product integration with internal developer tools, CI workflows, IDE plug-ins, retrieval-augmented generation (RAG), and safe tool-use.
Mentor engineers on LLM best practices, reproducible experimentation, and secure system design.
Minimum Qualifications
10+ years of overall engineering experience, including:
5+ years in ML/AI or large-scale distributed systems
3+ years working hands-on with transformers or LLMs
Proven experience shipping LLM-powered features to production and leading cross-functional technical initiatives.
Deep expertise with PyTorch, Hugging Face (Transformers, PEFT, TRL), and distributed training frameworks (DeepSpeed, FSDP).
Experience with quantization-aware fine-tuning, constrained decoding, and evaluation of code-generation models.
Strong AWS background, including:
Amazon Bedrock (model usage, customization, Guardrails, runtime APIs, VPC endpoints)
SageMaker (Training, Inference, Pipelines)
Core services: S3, EC2/EKS, IAM, KMS, VPC, CloudWatch, CloudTrail, Secrets Manager
Solid software engineering fundamentals: testing, CI/CD, observability, and performance optimization.
Excellent communication skills and the ability to influence both technical and executive stakeholders.
Preferred Qualifications
Familiarity with Verilog/SystemVerilog or RTL workflows, including linting, synthesis, simulation, and EDA tools.
Experience with AST-aware tokenization or grammar-constrained decoding for code models.
Retrieval-augmented generation (RAG) over code and technical specifications.
Inference optimization techniques (TensorRT-LLM, KV-cache optimization, speculative decoding).
Enterprise model governance, security reviews, and compliance frameworks (e.g., SOC 2, ISO 27001).
Experience with data anonymization, DLP scanning, and IP-safe ML pipelines.
For more details reach at [email protected]
About Navitas Partners, LLC: It is a certified WBENC and one of the fastest-growing Technical / IT staffing firms in the US providing services to numerous clients. We offer the most competitive pay for every position. We understand this is a partnership. You will not be blindsided and your salary will be discussed upfront.
Level: Staff
Location: San Jose, CA (Onsite)
Cloud Platform: AWS (Bedrock & SageMaker)
Role Overview
We are building privacy-preserving large language model (LLM) capabilities to support hardware design workflows involving Verilog/SystemVerilog and RTL artifacts. These models enable advanced use cases such as code generation and refactoring, lint explanation, constraint translation, and spec-to-RTL assistance—while operating within strict enterprise security and data-privacy boundaries.
This Staff-level role will provide technical leadership for fine-tuning, evaluating, and deploying LLMs in production environments. While experience with Verilog/RTL is a strong plus, success in this role is driven primarily by deep LLM expertise, strong engineering fundamentals, and the ability to lead high-impact initiatives.
Responsibilities
Own the technical roadmap for RTL-focused LLM capabilities, from model selection and fine-tuning through deployment and continuous improvement.
Lead and mentor a small team of applied ML engineers and scientists; review designs and code, remove technical blockers, and drive execution.
Fine-tune and customize transformer models using modern techniques such as LoRA/QLoRA, PEFT, instruction tuning, and preference optimization (RLAIF).
Design and operate HDL-aware evaluation frameworks, including:
Compile, lint, and simulation pass rates
Pass@k metrics for code generation
Constrained/grammar-guided decoding
Synthesis-readiness checks
Build and maintain secure, privacy-first ML pipelines on AWS, including:
Amazon Bedrock for managed foundation models
SageMaker and/or EKS for bespoke training and inference
Encrypted storage (S3 + KMS), private VPCs, IAM least privilege, CloudTrail auditing
Deploy and operate low-latency, production inference using Bedrock and/or self-hosted stacks (vLLM, TensorRT-LLM), with autoscaling and safe rollout strategies.
Establish a strong evaluation and MLOps culture with automated regression testing, experiment tracking, and model documentation.
Partner with hardware engineering, EDA, security, and legal stakeholders to ensure compliant data sourcing, anonymization, and governance.
Drive product integration with internal developer tools, CI workflows, IDE plug-ins, retrieval-augmented generation (RAG), and safe tool-use.
Mentor engineers on LLM best practices, reproducible experimentation, and secure system design.
Minimum Qualifications
10+ years of overall engineering experience, including:
5+ years in ML/AI or large-scale distributed systems
3+ years working hands-on with transformers or LLMs
Proven experience shipping LLM-powered features to production and leading cross-functional technical initiatives.
Deep expertise with PyTorch, Hugging Face (Transformers, PEFT, TRL), and distributed training frameworks (DeepSpeed, FSDP).
Experience with quantization-aware fine-tuning, constrained decoding, and evaluation of code-generation models.
Strong AWS background, including:
Amazon Bedrock (model usage, customization, Guardrails, runtime APIs, VPC endpoints)
SageMaker (Training, Inference, Pipelines)
Core services: S3, EC2/EKS, IAM, KMS, VPC, CloudWatch, CloudTrail, Secrets Manager
Solid software engineering fundamentals: testing, CI/CD, observability, and performance optimization.
Excellent communication skills and the ability to influence both technical and executive stakeholders.
Preferred Qualifications
Familiarity with Verilog/SystemVerilog or RTL workflows, including linting, synthesis, simulation, and EDA tools.
Experience with AST-aware tokenization or grammar-constrained decoding for code models.
Retrieval-augmented generation (RAG) over code and technical specifications.
Inference optimization techniques (TensorRT-LLM, KV-cache optimization, speculative decoding).
Enterprise model governance, security reviews, and compliance frameworks (e.g., SOC 2, ISO 27001).
Experience with data anonymization, DLP scanning, and IP-safe ML pipelines.
For more details reach at [email protected]
About Navitas Partners, LLC: It is a certified WBENC and one of the fastest-growing Technical / IT staffing firms in the US providing services to numerous clients. We offer the most competitive pay for every position. We understand this is a partnership. You will not be blindsided and your salary will be discussed upfront.
💡 Quick Summary
Seeking a career-building opportunity? The Staff Machine Learning Engineer - MLAV#01- post position is now open for candidates interested in the IT Engineer & Developer Jobs sector. This role in San Francisco offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in IT Engineer & Developer Jobs is a plus.
