Machine Learning Engineer (ML)
1 day ago
San Jose
Job Description Staff Machine Learning Engineer, LLM Fine‐Tuning (Verilog/RTL Applications) Location: San Jose, CA (Onsite/Hybrid) Schedule: Full Time Position Type: Contract Hourly: $70/hr Overview: Our client is building privacy‐preserving LLM capabilities that help hardware design teams reason over Verilog/SystemVerilog and RTL artifacts—code generation, refactoring, lint explanation, constraint translation, and spec‐to‐RTL assistance. Our client is looking for a Staff‐level engineer to technically lead a small, high‐leverage team that fine‐tunes and productizes LLMs for these workflows in a strict enterprise data‐privacy environment. You don’t need to be a Verilog/RTL expert to start; curiosity, drive, and deep LLM craftsmanship matter most. Any HDL/EDA fluency is a strong plus. What you’ll do (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, 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. What you’ll bring (Minimum 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, 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. Nice to have (Preferred qualifications) • Familiarity with Verilog/SystemVerilog/RTL workflows: lint, synthesis, timing closure, simulation, formal, test benches, and EDA tools (Synopsys/Cadence/Mentor)., • Experience integrating static analysis/AST‐aware tokenization for code models or grammar‐constrained decoding., • RAG at scale over code/specs (vector stores, chunking strategies), tool‐use/function‐calling for code transformation., • Inference optimization: TensorRT‐LLM, KV‐cache optimization, speculative decoding; throughput/latency trade‐offs at batch and token levels., • Model governance/safety in the enterprise: model cards, red‐teaming, secure eval data handling; exposure to SOC2/ISO 27001/NIST frameworks., • Data anonymization, DLP scanning, and code de‐identification to protect IP. What success looks like 90 days • Baseline an HDL‐aware eval harness that compiles/simulates; establish secure AWS training & serving environments (VPC‐only, KMS‐backed, no public egress)., • Ship an initial fine‐tuned/customized model with measurable gains vs. base (e.g., +X% compile‐pass rate, −Y% lint findings per K LOC generated). 180 days • Expand customization/training coverage (Bedrock for managed FMs including Anthropic; SageMaker/EKS for bespoke/open models)., • Add constrained decoding + retrieval over internal design specs; productionize inference with SLOs (p95 latency, availability) and audited rollout to pilot hardware teams. 12 months • Demonstrably reduce review/iteration cycles for RTL tasks with clear metrics (defect reduction, time‐to‐lint‐clean, % auto‐fix suggestions accepted), and a stable MLOps path for continuous improvement. (Security & privacy by design) • Customer and internal design data remain within private AWS VPCs; access via IAM roles and audited by CloudTrail; all artifacts encrypted with KMS., • No public internet calls for sensitive workloads; Bedrock access via VPC interface endpoints/PrivateLink with endpoint policies; SageMaker and/or EKS run in private subnets., • Data pipelines enforce minimization, tagging, retention windows, and reproducibility; DLP scanning and redaction are first‐class steps., • We produce model cards, data lineage, and evaluation artifacts for every release. Tech you’ll touch • Modeling: PyTorch, HF Transformers/PEFT/TRL, DeepSpeed/FSDP, vLLM, TensorRT‐LLM, • AWS & MLOps: Amazon Bedrock (Anthropic and other FMs, Guardrails, Knowledge Bases, Runtime APIs), SageMaker (Training/Inference/Pipelines), MLflow/W&B, ECR, EKS/KServe/Triton, Step Functions, • Platform/Security: S3 + KMS, IAM, VPC/PrivateLink (incl. Bedrock), CloudWatch/CloudTrail, Secrets Manager Tooling (nice to have): • HDL toolchains for compile/simulate/lint, vector stores (pgvector/OpenSearch), GitHub/GitLab CI "We are GTN – The Go To Network" Company DescriptionWho We Are GTN is the leader in technical staffing, leveraging innovation to drive next-generation recruiting to Fortune 2000 companies. What We Do We provide Scalable Technical Staffing solutions encompassing SOW, staff augmentation, and direct hire placement for Fortune 2000 companies, with niche service offerings in Cyber Security, Digital, Field Services, and Professional Services. Our recruiting teams are specialized, certified, and have endured rigorous technical boot camps and ongoing required educational courses and meetups. What Proof We Have We have experience in scalable IT Staffing since the year 2000. Our IT experts have helped Fortune 2000 corporations embrace the cybersecurity and digital space, with many of our experts remaining on contract with clients for 8+ years. Our staffing experts have been engaged to speak at cybersecurity conferences, and our teams are certified and specialize in divisions focused around cyber, digital, field, and professional services. Why People Want It Our valueWho We Are\r\nGTN is the leader in technical staffing, leveraging innovation to drive next-generation recruiting to Fortune 2000 companies.\r\n\r\nWhat We Do\r\nWe provide Scalable Technical Staffing solutions encompassing SOW, staff augmentation, and direct hire placement for Fortune 2000 companies, with niche service offerings in Cyber Security, Digital, Field Services, and Professional Services. Our recruiting teams are specialized, certified, and have endured rigorous technical boot camps and ongoing required educational courses and meetups.\r\n\r\nWhat Proof We Have\r\nWe have experience in scalable IT Staffing since the year 2000. Our IT experts have helped Fortune 2000 corporations embrace the cybersecurity and digital space, with many of our experts remaining on contract with clients for 8+ years. Our staffing experts have been engaged to speak at cybersecurity conferences, and our teams are certified and specialize in divisions focused around cyber, digital, field, and professional services.\r\n\r\nWhy People Want It\r\nOur value