GTN Technical Staffing
Staff Machine Learning Engineer, LLM Fine Tuning (Verilog/RTL Applications)
GTN Technical Staffing, Topeka, Kansas, United States
Staff Machine Learning Engineer, LLM Fine‑Tuning (Verilog/RTL Applications)
Highlights
Location: San Jose, CA (Onsite/Hybrid)
Schedule: Full Time
Position Type: Contract
Hourly: BOE
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. The role is a Staff‑level engineer who
technically leads
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.
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),
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.
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.
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/Weights & Biases, 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"
#J-18808-Ljbffr
Schedule: Full Time
Position Type: Contract
Hourly: BOE
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. The role is a Staff‑level engineer who
technically leads
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.
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),
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.
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.
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/Weights & Biases, 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"
#J-18808-Ljbffr