First Soft Solutions LLC
Machine Learning Engineer, LLM Fine-Tuning
First Soft Solutions LLC, San Jose, California, United States, 95123
Machine Learning Engineer, LLM Fine‑Tuning
Check you match the skill requirements for this role, as well as associated experience, then apply with your CV below. We are actively hiring for a
Machine Learning Engineer
focused on LLM fine‑tuning for Verilog/RTL applications.
Location:
San Jose, CA (Onsite)
Skills:
LLM fine‑tuning, Verilog/RTL, AWS, Bedrock, SageMaker
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 and 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.
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: Bedrock, SageMaker, 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.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industries IT Services and IT Consulting
#J-18808-Ljbffr
Check you match the skill requirements for this role, as well as associated experience, then apply with your CV below. We are actively hiring for a
Machine Learning Engineer
focused on LLM fine‑tuning for Verilog/RTL applications.
Location:
San Jose, CA (Onsite)
Skills:
LLM fine‑tuning, Verilog/RTL, AWS, Bedrock, SageMaker
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 and 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.
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: Bedrock, SageMaker, 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.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industries IT Services and IT Consulting
#J-18808-Ljbffr