First Soft Solutions LLC

Machine Learning Engineer, LLM Fine Tuning 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, 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