Senior MLOps & AI Infrastructure Engineer (AI)

Location: San Jose, California, United States. Applicants must be eligible for any required U.S. export authorizations.

Salary: $149,100–$215,925 USD per year for the Bay Area, California.

Company

hirify.global is a pure-play FPGA solutions provider developing programmable technologies for AI, cloud, networking, and edge markets.

What you will do

• Design, build, and maintain scalable ML pipelines for training, evaluation, deployment, and continuous training across cloud and on-prem HPC environments.
• Build MLOps infrastructure for experiment tracking, model registries, feature stores, retraining, data versioning, and lineage.
• Develop, fine-tune, and deploy LLMs, GNNs, and reinforcement learning agents for EDA and chip design applications.
• Containerize and orchestrate ML workloads with Docker, Kubernetes, and GPU node pools while optimizing cloud infrastructure costs and performance.
• Implement model monitoring, alerting, observability, A/B testing, shadow deployments, and drift detection.
• Partner with research, software, data, and infrastructure teams; mentor junior engineers and establish ML engineering practices.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field, plus 10+ years of industry experience.
• 10+ years across ML engineering, data science, and MLOps, including PyTorch, TensorFlow, JAX, Hugging Face, and production model deployment at scale.
• 8+ years of experience with parallelism strategies such as FSDP, DeepSpeed, and data/model parallelism.
• 10+ years of Python experience and 8+ years with cloud ML platforms, Docker, Kubernetes, and CI/CD pipelines.
• 5+ years of hands-on experience with MLflow, Weights & Biases, or Neptune.
• Eligibility for any required U.S. export authorizations is required.

Nice to have

• Experience applying AI/ML to semiconductor, EDA, or chip design workflows.
• Experience with HPC schedulers such as LSF or Slurm and GPU cluster management.
• Knowledge of LLM fine-tuning, RAG, AI agent frameworks, GNNs, geometric deep learning, or reinforcement learning.
• Experience with DevSecOps, zero-trust security, compliance automation, simulation pipelines, or synthetic data generation.
• Familiarity with Synopsys, Cadence, or Siemens EDA toolchains; published research or open-source contributions in ML, MLOps, or AI for EDA.

Culture & Benefits

• Regular full-time employment in a deep-tech and semiconductor environment.
• Incentive opportunities based on individual and company performance.
• Work spans cloud, on-prem HPC, EDA, simulation, and chip design environments.
• AI is used to screen, assess, or select applicants.