Senior MLOps & AI Infrastructure Engineer
About The Position
About Altera At Altera™, our independence as the world’s largest pure‑play FPGA solutions provider gives us the focus, speed, and agility to innovate without compromise. With more than four decades of industry‑leading FPGA expertise, our singular mission is to deliver the programmable technologies that help customers differentiate, innovate, and scale across rapidly evolving markets like AI, cloud, networking, and edge. As an independent company, we move faster, invest deeper, and partner more closely—empowering our teams to drive breakthrough innovation and shape the future of the FPGA industry. About the Role We are looking for a Senior MLOps & AI Infrastructure Engineer to architect, build, and operationalize machine learning systems at scale. This role sits at the intersection of data science, software engineering, and infrastructure — combining deep ML expertise with the DevOps/MLOps discipline required to ship models reliably into production. You will partner closely with software, data, and infrastructure teams to design end-to-end ML pipelines, automate model lifecycle management, and deliver AI-powered capabilities across our EDA, HPC, and cloud environments.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or related field and 10+ years of industry experience
- 10+ years of experience across ML engineering, data science, and MLOps — including frameworks (PyTorch, TensorFlow, JAX, Hugging Face) and production model deployment at scale
- 8+ years of experience experience with parallelism strategies (FSDP, DeepSpeed, data/model parallelism)
- 10+ years of experience and proficiency in Python programming
- 8+ years of experience in cloud ML platforms (AWS, GCP, Azure), Docker/Kubernetes, and CI/CD pipelines
- 5+ years of hands-on experience with MLflow, W&B, or Neptune for tracking and reproducibility
Nice To Haves
- Phd in Computer Science, Machine Learning, Statistics, or related field
- Experience applying ML/AI to semiconductor, EDA, or chip design domains (e.g., timing prediction, place & route optimization, DRC closure)
- Familiarity with HPC schedulers such as LSF or Slurm and GPU cluster management for training workloads
- Knowledge of LLM fine-tuning, Retrieval-Augmented Generation (RAG) architectures, and AI agent frameworks such as LangChain or AutoGen
- Experience with graph neural networks (GNNs) or geometric deep learning for circuit and netlist analysis
- Background in reinforcement learning for optimization problems
- Exposure to zero-trust security, DevSecOps, and compliance automation for ML systems
- Experience working with large-scale simulation pipelines and synthetic data generation
- Experience at organizations such as NVIDIA, AMD, Intel, Google DeepMind, or similar AI/HPC-focused companies
- Published research or open-source contributions in ML, MLOps, or AI for EDA
- Experience building AI-powered developer tools or copilot-style products
- Familiarity with Synopsys, Cadence, or Siemens EDA toolchains and associated data formats
Responsibilities
- Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment across cloud and on-prem HPC environments
- Build MLOps infrastructure including experiment tracking, model registry, feature stores, and automated retraining workflows
- Implement CI/CD/CT (Continuous Training) pipelines for ML models using tools such as Kubeflow, MLflow, Airflow, or similar
- Containerize ML workloads with Docker and orchestrate at scale using Kubernetes and GPU node pools
- Develop, fine-tune, and deploy large-scale models including LLMs, GNNs, and reinforcement learning agents for EDA and chip design applications
- Apply advanced techniques: transfer learning, quantization, pruning, distillation, and RLHF for production-grade model efficiency
- Implement A/B testing frameworks and shadow deployments for safe model rollout
- Benchmark and optimize model inference performance on GPU/TPU clusters
- Build and maintain data pipelines for large-scale structured and unstructured datasets (terabyte-scale)
- Collaborate with data teams to design feature engineering systems and maintain data quality for ML training
- Implement data versioning and lineage tracking (DVC, Delta Lake, or similar)
- Manage cloud ML infrastructure on AWS (SageMaker), Azure (AML), or GCP (Vertex AI) with cost and performance optimization
- Automate infrastructure provisioning using Terraform or CloudFormation for GPU-backed ML environments
- Build monitoring, alerting, and observability systems for model performance drift, data quality, and system health
- Support HPC schedulers (LSF, Slurm) for large-scale distributed training jobs
- Partner with research scientists to productionize experimental models with engineering rigor
- Mentor junior engineers and define ML engineering best practices across the organization
- Drive adoption of AI/ML solutions within semiconductor, EDA, and simulation workflows
Benefits
- We also offer incentive opportunities that reward employees based on individual and company performance.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
