About the role

The role involves developing and optimizing machine learning technologies to enhance FPGA compiler performance, including timing closure and power efficiency. You will collaborate with architects and engineers to integrate ML models into the FPGA design flow and execute benchmarks across various FPGA families.

What they look for

Machine Learning FPGA Compiler Development C++ Python PyTorch TensorFlow Inference Optimization Graph Neural Networks Reinforcement Learning Quantization Pruning CI/CD EDA Tools Quartus Hardware Architecture

Requirements

Candidates must hold at least a Bachelor's degree in Computer Science, Electrical Engineering, or a related field. A minimum of 10 years of experience in machine learning development, model optimization, or ML systems engineering is required.

Full description

Job Details:

Job Description:

Altera is a global leader in programmable logic solutions, delivering high performance FPGA technology that powers next generation cloud, networking, and edge applications. With a renewed focus on agility, software first usability, and hardware accelerated innovation, Altera is shaping the future of computing. Join us as we build the next wave of AI optimized FPGA platforms and tools!

About the Role

We are seeking a Machine Learning Engineer to help drive the development, optimization and deployment of Altera FPGA Compiler. In this role, you will work at the intersection of machine learning and compiler/toolchain development, enabling customers to achieve breakthrough performance and efficiency on programmable logic.

You will collaborate closely with hardware architects, software engineers, and IP developers to design ML models, optimize inference pipelines, and contribute to the evolution of Altera’s FPGA Compiler.

Key Responsibilities

Develop, optimize, and deploy advanced machine learning technologies to enhance FPGA compiler performance, focusing on timing closure, resource utilization, and power efficiency.

Evaluate and integrate emerging ML models (e.g., graph neural networks, reinforcement learning) and frameworks (e.g., PyTorch, TensorFlow) for compiler optimization tasks like placement, routing, and logic synthesis.

Build robust tools, scripts, and CI/CD workflows to automate model conversion, quantization, pruning, and deployment within the FPGA design flow, ensuring compatibility with EDA tools like Quartus.

Collaborate with customers, FPGA architects, and internal engineering teams to gather ML requirements, define success metrics, and deliver tailored, production-ready solutions.

Design and execute comprehensive benchmarks to measure ML-optimized compiler performance across diverse FPGA families (e.g., Agilex, Stratix), analyzing metrics such as compile time, QoR, and scalability.

Salary Range

The pay range below is for Bay Area California only. Actual salary may vary based on a number of factors including job location, job-related knowledge, skills, experiences, trainings, etc. We also offer incentive opportunities that reward employees based on individual and company performance.

$200.4K - $290.1K USD

We use artificial intelligence to screen, assess, or select applicants for the position. Applicants must be eligible for any required U.S. export authorizations.

Qualifications:

Minimum Qualifications

Bachelor’s Degree or higher in Computer Science, Electrical Engineering, or a related field.

10+ years of experience in machine learning development, model optimization or ML systems engineering.

Experience with C++ and Python in production or research environments.

Preferred Qualifications

Experience with Agile methodologies, GitHub Copilot or similar AI coding assistants, and high-performance computing environments.

Familiarity with edge AI inference on FPGAs and neuro-symbolic AI techniques.

Strong communication skills for cross-functional collaboration and presenting results at conferences like DAC or FPGA World.

Job Type:

Regular

Shift:

Shift 1 (United States of America)

Primary Location:

San Jose, California, United States

Additional Locations:

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.