Senior ML Engineer: GPU AI for PCB Design (Equity)

7Seventy; United States; Hybrid; USD 180,000 - 200,000; Full time

Benefits offered by this job

Equity

Health insurance

Dental insurance

Vision insurance

Unlimited PTO

Parental leave

Team offsites

Job summary

7Seventy is seeking a Senior ML Engineer to develop AI systems for automated component placement on PCBs. You will own projects end-to-end, from exploratory research to production, spanning ML, optimization, and geometric deep learning.

You’ll work on GPU-accelerated systems with PyTorch and CUDA C++, exploring reinforcement learning, graph neural networks, and generative modeling with substantial autonomy and impact.

Qualifications

• 5+ years of industry experience in ML, optimization, or related field.
• Production experience developing ML systems with PyTorch.
• Experience across research and production codebases.

Responsibilities

• Own complex technical problems end-to-end from research to production systems.
• Develop and extend GPU-accelerated ML code using PyTorch and CUDA C++.
• Build and evaluate modeling approaches across RL, GNNs, geometric DL, and optimization.
• Formulate optimization objectives and model constraints; debug numerical behavior.
• Contribute to technical direction and ML research strategy with senior team.

Skills

Machine learning

Optimization

PyTorch

CUDA C++

Reinforcement learning

Graph neural networks

Geometric deep learning

Generative modeling

Combinatorial optimization

Job description

Experience: 5+ years of industry experience in machine learning, optimization, or a related field

Core Areas: Machine Learning, PyTorch, CUDA C++, Reinforcement Learning, Graph Neural Networks, Geometric Deep Learning, Generative Modeling, Combinatorial Optimization

Compensation: $180,000 – $200,000 per year, plus equity

About the Role

This opportunity is for a Senior ML Engineer focused on developing AI systems for automated component placement on printed circuit boards. The role owns complex technical problems across the full lifecycle, from exploratory research and prototyping through productionization and long-term maintenance, with work spanning machine learning, optimization, and geometric deep learning.

The position involves developing GPU-accelerated systems with PyTorch and CUDA C++, working with reinforcement learning, graph neural networks, generative modeling, and classical and black-box optimization. The role also contributes to model objectives and constraints, numerical debugging, technical direction, and research strategy while operating with substantial autonomy and ownership.

What You'll Do

• Own complex technical problems end-to-end, taking projects from exploratory research and development through prototyping, productionization, and maintainable production systems.
• Develop and extend GPU-accelerated machine learning code using PyTorch and CUDA C++.
• Build and evaluate modeling approaches across reinforcement learning, graph neural networks, geometric deep learning, black-box and classical optimization, and generative modeling.
• Formulate optimization objectives and model constraints while investigating and debugging numerical behavior throughout the technical stack.
• Contribute to technical direction and machine learning research strategy alongside senior technical team members.

Qualifications

Required Experience

• 5+ years of industry experience in machine learning, optimization, or a related field.
• Production experience developing machine learning systems with PyTorch.
• Demonstrated experience working effectively across both research and production codebases.

Required Skills

• Strong fundamentals in machine learning and optimization.
• Ability to work with substantial autonomy and ownership in ambiguous technical problem spaces.
• Strong communication and collaboration skills for working on complex research and engineering problems.

Preferred Qualifications

• 5–7 years of relevant industry experience; a Staff-level appointment may be considered based on experience and scope.
• Hands-on experience developing GPU-accelerated software with CUDA C++.
• Experience with one or more of the following areas: reinforcement learning, geometric deep learning, graph neural networks, multi-objective optimization, or combinatorial optimization.
• Health, dental, and vision insurance.
• Company equity.
• Unlimited paid time off.
• Paid parental leave.
• Regular team events and offsites, approximately four times per year.
• Opportunity to work on challenging technical problems across machine learning, optimization, and production AI systems.

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