AI Algorithms/Software Engineer

Jobgether; Oregon; Hybrid; USD 130,000 - 210,000; Full time

Benefits offered by this job

Remote flexibility

Meaningful technical impact

Challenging engineering work

Collaborative culture

Professional growth

Engineering ownership

Job summary

Partner Company in the United States seeks an AI Algorithms/Software Engineer to design and optimize neural network algorithms for deployment on specialized analog hardware. You will collaborate across software, AI, and hardware teams to improve accuracy, latency, and efficiency.

You will work on quantization-aware training, model simulation of hardware effects, and robust software tooling in a remote-friendly environment. A strong background in PyTorch and ONNX is preferred.

Qualifications

• Bachelor's degree in Computer Science, Mathematics, or a related technical field.
• At least five years of professional software engineering experience in a production environment.
• Strong programming, analytical, debugging, and problem-solving skills.
• Experience with PyTorch and model interoperability with ONNX is an advantage.
• Experience with hardware-aware machine learning techniques (quantization, pruning, QAT) is a plus.
• Familiarity with ML operations (CI/CD, deployment, monitoring) is beneficial.
• A master's degree or PhD is desirable.
• Curiosity, adaptability, and collaborative mindset are important.

Responsibilities

• Design, develop, and optimize algorithms and software tools for neural network conversion, adaptation, and deployment on specialized analog computing hardware.
• Improve AI software pipelines to enhance model accuracy, latency, computational efficiency, and usability.
• Develop quantization-aware and analog-aware training techniques to improve neural network performance under hardware constraints.
• Build models that simulate analog hardware effects and evaluate their impact on neural network behavior.
• Implement network regularization and other optimization techniques to improve robustness.
• Collaborate with hardware and software engineers across teams.
• Contribute to hardware co-design by providing software insights and performance evaluations.
• Strengthen software quality through refactoring, automated testing, and documentation.
• Monitor advances in deep learning to identify opportunities for improvement.

Skills

5+ years exp

PyTorch familiarity

ONNX interoperability

Hardware-aware ML

Quantization-aware training

C++ / Python

MLOps basics

Open-source contributions

Education

Bachelor's degree in CS/Math

Master's or PhD desirable

Tools

PyTorch

ONNX

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Algorithms/Software Engineer based in the United States. Join an innovative engineering team developing next-generation AI computing solutions powered by advanced analog technology. In this role, you will design and optimize algorithms and software tools that enable neural networks to run efficiently on specialized hardware. You will work at the intersection of deep learning, software engineering, and hardware-aware AI optimization, helping improve model accuracy, latency, and computational efficiency. Your work will support applications ranging from large language models and computer vision to robotics, autonomous systems, and industrial technologies. Collaborating with hardware and software engineers, you will contribute to the development, validation, and deployment of advanced AI systems. This is an opportunity to solve challenging technical problems, influence emerging computing architectures, and contribute to impactful AI innovation in a collaborative, remote-friendly environment.

Accountabilities

• Design, develop, and optimize algorithms and software tools for neural network conversion, adaptation, and deployment on specialized analog computing hardware.
• Improve AI software pipelines to enhance model accuracy, inference latency, computational efficiency, and overall usability.
• Develop advanced quantization-aware and analog-aware training and retraining techniques to improve neural network performance under hardware constraints.
• Build accurate and computationally efficient models that simulate analog hardware effects and evaluate their impact on neural network behavior.
• Implement network regularization and other optimization techniques to improve model robustness and mitigate the effects of analog computing characteristics.
• Contribute to hardware bring-up, debugging, testing, and validation in close collaboration with cross-functional engineering teams.
• Support the co-design of future hardware architectures by providing software insights, algorithmic improvements, and performance evaluations.
• Strengthen software quality through code refactoring, automated testing, technical documentation, and established engineering best practices.
• Monitor advances in deep learning research, neural network architectures, and machine learning frameworks to identify opportunities for continuous improvement.

Requirements

• Bachelor's degree in Computer Science, Mathematics, or a related technical field.
• At least five years of professional software engineering experience in a production environment.
• Demonstrated ability to solve complex technical problems involving algorithm-intensive code and performance optimization.
• Strong programming, analytical, debugging, and problem-solving skills, with a commitment to producing reliable, maintainable, and high-quality software.
• Excellent communication and collaboration skills, with the ability to work effectively across software, AI, and hardware engineering teams.
• Familiarity with modern neural network frameworks, such as PyTorch, and model interoperability tools such as ONNX, is an advantage.
• Experience with contemporary neural network architectures, including convolutional neural networks and large language models, is desirable.
• Practical experience with hardware-aware machine learning techniques, including quantization, pruning, quantization-aware training, or optimization under model-size and hardware constraints, is a plus.
• Familiarity with machine learning operations (MLOps), including model versioning, ML-focused CI/CD pipelines, deployment, and monitoring, is beneficial.
• Experience maintaining critical APIs used by large user communities and contributing to open-source projects is an advantage.
• A master's degree or PhD in Computer Science, Mathematics, or a related field is desirable.
• Curiosity, adaptability, attention to detail, and a collaborative mindset are important for succeeding in a fast-evolving technical environment.

Benefits

• Remote flexibility: Full-time remote work, supporting flexibility in how and where you work within the role's applicable arrangements.
• Meaningful technical impact: The opportunity to contribute to advanced AI computing technologies with applications across robotics, computer vision, cloud computing, industrial systems, and other demanding environments.
• Challenging engineering work: Hands-on involvement in algorithm development, neural network optimization, hardware modeling, and next-generation computing solutions.
• Collaborative culture: Work alongside experienced professionals with diverse backgrounds, perspectives, and areas of expertise.
• Professional growth: Opportunities to deepen your knowledge of deep learning, hardware-aware algorithms, neural network frameworks, and AI software engineering.
• Engineering ownership: A work environment that values creativity, integrity, professionalism, high standards, and the autonomy to solve complex problems.
• Compensation and additional benefits: Salary range, healthcare coverage, paid time off, retirement benefits, and other perks were not specified in the source job description and should be confirmed during the recruitment process.