AI Algorithms/Software Engineer

Accountabilities •

Design, develop, and optimize algorithms and software tools for neural network conversion, adaptation, and deployment on specialized analog computing hardware.

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Improve AI software pipelines to enhance model accuracy, inference latency, computational efficiency, and overall usability.

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Develop advanced quantization-aware and analog-aware training and retraining techniques to improve neural network performance under hardware constraints.

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Build accurate and computationally efficient models that simulate analog hardware effects and evaluate their impact on neural network behavior.

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Implement network regularization and other optimization techniques to improve model robustness and mitigate the effects of analog computing characteristics.

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Contribute to hardware bring-up, debugging, testing, and validation in close collaboration with cross-functional engineering teams.

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Support the co-design of future hardware architectures by providing software insights, algorithmic improvements, and performance evaluations.

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Strengthen software quality through code refactoring, automated testing, technical documentation, and established engineering best practices.

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Monitor advances in deep learning research, neural network architectures, and machine learning frameworks to identify opportunities for continuous improvement.

Requirements:

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Bachelor's degree in Computer Science, Mathematics, or a related technical field.

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At least five years of professional software engineering experience in a production environment.

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Demonstrated ability to solve complex technical problems involving algorithm-intensive code and performance optimization.

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Strong programming, analytical, debugging, and problem-solving skills, with a commitment to producing reliable, maintainable, and high-quality software.

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Excellent communication and collaboration skills, with the ability to work effectively across software, AI, and hardware engineering teams.

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Familiarity with modern neural network frameworks, such as PyTorch, and model interoperability tools such as ONNX, is an advantage.

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Experience with contemporary neural network architectures, including convolutional neural networks and large language models, is desirable.

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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.

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Familiarity with machine learning operations (MLOps), including model versioning, ML-focused CI/CD pipelines, deployment, and monitoring, is beneficial.

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Experience maintaining critical APIs used by large user communities and contributing to open-source projects is an advantage.

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A master's degree or PhD in Computer Science, Mathematics, or a related field is desirable.

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Curiosity, adaptability, attention to detail, and a collaborative mindset are important for succeeding in a fast-evolving technical environment.

Benefits:

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Remote flexibility: Full-time remote work, supporting flexibility in how and where you work within the role's applicable arrangements.

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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.

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Challenging engineering work: Hands-on involvement in algorithm development, neural network optimization, hardware modeling, and next-generation computing solutions.

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Collaborative culture: Work alongside experienced professionals with diverse backgrounds, perspectives, and areas of expertise.

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Professional growth: Opportunities to deepen your knowledge of deep learning, hardware-aware algorithms, neural network frameworks, and AI software engineering.

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Engineering ownership: A work environment that values creativity, integrity, professionalism, high standards, and the autonomy to solve complex problems.

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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. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1