We are seeking a highly motivated AI Scientist specializing in Machine Learning to join our growing AI R&D team. In this role, you will be at the forefront of developing and deploying cutting-edge deep learning models to solve real-world temporal modeling challenges in manufacturing. We’re looking for a candidate with strong practical R&D experience, grounded in solid theoretical fundamentals, and deep expertise in AI disciplines. The ideal candidate will have a deep understanding of state-of-the-art machine learning algorithms and techniques, a track record of impactful publications in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, CVPR, or ICCV, and a solid background in computer science and engineering. Experience collaborating with software engineering teams to scale and productize ML solutions is a strong plus. This is a high-impact role that combines foundational research, system-level design, and hands-on implementation. You’ll work closely with cross-functional teams to develop innovative solutions that guide strategic decisions and deliver tangible business value.
Responsibilities
Design and implement Transformer-based architectures for time-series prediction and sequence modeling, across both univariate and multivariate data.
Drive the full machine learning lifecycle—from exploratory data analysis to model deployment, monitoring, and continuous improvement.
Conduct rigorous benchmarking, ablation studies, and performance optimization to ensure robustness and efficiency.
Collaborate closely with data scientists, engineers, and product managers to translate complex business requirements into scalable technical solutions.
Partner with software engineers to scale and productize ML algorithms within manufacturing AI software products.
Contribute to Gauss Labs’ intellectual property portfolio through patents and high-impact technical publications.
Mentor junior team members and play an active role in shaping the team’s AI roadmap and long-term strategy.
Key Qualifications
Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.
3+ years of hands-on experience in deep learning, with a strong focus on sequence modeling and time-series forecasting.
In-depth expertise in Transformer architectures and their applications beyond natural language processing.
Proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Solid mathematical foundation in statistics, optimization, and signal processing.
Familiarity with hybrid modeling approaches that combine deep learning and traditional statistical methods.
Experience working with noisy, sparse, or irregularly sampled time-series data.
Strong publication track record in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR).
Practical experience deploying ML models in production environments, with knowledge of MLOps best practices.
[Nice to have] Familiar with state-of-the-art neural networks architecture. Preferably had experience in innovation in new architecture such as transformer based models.
