About the role
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.