Solve complex and novel ML challenges, introduce transformative approaches, and ensure that innovation is applied at scale across projects, developing and deploying cutting-edge deep learning models to solve real-world temporal modeling challenges in manufacturing.
Requirements
- 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).
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.
Other
- Serve as technical leaders who shape the long-term direction of multiple initiatives.
- Mentor junior/senior peers, influencing both engineering and product strategy.
- Their scope extends from direct execution to setting vision and ensuring sustainable growth in technical capability.
- We’re looking for a candidate with strong practical R&D experience, grounded in solid theoretical fundamentals, and deep expertise in AI disciplines.
- Experience collaborating with software engineering teams to scale and productize ML solutions is a strong plus.
