Senior Data Scientist

Work Model:
Hybrid (2 days onsite / 3 days remote)

Work Authorization:
Must be authorized to work in the U.S. (No sponsorship available)

We are working with a cutting-edge technology company building large-scale AI systems focused on time series and multimodal foundation models. This team is developing self-supervised models that learn from sequential sensor data, text, and vision inputs to power real-world applications such as anomaly detection, predictive maintenance, forecasting, classification, and multimodal sensor fusion across industrial and scientific domains.

If you enjoy solving complex modeling challenges, working with noisy real-world sensor data, and training large models at scale, this is a high-impact opportunity.

• Build, train, and deploy large-scale self-supervised foundation models for time series and multimodal data
• Develop deep learning architectures for sequential and sensor-based applications
• Design robust pipelines for ingesting, cleaning, aligning, and processing large-scale multi-sensor datasets
• Implement contrastive learning, masked modeling, temporal predictive coding, and multimodal fusion strategies
• Lead distributed model training across multi-GPU and multi-node environments
• Optimize performance using mixed precision and memory-efficient training techniques
• Collaborate cross-functionally with engineering, product, and domain experts
• Clearly communicate model performance, interpretability, and business impact

• Strong experience with time series and sequential data (univariate and multivariate)
• Experience working with real-world sensor data (accelerometer, vibration, temperature, audio, image, etc.)
• Proficiency in Python (Num Py, Sci Py, Pandas)
• Experience with PyTorch, Tensor Flow/Keras, or JAX
• Hands-on experience with Transformers, RNN/LSTM/GRU, TCNs, CNNs, or multimodal architectures
• Experience training models at scale (multi-GPU, distributed environments)

• Self-supervised or semi-supervised learning experience
• Signal processing background (Fourier, wavelets, filtering techniques)
• C++/CUDA experience for performance optimization
• Experience in industrial, IoT, medical, or scientific environments

• MS or Ph.D. in Computer Science, Data Science, AI, or related field
• 4+ years of relevant experience in machine learning, AI, or applied data science