Data Scientist - Drilling - Global Energy Tech

Senior Machine Learning Scientist, Foundation Models for Time Series and Sensors

We are looking for a Machine Learning Scientist who is excited by the challenge of teaching machines to understand the physical world through data. This role focuses on building large scale foundation models that learn deep representations from time series, sequential sensor streams, and other complex signals, with direct application to industrial, scientific, and real world systems.

In this position, you will design, train, and deploy self supervised and semi supervised learning systems that operate across time series, sensor data, vision, and text. Your work will enable advanced capabilities such as anomaly and event detection, predictive maintenance, forecasting, classification, and sensor fusion. You will help define the next generation of intelligent systems that operate reliably in noisy, high consequence environments.

• Develop large scale foundation models that learn transferable representations from sequential and sensor based data
• Apply self supervised learning techniques to unlock value from unlabeled and weakly labeled datasets
• Fine tune and adapt pretrained models for specialized downstream tasks across industrial and scientific domains
• Design systems that combine time series, images, audio, text, and structured data into unified learning architectures
• Collaborate with domain experts to ensure models align with real world constraints and objectives

• Process and analyze univariate and multivariate time series from domains such as industrial systems, IoT, finance, healthcare, or scientific instrumentation
• Perform data augmentation, feature extraction, resampling, and noise handling for real world sensor streams
• Work with diverse sensor modalities including vibration, temperature, accelerometers, audio, imagery, and more
• Handle synchronization challenges, variable sampling rates, and imperfect data collected in operational environments

• Build models using sequence focused architectures such as recurrent networks, temporal convolutional networks, and transformer based systems
• Apply contrastive learning, masked prediction, temporal forecasting objectives, and multimodal alignment techniques
• Leverage transfer learning, adapter based methods, and few shot strategies for efficient adaptation
• Evaluate models using both technical metrics and end user or business driven performance indicators

• Develop high performance pipelines in Python using scientific and data libraries
• Optimize components with C++ or CUDA when needed for large scale preprocessing or training
• Train models using modern deep learning frameworks such as PyTorch, Tensor Flow, or JAX
• Scale training across multiple GPUs and nodes using distributed and mixed precision techniques
• Build reliable data pipelines for ingesting, cleaning, segmenting, and aligning massive sensor datasets

• Strong grounding in linear algebra, probability, statistics, and optimization
• Experience with signal processing methods such as Fourier analysis, wavelets, filtering, and time frequency techniques
• Familiarity with numerical methods for modeling dynamic systems and solving inverse problems

• Work closely with engineers, researchers, product leaders, and end users from technical and non technical backgrounds
• Clearly explain model behavior, uncertainty, and tradeoffs to diverse audiences
• Contribute to documentation, design discussions, and technical decision making

• MS or PhD in Computer Science, Artificial Intelligence, Data Science, or a closely related field
• Three or more years of hands on experience in machine learning, data science, or applied AI

This role offers the opportunity to work at the frontier of foundation models for time series and sensor data, solving problems that matter in the physical world and shaping systems that operate at scale in complex, real environments.

Talent Partners for the AI Revolution. As a globally recognized staffing and consulting firm, we specialize in placing the top 2% of technology and go-to-market professionals with the world’s largest and most well-known companies.

For over 20 years, we have maintained the status of tier-one vendor for firms such as Palantir, Amazon, Fluidstack, Bloomberg, Relativity Space, Firefly, Master Card, Visa, Two Sigma, Citadel, as well as other major financial services firms, elite hedge funds, Google-backed tech start-ups, and major software firms.

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