Time Series Researcher - Forward deployed

Applied Computing•Houston, TX

About The Position

Applied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable abundance for a growing planet. The hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls. We’ve raised over $32 million, including one of the largest seed rounds for an AI company in the UK. We’re just getting started. The Time Series Researcher owns the core of Orbital’s temporal intelligence. This role exists to design, validate, and deploy foundational time-series models that operate under real world constraints: noisy sensors, partial observability, physical laws, and high economic stakes. This is a customer facing role part of forward deployed team working with customer engieering teams to fine tune our model to their plants. This is not offline research. You will own the full lifecycle; from theoretical formulation and experimentation to real-time inference, uncertainty estimation, and continuous retraining in production.

Requirements

PhD in Computer Science, Statistics, Applied Mathematics, Physics, or related field
First-author publications in time-series modelling, forecasting, signal processing, or physics-informed ML
3+ years of hands-on research experience in time-series or sequence modelling
Demonstrated experience in Deep Learning & Probabilistic modelling
Expert Python skills with production-grade PyTorch code
Experience deploying ML models into real systems

Responsibilities

Orbital’s foundational time-series modelling stack
Physics-informed and probabilistic model design
Uncertainty quantification and robustness under sensor faults
Research → production translation for time-series models
Benchmarking standards and validation protocols used across the company
Design core time-series architectures supporting: Forecasting, Classification / anomaly detection, Optimisation & control-adjacent tasks
Explore and select appropriate objectives examples: Probabilistic losses, Generative formulations, Reinforcement-learning-inspired objectives where appropriate
Develop hybrid approaches that blend: Classical statistical models, Deep learning architectures, Physics-based constraints
Integrate domain physics into learning systems, including: Conservation laws, Process constraints, Differential-equation-based priors
Improve generalisation, interpretability, and extrapolation beyond training regimes
Ensure models respect physical feasibility in production settings
Design uncertainty-aware models (Bayesian, ensemble, hybrid)
Quantify confidence under: Sensor drift and failure, Regime change, Sparse or delayed ground truth
Ensure outputs are usable by operations and engineering teams, not just statistically elegant.
Containerise and deploy models using Docker on AWS / Azure (EKS, ECS, SageMaker)
Build or integrate CI/CD pipelines for: Training, Evaluation, Rollout and rollback, Automated retraining triggers
Define rigorous back-testing and evaluation protocols
Build automated benchmarking pipelines across datasets, regimes, and failure modes
Compare against classical baselines and modern deep-learning approaches
Ensure claims are defensible to customers, partners, and internal stakeholders