Type: Full-time

Term: Long-term

At Sphere, we partner with organizations to transform their operations by embedding advanced technology and data-driven processes into everything they do. We combine global expertise with strong engineering and research capabilities to help ambitious teams build scalable AI solutions.

Sphere is working with companies in the energy sector, applying AI-first infrastructure and machine learning to improve efficiency, reliability, and performance across large-scale industrial systems. We are looking for a Senior AI Researcher to join our team and contribute to the design, experimentation, and optimization of advanced machine learning models used in production environments.

Responsibilities:

• Applied AI & Machine Learning Research
  Conduct applied research to design, evaluate, and improve machine learning models used in real-world industrial systems.

• Model Development & Experimentation
  Develop custom models, loss functions, and training pipelines; design experiments to evaluate performance, robustness, and scalability.

• Performance Optimization
  Profile and optimize model training and inference for efficiency across large datasets and distributed environments.

• Cross-Domain Optimization
  Apply expertise across domains such as Computer Vision, NLP, Meta Learning, and representation learning to improve model accuracy and generalization.

• Research Engineering
  Build reusable research frameworks, experimental tooling, and prototype pipelines to accelerate iteration and validation.

Requirements:

• Experience with Python and C++ - 4+ years of combined academic and/or industry experience in AI or machine learning research.

• Experience with PyTorch and/or TensorFlow.

• Experienced in design and implement custom machine learning models, including training logic, optimization strategies, and evaluation workflows.

• Experience working with cloud platforms and/or high-performance computing environments for large-scale model training.

• Understanding of software engineering best practices, including version control, debugging, testing, and documentation.

Nice to Have:

• Experience working with large-scale or industrial datasets.

• Background in distributed systems or performance-critical ML workloads.

• Publications, patents, or open-source contributions in AI or machine learning.