Machine Learning Engineer

About Scalar Labs

Scalar Labs operates as the applied intelligence arm of Polaris, developing complex world models, optimization algorithms, and agentic systems across domains such as energy, agriculture, healthcare, and resource management. Their work centers on delivering operational systems rather than mere recommendations, with success measured by system performance in real-world applications.

Responsibilities

• Develop and train predictive models using operational datasets derived from sectors including energy, agriculture, and healthcare.
• Create evaluation methodologies aligned with practical application contexts beyond standard benchmarking metrics.
• Deploy models into production environments and assume accountability for their ongoing efficacy and reliability.
• Collaborate with program teams to translate operational challenges into well-defined modeling problems.

Required Qualifications

• Demonstrated experience in building machine learning solutions that have been implemented in production settings.
• Proficiency in Python programming and familiarity with ML frameworks such as PyTorch or JAX.
• Knowledge and experience with time series analysis, probabilistic modeling, or representation learning techniques.
• Ability to work effectively with incomplete or noisy real-world data sets.

Hiring Process

• Submit a brief note along with your CV and a portfolio or link demonstrating your work.
• Engage in a technical discussion with a team engineer focusing on relevant programs.
• Participate in a practical working session involving problem-solving related to one of the active programs.
• Conclude with a final interview with leadership before final hiring decision.

Additional Information

The company maintains confidentiality regarding its partners and discusses program specifics during the technical interview phase under appropriate confidentiality agreements.