Machine Learning Engineer

Description

• Build and train ML and optimization models for production-scale use cases.
• Continuously monitor, maintain, and enhance model accuracy and scalability.
• Stay updated with advancements in optimization algorithms, ML model architectures, and applied AI techniques.
• Collaborate with engineering and research teams to develop scalable ML solutions.
• Build, train, and fine-tune machine learning and statistical models for regression, classification, and clustering.
• Collaborate with cross-functional teams to ensure models are scalable, interpretable, and production-ready.
• Apply mathematical programming (linear, nonlinear, integer, and mixed-integer optimization) to operational and decision-making scenarios.
• Design, develop, and implement optimization and forecasting models for complex analytical problems.
• Integrate ML and optimization algorithms within business or system logic for intelligent decision-making.
• Shape a first-of-its-kind AI + clean energy platform.
• Conduct feature selection, hyperparameter tuning, and performance evaluation using systematic validation techniques.
• Develop APIs and tools to make ML capabilities accessible across systems.
• Design intelligent data workflows and pipelines to support predictive and prescriptive analytics.
• The role involves performing scenario analysis and supporting optimization-based decision-making.

Requirements

We are seeking a highly skilled and analytical Machine Learning Engineer to design, develop, and optimize advanced ML and optimization models that power intelligent, data-driven decision systems. The ideal candidate will focus on building predictive, prescriptive, and optimization-based solutions to solve complex real-world challenges. This role involves close collaboration with data scientists, engineers, and domain experts to build scalable, production-ready ML systems.

• Proficiency in version control using Git.
• Passion for scalable systems, automation, and applied problem-solving.
• Strong background in mathematical modeling, statistics, or operations research.
• The candidate should possess a strong background in operations research and optimization model formulation.
• 1–2 years of hands-on experience in machine learning, optimization, or forecasting.
• Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or related quantitative fields.
• Knowledge of workflow automation and integration of optimization models with APIs will be an advantage.
• The candidate should have strong proficiency in Python, with practical experience for building optimization models.
• Strong mathematical and statistical foundation.
• Clear documentation skills and the ability to communicate technical results effectively are essential.
• The candidate should demonstrate strong problem-solving and analytical thinking abilities.
• Experience in production-grade model deployment and performance monitoring.
• Proven track record of implementing ML or optimization models in production environments.
• The role requires experience in algorithm design, numerical methods, and sensitivity analysis for robust model development.
• Experience in model validation and performance tuning is required to ensure accuracy and efficiency.
• A solid understanding of linear programming, mixed-integer programming, convex optimization, stochastic optimization, and constraint programming is essential.

Benefits

• Work with a small, mission-driven team obsessed with impact.
• A chance to leave your mark at the intersection of AI and sustainability.

Training + Development

Interview process

Visa Sponsorship

Security clearance