Machine Learning Engineer

About Us

We’re a technology and AI company focused on building intelligent, scalable systems that can be designed once and deployed everywhere. Our goal is to harness advanced AI to make products smarter, more efficient, and more sustainable—reducing waste, improving decision-making, and pushing the boundaries of what technology can do.

About the Role

As a Machine Learning Engineer, you’ll play a key role in developing models and systems that power our next-generation AI solutions. You’ll work on everything from data pipelines to model training, optimization, and deployment—ensuring our AI is high-performing, reliable, and aligned with real-world needs.

What You’ll Do

• Build, train, and optimize machine learning models for production use

• Collaborate with engineering teams to deploy models across scalable systems

• Develop efficient data pipelines and workflows

• Improve model performance, accuracy, and resource efficiency

• Help integrate responsible and sustainable AI practices into our products

• Participate in cross-functional discussions around architecture, product features, and innovation

What We’re Looking For

• Strong background in machine learning and deep learning

• Experience with Python and ML frameworks (TensorFlow, PyTorch, etc.)

• Understanding of scalable systems, cloud environments, and MLOps practices

• Ability to work with large datasets and optimize model performance

• Clear communication and a collaborative mindset

• Curiosity, initiative, and a desire to build AI that’s both powerful and responsible

Nice to Have

• Experience in reinforcement learning, NLP, or multimodal AI

• Familiarity with distributed training or hardware optimization

• Interest in sustainable computing or low-energy AI solutions

• Prior work with data engineering or DevOps tooling

Why Join Us

• Work on cutting-edge AI with meaningful, real-world impact

• Build technology that supports global deployment and long-term sustainability

• Join a team that values innovation, ownership, and continuous learning

• Competitive compensation, equity, and benefits

• Flexible remote work and a supportive culture