Machine Learning Engineer

Company Description

At Dusker AI, the focus goes beyond building artificial intelligence to ensuring it is reliable, scalable, and ready for real-world impact. In today's rapidly evolving AI landscape, success is not defined by model creation alone, but by how well those systems perform under real conditions. Dusker AI works at this critical intersection, helping organizations measure, evaluate, and enhance AI systems through expert-driven benchmarks and advanced evaluation frameworks.

Operating across industries such as finance, healthcare, robotics, and enterprise productivity, the company enables businesses to transition from experimental models to production-ready solutions. By leveraging cutting-edge frameworks like Terminal-Bench and state-of-the-art methodologies, Dusker AI ensures that AI systems are evaluated for reasoning, robustness, and adaptability. The ultimate goal is to deliver AI that is not only intelligent but dependable, secure, and aligned with real-world needs.

Role Description

We are seeking a Machine Learning Engineer for a full-time, remote role. The selected candidate will develop, implement, and optimize machine learning models, with a focus on creating robust, innovative, and high-performing solutions. Responsibilities include designing and training neural networks, improving algorithms, analyzing datasets, and collaborating with a cross-functional team to deliver state-of-the-art AI applications. The role also involves evaluating AI models using Dusker's proprietary benchmarks and frameworks to ensure they meet industry-specific requirements.

Key Responsibilities

• Design, develop, and deploy high-performance machine learning and deep learning models
• Build and train neural networks with a focus on accuracy, efficiency, and scalability
• Optimize algorithms for real-world production environments
• Analyze large-scale datasets to extract meaningful patterns and insights
• Implement robust data pipelines and model deployment workflows
• Evaluate AI systems using advanced benchmarking frameworks and methodologies
• Collaborate with researchers, engineers, and product teams to deliver cutting-edge AI solutions
• Continuously improve model performance through experimentation and iteration

Qualifications

• Strong foundation in Computer Science, including data structures and algorithms
• Hands-on experience with Neural Networks, Deep Learning, and Pattern Recognition
• Solid understanding of Statistics, Probability, and data analysis techniques
• Proven experience deploying machine learning models in production environments
• Proficiency in Python and frameworks such as TensorFlow, PyTorch, or similar
• Strong analytical thinking, problem-solving, and debugging skills
• Familiarity with AI evaluation, benchmarking, or model validation techniques is highly desirable
• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field