Machine Learning Engineer/Applied Scientist

Stealth Talent Solutions is seeking a Machine Learning Engineer to join their growing team focused on building and optimizing large-scale machine learning systems. The role involves designing, developing, and deploying predictive models for real-time environments, ensuring reliability and performance under strict requirements.

Responsibilities:

• Build and improve predictive models used in high-throughput, real-time decision systems
• Develop ML solutions capable of operating under strict latency requirements
• Design and maintain data pipelines that process and transform large-scale datasets
• Identify predictive signals within noisy, high-volume data to improve model performance
• Implement and optimize machine learning models, including deep learning architectures
• Support experimentation and evaluation of new modeling techniques
• Write scalable, production-quality code integrated into large-scale ML infrastructure
• Monitor model performance and address issues such as drift, latency bottlenecks, and system efficiency
• Analyze system performance and continuously improve model accuracy and operational efficiency

Requirements:

• 0–3 years of experience in Machine Learning, Data Science, or Software Engineering
• Strong proficiency in Python and SQL
• Solid understanding of statistics, probability, and machine learning theory
• Ability to analyze complex datasets and extract meaningful insights
• Degree in Computer Science, Applied Mathematics, Statistics, or a related quantitative field
• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Familiarity with neural networks or transformer architectures
• Exposure to large-scale data systems or cloud-based data pipelines