Senior Machine Learning Engineer in United States at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in the United States.

As a Senior Machine Learning Engineer, you will help design and optimize advanced deep learning solutions that power large-scale predictive applications. Working alongside experienced data scientists and engineers, you will contribute across the entire machine learning lifecycle—from feature engineering and model development to production optimization and continuous improvement. This role offers the opportunity to apply state-of-the-art research to real-world challenges while building scalable, high-impact AI systems. You'll collaborate in a highly technical, innovation-driven environment where experimentation, engineering excellence, and continuous learning are encouraged. If you're passionate about advancing machine learning and delivering production-ready solutions, this role offers significant technical ownership and career growth.

• Design, develop, and optimize deep learning models using large-scale customer and behavioral datasets to improve predictive performance.
• Engineer and refine features that enhance model accuracy, scalability, and business impact across production machine learning systems.
• Research, evaluate, and implement state-of-the-art machine learning techniques inspired by leading academic publications and emerging industry advancements.
• Collaborate closely with cross-functional engineering and data science teams to translate business objectives into effective machine learning solutions.
• Improve model training pipelines, infrastructure, and workflows to maximize efficiency, scalability, and reproducibility.
• Document technical approaches, experimental results, architectural decisions, and implementation best practices to support knowledge sharing.
• Participate in peer code reviews, technical discussions, and engineering initiatives that promote software quality and continuous improvement.
• Contribute to the evolution of machine learning engineering standards, tooling, and deployment practices across the organization.

• Strong professional experience developing machine learning and deep learning solutions in production environments.
• Expert proficiency with deep learning frameworks such as PyTorch or TensorFlow.
• Strong background in feature engineering, supervised learning, neural network architectures, optimization techniques, and predictive modeling.
• Excellent Python programming skills for machine learning, data processing, and model development.
• Demonstrated ability to interpret, evaluate, and implement techniques from academic machine learning research.
• Experience optimizing model training pipelines for performance, scalability, and maintainability.
• Strong analytical thinking and problem-solving skills with the ability to thrive in fast-changing, ambiguous technical environments.
• Excellent collaboration and communication skills for working effectively with engineering, data science, and client-facing teams.
• Experience with MLOps, model deployment, monitoring, CI/CD pipelines, or workflow orchestration is preferred.
• Familiarity with cloud platforms such as Google Cloud Platform (GCP), distributed training, time-series modeling, causal inference, or attribution modeling is considered an advantage.
• Previous consulting or client-facing technical experience is a plus.
• Authorization to work in the country of residence and availability to collaborate within Western Hemisphere time zones.

• Competitive salary ranging from $145,000 to $250,000 USD, depending on experience and location.
• Fully remote position with preference for candidates located in the United States.
• 100% employer-paid medical insurance premiums for employees.
• Self-managed paid time off with a minimum time-off policy that encourages work-life balance.
• Opportunity to work on cutting-edge artificial intelligence and deep learning initiatives.
• Collaborative, innovation-focused engineering culture with significant technical ownership.
• Career development framework supporting continuous learning and professional growth.
• Opportunity to contribute to open-source technologies and collaborate with globally distributed engineering teams.