Applied ML Engineer - Model R&D

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied ML Engineer – Model R&D based in United States. We are looking for a hands-on Applied ML Engineer to bridge the gap between machine learning research and production-ready engineering systems. In this role, you will transform experimental models and research prototypes into reliable, scalable solutions that deliver measurable business impact. Working closely with researchers, engineers, and quantitative specialists, you will take ownership of the full deployment lifecycle, from data pipelines and feature engineering to model serving and monitoring. You will help establish strong software engineering practices within a fast-moving, highly autonomous environment. The position offers an opportunity to contribute to applied ML research while focusing primarily on implementation, system reliability, and performance. You will also support the evolution toward Rust-based infrastructure and work on technology at the intersection of AI, machine learning, and financial markets. This is a remote-first opportunity requiring some overlap with U. S. business hours. Accountabilities: - Transform research-grade machine learning code and experimental prototypes into production-ready, maintainable, and scalable software systems. - Take ownership of end-to-end ML deployment pipelines, including data ingestion, feature engineering, model integration, inference, serving, monitoring, and reliability. - Work closely with machine learning researchers and engineering teams to translate research objectives and technical concepts into robust, observable production solutions.

- Introduce and strengthen structured software development practices, including task management, Git workflows, code reviews, testing, and continuous integration and deployment (CI/CD). - Identify architectural weaknesses, technical debt, and engineering quality issues, recommending and implementing solutions that improve maintainability, scalability, and system performance. - Build and maintain monitoring, logging, alerting, and observability systems to detect issues proactively and ensure reliable operations. - Contribute to the transition toward Rust-native infrastructure by supporting the adoption, integration, and development of Rust-based systems. - Participate in applied ML research and experimentation, helping evaluate promising approaches and translate relevant findings into practical engineering solutions. - Promote a culture of technical excellence, shared ownership, continuous improvement, and constructive collaboration across the team. Requirements: - Professional experience in a structured software engineering environment, working within collaborative teams where architecture, code quality, peer reviews, and shared system ownership are essential. - Strong software engineering and programming skills, with demonstrated experience building, maintaining, and deploying production-grade applications and systems. - Proven ability to take complex or experimental technical work and turn it into reliable, maintainable, and scalable production solutions. - Strong understanding of software architecture, engineering best practices, version control, code reviews, testing, and collaborative development workflows. - Experience with task management and development tools such as Jira or Linear, as well as Git-based workflows and CI/CD practices. - Genuine interest in artificial intelligence and machine learning, with familiarity or practical exposure to model training, inference pipelines, or collaboration with ML research teams. - Strong problem-solving and analytical skills, with the ability to work independently, navigate ambiguity, and make sound technical decisions in a fast-paced environment.