Senior Machine Learning Engineer

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

This senior technical leadership role offers the opportunity to build advanced AI systems that transform complex global trade data into actionable intelligence for risk management, compliance, and enforcement. You will shape the architecture and technical direction of agentic AI systems, developing production-ready models and workflows that process large volumes of shipment records, customs declarations, invoices, and trade documents. Combining machine learning research with practical engineering, you will lead initiatives across data ingestion, document understanding, entity resolution, model inference, and intelligent decision-making.

You will also establish robust evaluation and monitoring frameworks to improve reliability, accuracy, and performance in production. Working closely with engineering, data science, product, and customer-facing teams, you will influence product strategy while mentoring senior engineers and raising technical standards across the organization. This opportunity is ideal for an experienced ML leader who thrives on solving complex real-world problems and delivering measurable impact through cutting-edge AI.

• Define the technical vision, architecture, and roadmap for agentic AI systems that process global trade data and generate real-time risk assessments, classifications, and compliance decisions.
• Lead the design and implementation of scalable, reliable, and cost-efficient data pipelines covering ingestion, document understanding, entity resolution, model inference, and intelligent routing.
• Develop and deploy production-grade machine learning systems that transform structured and unstructured trade data, including shipments, customs declarations, invoices, and bills of lading, into actionable insights.
• Adapt and improve large language models using supervised fine-tuning (SFT), direct preference optimization (DPO), reinforcement learning, and domain-specific evaluation techniques when these approaches provide measurable improvements over prompting alone.
• Build and maintain evaluation frameworks, golden datasets, tracing systems, and feedback loops to measure model quality, detect regressions, and continuously improve production performance.
• Design observability and monitoring capabilities that capture agent behavior, identify failure modes, and support reliable operation across complex workflows.
• Determine when to use frontier model APIs, fine-tuned open-weight models, or traditional machine learning approaches, balancing accuracy, latency, cost, and operational requirements.
• Provide technical leadership across engineering teams by establishing architectural standards, mentoring senior engineers, and contributing to hiring, technical assessment, and career development practices.
• Collaborate with software engineers, data scientists, product managers, and customers to identify opportunities, clarify ambiguous requirements, and turn business challenges into production-ready capabilities.
• Promote engineering excellence, maintainability, supportability, and knowledge-sharing across the machine learning organization.
• Explore emerging AI techniques and tools to improve model capabilities, agent orchestration, document processing, and decision-making in complex trade environments.

• Bachelor’s or master’s degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
• At least 10 years of experience building and deploying machine learning systems in production, including a strong track record of delivering…