Senior Staff Machine Learning Engineer

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.

• 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 product-oriented ML solutions and recent experience with LLM-powered products.

• Deep expertise in PyTorch and the modern Python machine learning ecosystem, including tools such as Hugging Face, scikit-learn, NumPy, and Pandas or Polars.

• Hands-on experience developing production-grade LLM applications, including retrieval-augmented generation (RAG), tool and function calling, structured outputs, and agentic workflows.

• Proven experience deploying agentic systems in production, with a strong understanding of planning, tool use, state management, recovery strategies, and human-in-the-loop processes.

• Practical experience adapting and post-training language models using techniques such as SFT, DPO, RLHF, RLAIF, or reward modeling.

• Strong expertise in model evaluation, including the development of evaluation suites, golden datasets, and LLM-as-a-judge pipelines.

• Sound technical judgment when selecting between proprietary model APIs, fine-tuned open-weight models, and classical ML approaches based on performance, cost, latency, and accuracy.

• Senior Staff-level leadership experience, including setting architecture direction across teams, mentoring engineers, and translating ambiguous problems into clear technical roadmaps.

• Strong problem-solving skills and a willingness to work directly with data and domain-specific challenges to achieve measurable business outcomes.

• Commitment to engineering quality, system reliability, operational supportability, and collaborative knowledge-sharing.

• Excellent written and verbal communication skills, with the ability to influence technical decisions and collaborate effectively across disciplines.

• Regular use of AI coding assistants such as Cursor or Claude Code as part of the development workflow.

• Preferred experience includes multi-agent systems and orchestration frameworks such as LangGraph; hybrid retrieval techniques, including vector search, reranking, and embedding fine-tuning; and vision-language models for processing invoices, bills of lading, and customs forms.

• Additional preferred qualifications include multilingual natural language processing, LLMOps tools such as MLflow or Databricks Mosaic AI, AI guardrails, and experience building safe, auditable systems for regulated or high-stakes domains.

• Familiarity with technologies such as Python, Spark, Docker, Git, Kubernetes, Swagger/OpenAPI, AWS, Azure, Databricks, OpenSearch, PostgreSQL, FAISS, vLLM, and model evaluation and observability platforms is beneficial.