Senior Staff 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. \n Accountabilities

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.

Requirements:

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.

Benefits:

Base salary: $250,000–$295,000 USD per year. Final compensation depends on experience, education, training, specialized skills, and business considerations.

Equity: Competitive equity grants are included in the majority of full-time offers.

Performance compensation: A discretionary bonus or variable compensation plan may be available, depending on the role.

Flexible time off: Flexible Time Off (FTO) designed to support work-life balance.

Paid parental leave: Up to 26 weeks at 100% of base salary for birthing parents and 14 weeks at 100% of base salary for non-birthing, adoptive, and foster parents.

Healthcare: Medical, dental, and vision coverage with generous employer contributions and flexible plan options.

Health savings: A high-deductible health plan with premiums fully covered by the employer for employees, plus an employer contribution to a Health Savings Account (HSA).

Flexible spending accounts: Access to healthcare Flexible Spending Accounts (FSA) and a Dependent Care FSA for eligible childcare expenses.

Insurance protection: Employer-paid life insurance, short- and long-term disability coverage, and accidental death and dismemberment (AD&D) insurance.

Retirement savings: Access to a Fidelity 401(k) retirement savings program.

Commuter benefits: Pre-tax savings for eligible public transportation and parking expenses.

Wellness support: Complimentary premium access to Calm for meditation, sleep, and mindfulness.

Pet care: Access to pet insurance and veterinary care or telehealth discount services.

Employee assistance: Free access to confidential personal support through an Employee Assistance Program.

Work flexibility: Remote or hybrid work options in the United States, with the possibility of working from hubs in New York City, Boston, or Washington, D.C.

Technical influence: The opportunity to define AI architecture, shape product capabilities, mentor experienced engineers, and work on complex, high-impact problems involving global trade.

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