Senior Staff Machine Learning Engineer

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.