Senior Staff Machine Learning Engineer

Description

The company runs a trade network that uses AI to help governments and businesses manage risk and compliance while goods keep moving.

This role leads the machine learning work behind the company's agentic platform for global trade, which processes a continuous flow of public and proprietary data on shipments, customs filings and trade documents.

The models and agents you build turn that data into real-time decisions on risk, compliance and enforcement for governments and large companies.

Responsibilities

• Set the technical direction for agentic systems that process trade documents as they arrive and produce risk, classification and compliance decisions in real time, including the architecture and roadmap.
• Lead the design of reliable, cost-efficient pipelines covering ingestion, document understanding, entity resolution, inference and model routing at global trade scale.
• Fine-tune and post-train models (SFT, DPO, RL with domain graders) using analyst feedback, production results and agent traces, where this clearly outperforms prompting, and keep models improving over time.
• Build the evals, tracing and feedback loops that decide what gets released and detect regressions in production.
• Mentor senior engineers, raise the technical bar across Engineering, and help shape hiring and leveling for ML roles.
• Partner with software engineers, data scientists, product managers and customers to propose new business ideas and turn unclear problems into shipped features.

Requirements

• BS or MS in computer science, machine learning or a related field, or equivalent experience.
• 10+ years building and shipping production ML systems, including product-focused work and recent work on LLM-powered products.
• Deep expertise in PyTorch and the modern Python ML stack, such as Hugging Face, scikit-learn, NumPy and Pandas or Polars.
• Hands-on experience building production LLM applications, including retrieval-augmented generation (RAG), tool and function calling, structured outputs and agentic workflows.
• Experience shipping agentic systems and handling their failure modes: tool use, planning, state, recovery and human-in-the-loop.
• Experience post-training or adapting LLMs (SFT, DPO, RLHF/RLAIF, reward modeling) for a production use case.
• Strong evaluation practice, with experience building eval suites, golden datasets and LLM-as-judge pipelines.
• Good judgment on when to use a frontier model API, a fine-tuned open-weight model or a classical ML model, weighing cost, latency and accuracy.
• Senior Staff-level technical leadership: you set architecture direction across teams, mentor engineers and turn ambiguous problems into roadmaps.
• Enthusiasm for applying state-of-the-art techniques to real problems, and willingness to work hands-on with the data and domain to reach measurable results.
• A strong commitment to engineering quality, supportability and knowledge sharing.
• Strong written and verbal communication.
• Daily use of AI coding tools such as Cursor and Claude Code.
• Preferred: experience building multi-agent systems and agent tooling, such as LangGraph.
• Preferred: hybrid retrieval using vector search, re-rankers and embedding fine-tuning.
• Preferred: vision-language models for document understanding of invoices, bills of lading and customs forms.
• Preferred: multilingual NLP.
• Preferred: LLMOps with MLflow, Databricks Mosaic AI and guardrails.
• Preferred: experience with safety, guardrails or auditability for AI in regulated or high-stakes areas such as customs, legal, healthcare or finance.
• Tools the team uses: Python and Spark; Docker, Git, Kubernetes, Swagger/OpenAPI, AWS and Azure; Databricks, OpenSearch and Postgres; PyTorch, Hugging Face, FAISS and MLflow; vLLM, LangGraph, LangSmith or Braintrust, and frontier model APIs.

Conditions

Remote or hybrid, based in the United States or the UK, with the option to work from a hub in New York City, Boston, Washington D.C. or London.

US base salary range: $250,000 to $295,000 USD per year. Actual pay depends on factors such as experience, education, training, critical skills and business needs.

The recruiter assigned to this role can share more about compensation and benefits during the hiring process.

Equal opportunity: the company considers applicants without regard to protected characteristics.

Offers come from an the company's site email address. The company never asks for payment during interviews or onboarding.

Benefits

• Equity grants are included in most full-time offers and count toward total compensation.
• A discretionary bonus or variable compensation plan, depending on the role.
• Flexible time off with no set allowance.
• Paid parental leave: 14 weeks for non-birthing, adoptive and foster parents, and up to 26 weeks for birthing parents, paid at 100% of base salary.
• Medical, vision and dental coverage, with a high-deductible plan fully paid by the employer, an employer contribution to a Health Savings Account, and an optional Flexible Spending Account.
• Life, short-term and long-term disability, and AD&D insurance at no cost to the employee.
• Fidelity 401(k) with investment options.
• Pre-tax commuter benefits for public transit or parking.
• Free premium subscription to the Calm meditation app.
• Pet insurance through Wishbone, and a Total Pet vet service and telehealth discount plan.
• Free confidential employee assistance program.
• Dependent Care FSA for pre-tax childcare savings.