Staff Machine Learning Engineer
About The Position
Altana is hiring a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, its agentic platform for global trade. The role focuses on public and proprietary trade data—including shipments, customs filings, and trade documents—and building models and AI agents that support real-time risk, compliance, classification, and enforcement decisions for governments and large companies. The engineer will set technical direction for agentic systems and lead the design of a reliable, cost-efficient pipeline spanning data ingestion, document understanding, entity resolution, inference, and model routing. They will adapt and improve models using analyst feedback, production outcomes, and agent traces; build evaluation, tracing, observability, and feedback systems; and help determine what ships while catching production regressions. This is a senior technical leadership role that includes mentoring engineers, raising technical standards, contributing to ML hiring and leveling, and collaborating with engineering, data science, product, and customers to turn ambiguous problems into shipped capabilities. The position is based in Brooklyn, New York, with hybrid flexibility; candidates must live within commuting distance of the Williamsburg office.
Requirements
- Bachelor’s or master’s degree in Computer Science, Machine Learning, or a related field, or equivalent experience.
- At least 8 years of experience building and shipping production ML systems, with a track record of product-oriented ML solutions and recent work on LLM-powered products.
- Deep expertise in PyTorch and the modern Python ML stack, including Hugging Face, scikit-learn, NumPy, and Pandas or Polars.
- Hands-on experience building production LLM applications using RAG, tool or function calling, structured outputs, and agentic workflows.
- Experience shipping agentic systems in production and understanding failure modes involving tool use, planning, state, recovery, and human-in-the-loop workflows.
- Experience post-training or adapting LLMs for production use, using methods such as SFT, DPO, RLHF/RLAIF, or reward modeling.
- Experience building rigorous evaluation systems, including evaluation suites, golden datasets, and LLM-as-judge pipelines.
- Sound judgment in selecting frontier-model APIs, fine-tuned open-weight models, or classical ML models while balancing cost, latency, and accuracy.
- Passion for applying state-of-the-art techniques to real-world problems and willingness to work directly with data and domain details to achieve measurable outcomes.
- Commitment to engineering excellence, supportability, and knowledge-sharing.
- Strong written and verbal communication skills.
- Regular use of AI coding tools such as Cursor or Claude Code in daily work.
- Must be within commuting distance of Altana’s Williamsburg, Brooklyn office.
Nice To Haves
- Experience building multi-agent systems and agent tooling such as LangGraph.
- Experience with hybrid retrieval, including vector search, rerankers, and embedding fine-tuning.
- Experience with vision-language models for document understanding, such as invoices, bills of lading, and customs forms.
- Experience with multilingual NLP.
- Experience with LLMOps tools such as MLflow, Databricks Mosaic AI, and guardrails.
- Experience with AI safety, guardrails, or auditability in regulated or high-stakes domains such as customs, legal, healthcare, or finance.
- Experience with technologies listed by Altana: Python, Spark, Docker, Git, Kubernetes, Swagger/OpenAPI, AWS, Azure, Databricks, OpenSearch, Postgres, PyTorch, Hugging Face, FAISS, MLflow, vLLM, LangGraph, LangSmith/Braintrust, and frontier-model APIs.
Responsibilities
- Set the technical direction, architecture, and roadmap for agentic systems that process trade data and produce real-time risk, classification, and compliance decisions.
- Lead the design of reliable, cost-efficient systems for ingestion, document understanding, entity resolution, inference, and routing across models at global-trade scale.
- Fine-tune and post-train models using analyst feedback, production outcomes, and agent traces, applying methods such as SFT, DPO, and reinforcement learning with domain graders where they outperform prompting.
- Build evaluation, tracing, observability, and feedback loops that guide releases and detect production regressions.
- Mentor senior engineers, raise the technical bar across Engineering, and help shape ML talent hiring and leveling.
- Collaborate with software engineers, data scientists, product managers, and customers to develop business ideas and turn ambiguous problems into shipped capabilities.
Benefits
- Competitive equity grants are included in the majority of full-time offers.
- A discretionary bonus or variable compensation plan, depending on the role.
- Flexible Time Off (FTO).
- 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 benefits with employer contributions.
- Employer-paid high-deductible health plan for employees, with an employer contribution to a Health Savings Account (HSA).
- Flexible Spending Account (FSA) option.
- Life, short-term disability, long-term disability, and AD&D insurance at no cost to employees.
- Fidelity 401(k) retirement savings program.
- Commuter benefits for public transit or parking.
- Free premium Calm subscription.
- Pet insurance and a pet veterinary service and telehealth discount plan.
- Employee Assistance Program with confidential personal support.
- Dependent Care FSA for childcare expenses.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
