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Salary: USD 205,000 - 240,000 / annual
Altana is building the network for trusted trade, using AI to help governments and businesses create a more resilient and secure global economy. You will lead the technical direction for agentic AI systems that process real-time trade data (shipments, customs filings, trade documents) and generate risk, compliance, and enforcement decisions.
Key Responsibilities:
- Drive technical architecture and roadmap for agentic systems that ingest and process trade data at global scale, turning it into real-time risk and compliance decisions
- Design and build reliable, cost-efficient pipelines for ingestion, document understanding, entity resolution, inference, and model routing
- Fine-tune and post-train models (SFT, DPO, RL with domain graders) using analyst feedback and production outcomes, moving beyond pure prompting where it measurably improves performance
- Build observability, evals, tracing, and feedback loops to ensure production quality and catch regressions
- Mentor senior engineers, raise technical standards across the engineering organization, and shape ML hiring and leveling practices
- Partner with software engineers, data scientists, product managers, and customers to identify new business opportunities and ship new capabilities
This is a hybrid or remote role based in the United States or UK, with the option to work from hubs in New York City, Boston, Washington D.C., or London.
Requirements:
- BS/MS in Computer Science, Machine Learning, or related field, or equivalent experience
- 8+ years building and shipping ML systems in production with a proven track record of product-oriented ML solutions, including recent LLM-powered product work
- Deep expertise in PyTorch and the modern Python ML stack (Hugging Face, scikit-learn, NumPy, Pandas/Polars)
- Hands-on experience building production LLM applications: RAG, tool/function calling, structured outputs, and agentic workflows
- Shipped agentic systems in production and understand 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 production use cases
- Rigorous evaluation practices: built eval suites, golden datasets, and LLM-as-judge pipelines
- Sound judgment on when to use frontier model APIs, fine-tuned open-weight models, or classical ML, balancing cost, latency, and accuracy
- Passionate about applying state-of-the-art techniques to real-world problems with willingness to work deeply in data and domain
- Strong commitment to engineering excellence, supportability, and knowledge-sharing
- Strong written and verbal communication skills
- Heavy user of AI coding tools (Cursor, Claude Code) in daily workflow
Nice to Have:
- Building multi-agent systems and agent tooling (e.g., LangGraph)
- Hybrid retrieval: vector search, re-rankers, embedding fine-tuning
- Vision-language models for document understanding
- Multilingual NLP
- LLMOps: MLflow, Databricks Mosaic AI, guardrails
- Experience with safety, guardrails, or auditability for AI in regulated or high-stakes domains
