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Salary: USD 250,000 - 295,000 / annual
Altana is building the network for trusted trade, using AI to empower governments and businesses to create a more resilient and secure global economy. We're seeking a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, an agentic platform for global trade.
You will drive the technical direction for agentic systems that process continuous streams of public and proprietary trade data (shipments, customs filings, trade documents) and turn it into real-time decisions on risk, compliance, and enforcement for governments and the world's largest companies.
Key Responsibilities:
- Define the architecture and roadmap for models and agentic systems that ingest trade data and produce risk, classification, and compliance decisions in real time
- Lead the design of reliable, cost-efficient systems for ingestion, document understanding, entity resolution, inference, and routing across models at global trade scale
- Use analyst feedback, production outcomes, and agent traces to fine-tune and post-train models (SFT, DPO, RL with domain graders) where it measurably outperforms prompting
- Build the evals, tracing, and feedback loops that determine what ships to production and catch regressions
- Mentor senior engineers, raise the technical bar across Engineering, and shape how the company hires and levels ML talent
- Work with software engineers, data scientists, product managers, and customers to generate new business ideas and ship new capabilities
You will work with Python, PyTorch, Hugging Face, LangGraph, vLLM, and frontier model APIs. The tech stack includes Docker, Kubernetes, AWS/Azure, Databricks, OpenSearch, Postgres, and MLflow.
Requirements:
- BS/MS in Computer Science, Machine Learning, or related field, or equivalent experience
- 10+ 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 about evaluation: 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
- Senior Staff-level technical leadership: set architecture direction across teams, mentor engineers, turn ambiguous problems into roadmaps
- Passionate about applying state-of-the-art techniques to real-world problems with willingness to get hands dirty 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 (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
