About This Role
This Senior Staff Machine Learning Engineer role involves leading the AI development for Altana Nexus, an agentic platform focused on global trade. The position requires working with continuous streams of public and proprietary trade data to build models and AI agents that facilitate real-time decisions on risk, compliance, and enforcement for governments and major corporations.
Responsibilities
- Drive the technical direction for agentic systems
- Define the architecture and roadmap for models and agentic systems that process trade data (shipments, customs declarations, invoices, bills of lading) and generate 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 the scale of global trade
- Adapt models to trade using analyst feedback, production outcomes, and agent traces to fine-tune and post-train models (SFT, DPO, RL with domain graders) where it measurably beats prompting, ensuring continuous model improvement
- Design the observability and feedback stack by building evals, tracing, and feedback loops to determine what ships and to catch production regressions
- Lead beyond your team by mentoring senior engineers, raising the technical bar across Engineering, and helping shape ML talent hiring and leveling
- Shape the product and the business by working with software engineers, data scientists, product managers, and customers to generate new business ideas and translate ambiguous problems into shipped capabilities
Requirements
- BS/MS in Computer Science, Machine Learning, or a related field, or equivalent experience
- 8+ years building and shipping ML systems in production, with a proven track record of delivering product-oriented ML solutions, including recent work on LLM-powered products
- Deep expertise in PyTorch and the modern Python ML stack (Hugging Face, scikit-learn, NumPy, Pandas/Polars)
- Hands-on experience building production LLM applications: retrieval-augmented generation (RAG), tool/function calling, structured outputs, and agentic workflows
- Experience shipping agentic systems in production and understanding 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
- Rigorous about evaluation: you've built eval suites, golden datasets, and LLM-as-judge pipelines
- Sound judgment on when to use a frontier model API, a fine-tuned open-weight model, or a classical ML model, balancing cost, latency, and accuracy
- Passionate about applying SOTA techniques to real-world problems, with a willingness to get your hands dirty in the data and domain to achieve measurable outcomes
