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Staff Machine Learning Engineer

Altana Technologies, Inc.

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At a glance

Compensation
No compensation found
Location
Washington, DC
Work Authorization
Not specified

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Job overview

Altana Technologies seeks a Senior Staff Machine Learning Engineer to lead the AI behind its agentic global-trade platform. The role sets technical direction for models and agentic systems, builds production pipelines that turn trade data into real-time decisions, and adapts models using feedback and evaluation. It also mentors engineers and partners across teams and with customers to shape product capabilities and business ideas.

Skills & qualifications

RequiredNice to have

Skills

PyTorchPython ML StackHugging FaceScikit-LearnNumPyPandasPolarsLLM ApplicationsRetrieval-Augmented GenerationTool CallingStructured OutputsAgentic WorkflowsAgentic SystemsLLM Post-TrainingSupervised Fine-TuningDirect Preference OptimizationRLHF/RLAIFReward ModelingEvaluation SuitesGolden DatasetsLLM-as-Judge PipelinesModel SelectionCommunicationAI Coding ToolsCursorClaude CodeMulti-Agent SystemsAgent ToolingLangGraphHybrid RetrievalVector SearchRe-RankersEmbedding Fine-TuningVision-Language ModelsMultilingual NLPLLMOpsMLflowDatabricks Mosaic AIGuardrailsAI Safety

Qualifications

BS/MS in Computer Science, Machine Learning, or Related Field or Equivalent Experience8+ Years Building ML Systems

Full job description

We're looking for a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, our agentic platform for global trade. You'll work on a continuous stream of public and proprietary trade data (shipments, customs filings, trade documents), and build the models and AI agents that turn it into real-time decisions on risk, compliance and enforcement for governments and the world's largest companies. ## Your Responsibilities - Drive the technical direction for agentic systems. Define the architecture and roadmap for the models and agentic systems that process trade data as it arrives (shipments, customs declarations, invoices, bills of lading) and turn it into risk, classification and compliance decisions in real time. - Build the pipeline from ingestion to decision. 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. Use 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, and keep every model improving over time. - Design the observability and feedback stack. Build the evals, tracing and feedback loops that decide what ships and catch regressions in production. - Lead beyond your team. Mentor senior engineers, raise the technical bar across Engineering, and help shape how we hire and level ML talent. - Shape the product and the business. Work with software engineers, data scientists, product managers and customers to generate new business ideas and turn ambiguous problems into shipped capabilities. ## About You - 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 - You've shipped agentic systems in production and know 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 - You care deeply about engineering excellence, supportability and knowledge-sharing - You have strong written and verbal communication skills - Heavy user of AI coding tools (Cursor, Claude Code) in your daily workflow Nice to have, but not required - Building multi-agent systems and agent tooling (e.g., LangGraph) - Hybrid retrieval: vector search, re-rankers, and embedding fine-tuning - Vision-language models for document understanding (invoices, bills of lading, customs forms) - Multilingual NLP - LLMOps: MLflow, Databricks Mosaic AI, and guardrails - Experience with safety, guardrails or auditability for AI in regulated or high-stakes domains (e.g., customs, legal, healthcare, finance) Technologies we love - Languages: Python, Spark - Tools: Docker, Git, Kubernetes, Swagger/OpenAPI, AWS, Azure - Datastores: Databricks, OpenSearch, Postgres - ML Tools: PyTorch, Hugging Face, FAISS, MLflow - LLM Tools: vLLM, LangGraph, LangSmith/Braintrust, frontier model APIs At Altana, we believe that a diverse workforce enables greater creativity, performance, and adaptability. Were proud to be an equal opportunity employer and welcome you to join us as you are. Our employment opportunities and decisions are based on business needs and individual qualifications, without regard to race, color, religious creed, national origin, ancestry, age, physical or mental disability, medical condition, marital status, sexual orientation, gender identity or expression, genetic information, family care or medical leave status, military or veteran status, or any other characteristic protected by the laws or regulations in the areas in which we operate. We prohibit discrimination and harassment of any type, in any situation.

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