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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** ALTANA - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $205,000.0 - $240,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, Data Stores, Cursor, Information Retrieval, Python (Programming Language), PostgreSQL, Machine Learning, Language Modeling, NumPy, Swagger, Search Technologies, Openapi, Pytorch, Large Language Models, Claude Code, Multi-Agent Systems, Apache Spark, Generative AI, AI Coding Agents, Git, Pandas, LangSmith, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Machine Learning Operations, FAISS, VLLM, OpenSearch, Docker, Databricks - **Published:** October 15, 2026 - **Apply:** https://startup.jobs/staff-machine-learning-engineer-altana-ai-10456961 ## About the Role * 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 ## Description * 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., Our values are the core beliefs that shape who we are, what we stand for, and how we behave.They form the foundation of Altana's culture and integrity and guide how we hire, design, build, and connect with each other and our customers. * Trust: Our customers and partners entrust us with missions of the highest importance. We honor that by keeping our word, meeting commitments, and ensuring every action we take reinforces confidence in us. We rely on each other to deliver, to speak openly, and to hold ourselves accountable. * Resilience: In a world of uncertainty and complexity, our work must withstand challenges, evolve with conditions, and ensure reliability over time. Resilience is both how we operate and what we deliver. It's how we respond when things don't go to plan -- we adapt, we support each other, and we keep moving forward. * Stewardship: We are stewards of every mission we touch. Because our work impacts lives and futures, we hold ourselves accountable to delivering mission impact and never compromising. Our responsibility extends beyond individual projects to the broader system of global trade. We believe that stewardship starts from within so that we can bring focus, creativity, and excellence to our work. Each of us is personally responsible for fostering a workplace where people can thrive. And we are stewards of the greater good of the company. By holding ourselves and each other accountable, we build a culture of innovation and collective success that reflects the scale of our mission. * Courage: Courage is what unlocks the seemingly impossible for our customers. It's the core value that drives us make bold moves and take on big, complicated network problems-the ones others avoid. We know success isn't guaranteed, but we have the audacious vision to believe a solution is possible and to build it. Courage fuels our growth mindset. It means embracing challenges that make us stronger, and it's demonstrated by how we approach hard conversations and complex projects.