Altana · Posted 1d ago
Staff Machine Learning Engineer
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇬🇧 Ashington, United Kingdom
- Contract
- Full-time
- Role
- ML engineering
- Source
- Stapply Data
Stack
- Databricks
- Spark
- Pandas
- Polars
- NumPy
- scikit-learn
- PyTorch
- MLflow
- Hugging Face
- LangChain
- LLM
- Vector DB
- PostgreSQL
- Elasticsearch
- Python
- AWS
- Azure
- Kubernetes
- Docker
The posting
Join Altana, a leading company in global trade technology. As a Senior Staff Machine Learning Engineer, you will lead the AI behind Altana Nexus, our agentic platform for global trade. You will drive the technical direction for agentic systems, build the pipeline from ingestion to decision, adapt models to trade, design the observability and feedback stack, mentor senior engineers, and shape the product and the business. You will work with a variety of technologies, including Python, Spark, Docker, Git, Kubernetes, AWS, Azure, Databricks, OpenSearch, Postgres, PyTorch, Hugging Face, FAISS, and MLflow.
- Conduire la direction technique des systèmes agentiques, définir l'architecture et la feuille de route des modèles et des systèmes agentiques. - Diriger la conception de systèmes fiables et rentables pour l'ingestion, la compréhension des documents, la résolution d'entités, l'inférence et le routage à l'échelle du commerce mondial. - Utiliser les retours des analystes, les résultats de production et les traces des agents pour affiner et post-entraîner les modèles, et maintenir chaque modèle en amélioration continue.
- You care deeply about engineering excellence, supportability and knowledge-sharing - Experience post-training or adapting LLMs (SFT, DPO, RLHF/RLAIF, reward modeling) for a production use case - Deep expertise in PyTorch and the modern Python ML stack (Hugging Face, scikit-learn, NumPy, Pandas/Polars) - 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 - Hands-on experience building production LLM applications: retrieval-augmented generation (RAG), tool/function calling, structured outputs, and agentic workflows - 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've shipped agentic systems in production and know their failure modes: tool use, planning, state, recovery and human-in-the-loop - 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 - Rigorous about evaluation: you've built eval suites, golden datasets and LLM-as-judge pipelines - BS/MS in Computer Science, Machine Learning, or a related field, or equivalent experience - You have strong written and verbal communication skills - Heavy user of AI coding tools (Cursor, Claude Code) in your daily workflow - 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)