Senior Staff Machine Learning Engineer

Altana; Ashington; On-site; GBP 170,000 - 230,000; Full time

Benefits offered by this job

Competitive salary

Learning & development support

Health benefits

Generous paid time off

Equity grants

Parental leave

Job summary

Altana is seeking a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, our agentic platform for global trade. You will architect and ship production-grade ML systems handling shipments, customs filings, and invoices to enable real-time risk and compliance decisions for governments and major enterprises.

You will mentor engineers, shape the ML roadmap, and collaborate with product managers and customers to turn ambiguous problems into shipped capabilities.

Qualifications

• Senior staff level technical leadership with a track record of delivering ML products.
• Experience shipping production LLM/RAG-based solutions at scale.
• Strong foundation in PyTorch and modern Python ML stack.

Responsibilities

• Lead architecture and roadmap for agentic ML systems.
• Mentor engineers and shape ML talent across teams.
• Design end-to-end data-to-decision pipelines for real-time risk and compliance.

Skills

Technical leadership

LLM deployment

Production ML systems

PyTorch

Python

Multilingual NLP

MLOps

Model monitoring

Cross-functional collaboration

Problem solving

Education

BS/MS in CS/ML or equivalent

Tools

Docker

Git

Kubernetes

Swagger/OpenAPI

AWS

Azure

MLflow

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
• 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
• 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

Benefits

• Competitive base salary: We offer competitive base salaries to recognize the incredible work our employees do.
• Learning & development: We support your development through regular performance check-ins and offer financial support for Learning and Development initiatives.
• Health benefits: We offer full global benefits, including a 100% employer-paid plan and tax-advantaged HSA and FSA options in the US.
• Paid time off: We provide a generous PTO policy, including paid sick time, to support your well-being in and out of the office.
• Equity program: We instill ownership by offering competitive new hire equity grants, plus the opportunity to gain more equity along the way.
• Parental leave: We offer up to 26 weeks of fully paid parental leave to support you and your family.

• Senior Staff-level technical leadership: you set architecture direction across teams, mentor engineers, and turn ambiguous problems into roadmaps
• Hands-on experience building production LLM applications: retrieval-augmented generation (RAG), tool/function calling, structured outputs, and agentic workflows
• Rigorous about evaluation: you’ve built eval suites, golden datasets and LLM-as-judge pipelines
• You’ve shipped agentic systems in production and know their failure modes: tool use, planning, state, recovery and human-in-the-loop
• You have strong written and verbal communication skills
• Heavy user of AI coding tools (Cursor, Claude Code) in your daily workflow
• 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
• Deep expertise in PyTorch and the modern Python ML stack (Hugging Face, scikit‑learn, NumPy, Pandas/Polars)
• BS/MS in Computer Science, Machine Learning, or a related field, or equivalent experience
• 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
• Experience post‑training or adapting LLMs (SFT, DPO, RLHF/RLAIF, reward modeling) for a production use case
• 10+ 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
• You care deeply about engineering excellence, supportability and knowledge‑sharing
• Building multi‑agent systems and agent tooling (e.g., LangGraph)
• Vision‑language models for document understanding (invoices, bills of lading, customs forms)
• Hybrid retrieval: vector search, re‑rankers, and embedding fine‑tuning
• 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)