Staff Machine Learning Engineer
About The Position
Altana is seeking a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, its agentic platform for global trade. The role works with continuous streams of public and proprietary trade data—including shipments, customs filings, and trade documents—to build models and AI agents that support real-time risk, compliance, classification, and enforcement decisions for governments and major companies. The position may be remote or hybrid in the United States or UK, with the option to work from hubs in New York City, Boston, Washington, D.C., or London. The successful candidate will set technical direction for agentic systems, lead reliable and cost-efficient ML infrastructure from data ingestion through decision-making, and adapt models using analyst feedback, production outcomes, and agent traces. They will build evaluation, tracing, observability, and feedback systems; mentor senior engineers; raise engineering standards; help shape ML hiring and leveling; and work with engineering, data science, product, and customers to turn ambiguous problems into shipped capabilities. Altana describes its values as Trust, Resilience, Stewardship, and Courage, and states that it is an equal opportunity employer. The stated US annual base salary range is $205,000–$240,000. Competitive equity grants are included in the majority of full-time offers, and the company may offer a discretionary bonus or variable compensation plan depending on the role.
Requirements
- Bachelor’s or master’s degree in Computer Science, Machine Learning, or a related field, or equivalent experience.
- At least 8 years of experience building and shipping production ML systems, with a track record of product-oriented ML solutions, including recent LLM-powered product work.
- Deep expertise in PyTorch and the modern Python ML stack, including Hugging Face, scikit-learn, NumPy, and Pandas or Polars.
- Hands-on experience building production LLM applications, including RAG, tool/function calling, structured outputs, and agentic workflows.
- Experience shipping agentic systems in production and understanding their failure modes, including tool use, planning, state, recovery, and human-in-the-loop workflows.
- Experience post-training or adapting LLMs for production use cases, including SFT, DPO, RLHF/RLAIF, or reward modeling.
- Experience building evaluation suites, golden datasets, and LLM-as-judge pipelines.
- Sound judgment in choosing between frontier model APIs, fine-tuned open-weight models, and classical ML models while balancing cost, latency, and accuracy.
- Interest in applying state-of-the-art techniques to real-world problems, including hands-on work with data and domain details to achieve measurable outcomes.
- Commitment to engineering excellence, supportability, and knowledge-sharing.
- Strong written and verbal communication skills.
- Daily use of AI coding tools such as Cursor or Claude Code.
Nice To Haves
- Experience building multi-agent systems and agent tooling, such as LangGraph.
- Experience with hybrid retrieval, including vector search, re-rankers, and embedding fine-tuning.
- Experience with vision-language models for document understanding, such as invoices, bills of lading, and customs forms.
- Experience with multilingual NLP.
- Experience with LLMOps tools such as MLflow and Databricks Mosaic AI, and with guardrails.
- Experience with safety, guardrails, or AI auditability in regulated or high-stakes domains such as customs, legal, healthcare, or finance.
Responsibilities
- Set the technical direction, architecture, and roadmap for models and agentic systems that process incoming trade data and produce 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 global-trade scale.
- Use analyst feedback, production outcomes, and agent traces to fine-tune and post-train models, including SFT, DPO, and reinforcement learning with domain graders, when this measurably outperforms prompting.
- Build evaluation, tracing, observability, and feedback systems that guide releases and catch production regressions.
- Mentor senior engineers, raise the technical bar across Engineering, and help shape ML talent hiring and leveling.
- Collaborate with software engineers, data scientists, product managers, and customers to generate business ideas and turn ambiguous problems into shipped capabilities.
Benefits
- Competitive equity grants are included in the majority of full-time offers.
- A discretionary bonus or variable compensation plan, depending on the role.
- Flexible Time Off (FTO).
- Paid parental leave: 14 weeks for non-birthing, adoptive, and foster parents, and up to 26 weeks for birthing parents, paid at 100% of base salary.
- Medical, vision, and dental benefits with employer contributions.
- A 100% employer-paid high-deductible health plan for employees, plus an employer contribution to a Health Savings Account (HSA).
- Flexible Spending Account (FSA) option, including a Dependent Care FSA.
- Life, short- and long-term disability, and AD&D insurance at no cost to employees.
- Fidelity 401(k) retirement savings program.
- Commuter benefits for public transit or parking.
- Free premium Calm subscription.
- Pet insurance and a pet veterinary service and telehealth discount plan.
- Free access to a confidential Employee Assistance Program.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
