Senior Staff Machine Learning Engineer
About The Position
Altana is hiring a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, its agentic platform for global trade. The role uses 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 large companies. The engineer will set technical direction and architecture for agentic systems, lead reliable and cost-efficient data and ML pipelines, adapt models using analyst feedback and production outcomes, and build evaluation, tracing, and feedback systems. The role also includes mentoring senior engineers, raising technical standards across Engineering, shaping hiring and leveling, and collaborating with engineering, data science, product, and customers to turn ambiguous problems into shipped capabilities. The position is remote or hybrid and based in the United States or UK, with the option to work from hubs in New York City, Boston, Washington D.C., or London. The US salary range is $250,000–$295,000. Altana states that most full-time offers include competitive equity grants and that the role may receive 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.
- 10+ years building and shipping production ML systems, with a proven record of delivering product-oriented ML solutions, including recent work on LLM-powered products.
- 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 using retrieval-augmented generation, tool or 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 a production use case, including SFT, DPO, RLHF/RLAIF, or reward modeling.
- Experience building rigorous evaluation systems, including 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.
- Senior Staff-level technical leadership, including setting architecture direction across teams, mentoring engineers, and turning ambiguous problems into roadmaps.
- Passion for applying state-of-the-art techniques to real-world problems, with willingness to work directly 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 auditability for AI 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 trade data and produce real-time risk, classification, and compliance decisions.
- Lead the design of reliable, cost-efficient systems for data ingestion, document understanding, entity resolution, inference, and model routing 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 improves on prompting.
- Build evaluation, tracing, and feedback systems that determine what ships and catch production regressions.
- Mentor senior engineers, raise the technical bar across Engineering, and help shape how ML talent is hired and leveled.
- Collaborate with software engineers, data scientists, product managers, and customers to generate business ideas and turn ambiguous problems into shipped capabilities.
Benefits
- Flexible Time Off policy.
- 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.
- High-deductible health plan fully paid by the employer for employees, plus an employer contribution to a Health Savings Account.
- Flexible Spending Account option.
- Life, short- and long-term disability, and AD&D insurance coverage at no cost to employees.
- Fidelity 401(k) retirement savings program.
- Commuter benefits using pre-tax funds for public transit or parking.
- Free premium Calm subscription.
- Pet insurance and a pet veterinary service and telehealth discount plan.
- Employee Assistance Program with free confidential personal support.
- Dependent Care FSA for pre-tax childcare expense savings.
- Competitive equity grants are included in the majority of full-time offers.
- A discretionary bonus or variable compensation plan, depending on the role.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
