2 days ago
Base Salary
$205k - $240k/yr
Responsibilities
- Define the technical direction, architecture, and roadmap for agentic systems processing global trade data in real time.
- Lead reliable and cost-efficient pipelines spanning ingestion, document understanding, entity resolution, inference, and model routing.
- Fine-tune and post-train models using SFT, DPO, and reinforcement learning where this improves production outcomes.
- Build evaluation, tracing, observability, and feedback systems that detect regressions and guide releases.
- Mentor senior engineers, raise engineering standards, and help shape ML hiring and leveling.
- Collaborate with engineers, data scientists, product managers, and customers to turn ambiguous opportunities into shipped capabilities.
Requirements
- BS/MS in Computer Science, Machine Learning, or a related field, or equivalent experience.
- 8+ years building and shipping production ML systems, including recent LLM-powered product experience.
- Deep expertise in PyTorch and the modern Python ML stack, including Hugging Face, scikit-learn, NumPy, and Pandas/Polars.
- Hands-on experience building production LLM applications using RAG, tool or function calling, structured outputs, and agentic workflows.
- Experience shipping agentic systems and handling tool use, planning, state, recovery, and human-in-the-loop workflows.
- Experience with LLM post-training or adaptation, including SFT, DPO, RLHF/RLAIF, or reward modeling.
- Experience building evaluation suites, golden datasets, and LLM-as-judge pipelines.
- Ability to choose among frontier model APIs, fine-tuned open-weight models, and classical ML models while balancing cost, latency, and accuracy.
- Strong written and verbal communication, engineering judgment, supportability, and knowledge-sharing skills.
- Heavy daily use of AI coding tools such as Cursor and Claude Code.
- Preferred experience with multi-agent systems, LangGraph, hybrid retrieval, vector search, re-rankers, embedding fine-tuning, vision-language models, multilingual NLP, MLflow, Databricks Mosaic AI, guardrails, safety, or auditability in regulated or high-stakes domains.
Benefits
- Annual base salary of $205,000-$240,000 USD, with competitive equity grants included in the majority of full-time offers and either a discretionary bonus or variable compensation plan depending on the role.
- Flexible Time Off policy.
- Paid parental leave of 14 weeks for non-birthing, adoptive, and foster parents and up to 26 weeks for birthing parents.
- Medical, vision, dental, high-deductible health plan, HSA contribution, and FSA option.
- Employer-provided life, short-term and long-term disability, and AD&D insurance.
- Fidelity 401(k) savings program.
- Pre-tax commuter benefits for public transit or parking.
- Free premium Calm subscription, pet insurance or veterinary telehealth discounts, Employee Assistance Program, and Dependent Care FSA.
- Based in Brooklyn, New York, with hybrid work flexibility; candidates must be within commuting distance of the Williamsburg office.
Tech Stack
Apache SparkAWSAzureDatabricksDockerGitKubernetesMLflowNumPyPandasPostgreSQLPythonPyTorchscikit-learn
Categories
About Altana
Altana builds an AI-powered network and software platform for global trade visibility and value chain management used by enterprises and governments. Its SaaS products connect importers, suppliers, logistics providers, and customs agencies to share data for compliance, classification, and supply chain risk. Founded in 2018 and headquartered in New York, Altana serves customers such as Maersk, Boston Scientific, L.L.Bean, U.S. Customs and Border Protection, and the UK Department for Business and Trade.