Senior Staff Machine Learning Engineer
J
JobgetherMachine learning, global trade
Based in the United States; Remote or hybrid work options in the United StatesFull-TimeStaff
SalaryBase salary: $250,000–$295,000 USD per year. Equity: Competitive equity grants are included in the majority of full-time offers. Performance compensation: A discretionary bonus or variable compensation plan may be available, depending on the role.
Apply NowOpens the employer's application page
Job Details
- Experience
- At least 10 years of experience building and deploying machine learning systems in production
- Required Skills
- PythonPyTorchscikit-learn
Requirements
- Have a bachelor’s or master’s degree in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
- Have at least 10 years of experience building and deploying machine learning systems in production.
- Bring a track record of product-oriented ML solutions and recent experience with LLM-powered products.
- Have deep expertise in PyTorch and the modern Python machine learning ecosystem, including Hugging Face, scikit-learn, NumPy, and Pandas or Polars.
- Have hands-on experience with production LLM applications, including RAG, tool and function calling, structured outputs, and agentic workflows.
- Have production experience with agentic systems, including planning, tool use, state management, recovery strategies, and human-in-the-loop processes.
- Have practical experience adapting and post-training language models using techniques such as SFT, DPO, RLHF, RLAIF, or reward modeling.
- Have expertise in model evaluation, including evaluation suites, golden datasets, and LLM-as-a-judge pipelines.
- Have Senior Staff-level experience setting architecture direction across teams, mentoring engineers, and translating ambiguous problems into technical roadmaps.
- Use AI coding assistants such as Cursor or Claude Code regularly as part of the development workflow.
- Preferred: experience with LangGraph, hybrid retrieval, vision-language models, multilingual NLP, LLMOps, AI guardrails, or safe and auditable systems for regulated or high-stakes domains.
Responsibilities
- Define the technical vision, architecture, and roadmap for agentic AI systems processing global trade data.
- Lead the design and implementation of scalable data pipelines for ingestion, document understanding, entity resolution, inference, and routing.
- Develop and deploy production machine learning systems that turn structured and unstructured trade data into actionable insights.
- Adapt and improve language models using SFT, DPO, reinforcement learning, and domain-specific evaluation where beneficial.
- Build evaluation frameworks, golden datasets, tracing systems, and feedback loops to measure model quality and detect regressions.
- Design observability and monitoring capabilities to identify agent failure modes and support reliable workflows.
- Select between model APIs, fine-tuned open-weight models, and traditional ML approaches based on operational and performance considerations.
- Establish architectural standards, mentor senior engineers, and contribute to hiring and career development.
- Collaborate with engineering, data science, product, and customer teams to translate business challenges into production capabilities.
- Explore AI techniques and tools for model capabilities, agent orchestration, document processing, and decision-making.
View Full Description & ApplyYou'll be redirected to the employer's site
