Confido
Applied AI/ML Engineer
About this role
Confido seeks a Senior Applied AI/ML Engineer to build the AI backbone of their CPG infrastructure platform. You'll own end-to-end AI problems spanning document understanding, demand forecasting, and agentic workflows, working with complex financial data from hundreds of enterprise sources.
What you'll do
- Build LLM systems that extract and validate data from complex, inconsistent financial documents
- Design and deploy agentic workflows with evaluation harnesses and guardrails for production reliability
- Develop demand forecasting models across thousands of time-series using classical and gradient-boosted approaches
- Engineer data pipelines for messy real-world data including outlier detection and entity reconciliation
- Own evaluation frameworks and monitoring strategies for probabilistic systems without single correct answers
- Drive end-to-end ML projects from research and prototyping through production deployment
What they're looking for
- LLM systems and agentic workflows
- Time-series forecasting and statistical modeling
- Data engineering and ETL for heterogeneous sources
- ML evaluation, monitoring, and metrics design
- Document understanding and information extraction
- Python and ML frameworks (scikit-learn, XGBoost, etc.)
- Production ML systems and deployment
- RAG and retrieval systems
Benefits
- Equity ownership in the company
- 100% fully paid health coverage through Aetna
- Top-tier dental and vision coverage
- 12 weeks paid parental leave and fertility support
- Unlimited PTO with regular 4-day weekends
- Paid relocation support and fully equipped workspace
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Confido
Confido builds an AI-powered financial infrastructure platform that streamlines financial operations and workflows for CPG brands. The company is hiring software engineers, forward-deployed engineers, analytics engineers, and GTM engineers to develop core product systems, customer integrations, data infrastructure, and go-to-market automation.
View all jobs at ConfidoLikely interview questions
- Walk us through a production ML system you built—what was the hardest part of moving from prototype to production, and how did you handle evaluation?
- Tell us about your experience with time-series forecasting. Which models have you used, and how did you handle intermittent or sparse data?