Staff ML / AI Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Staff ML / AI Engineer NYC Office | OnSite Confido is the AI infrastructure powering CPG brands from deduction to production plan. We unify cash application, deductions, disputes, trade promotion management, forecasting, demand planning, and analytics in one integrated platform. The result: measurable time savings, smarter top- and bottom-line decisions, and the speed to scale. Confido is trusted by 200+ brands managing $20B+ in revenue, including OLIPOP, Simple Mills, Dr. Squatch, Tropicana, and more. We've achieved best-in-class growth and recently raised a $15M Series A led by Footwork Ventures and Y Combinator to accelerate our momentum. As a Staff AI / ML Engineer, you will define and build the core AI systems behind the platform-from LLM-powered document understanding to predictive models that help brands make better decisions. This is a foundational role working directly with the CEO and CTO to shape our long-term AI strategy and architecture. Location: New York, NY (Relocation supported) WHAT YOU'LL DO - Own the full AI/ML lifecycle: research → prototyping → production deployment → monitoring - Design and build systems that extract structured data from complex financial documents - Develop agentic systems that retrieve and reason over data across fragmented enterprise systems - Build predictive models to forecast brand sales and financial performance - Detect anomalies in retail and sales data - Develop recommendation systems to help brands optimize retailer promotions - Partner with product and engineering to integrate AI capabilities into the core platform EXAMPLE PROBLEMS - AI-powered document ingestion for invoices, deductions, and

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