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Applied AI/ML Engineer @ Confido

USOnsiteFull-time
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About this role

The brands on your kitchen shelf are run by people doing extraordinary work that nobody sees.

We're building the platform they run on, and a brand they'll be proud to be part of.

Confido is the AI operating system that runs cash application, deductions, trade and planning in one place for CPG brands. That means Finance, Sales, and Ops finally work on the same number.

We’re trusted by 250+ brands managing $30B+ in revenue, including Daisy, OLIPOP, Simple Mills, Idahoan, and more. We've achieved best-in-class growth and recently raised a $55M Series B led by Insight Partners to accelerate our momentum.

If you want your work on shelves everywhere — and outsized ownership while you build — we'd love to meet you.

The RoleBuild the core AI behind Confido — and get your hands on an unusually rich, messy dataset: hundreds of thousands of documents across dozens of sources and hundreds of layouts and contexts, much of it beyond what any system reads well today.

As an Applied AI / ML Engineer, you'll own AI problems end to end — research, prototyping, production, and the evaluation that keeps probabilistic systems reliable — across document understanding, forecasting, and agentic workflows.

Location: New York, NY (Relocation supported)

What you'll doBuild LLM systems that turn complex, inconsistent financial documents into clean, typed, validated data — the backbone the rest of the platform runs on

Design agentic workflows that retrieve and reason over fragmented enterprise systems, with the eval harnesses and guardrails to run them in production

Build and improve demand forecasting across thousands of intermittent series — classical time-series and gradient-boosted models, with rolling-origin backtesting and best-fit selection

Wrangle messy, real-world data: baselining, outlier and anomaly detection, entity reconciliation, and ingestion across heterogeneous sources

Own how we measure success — eval datasets, benchmarks, and monitoring for systems with no single "correct" answer

What we're looking for Required

3+ years of applied AI / ML, with systems you've taken to production (not just prototypes)

Depth in at least one, and working fluency across several, of: time-series / forecasting and statistical modeling; LLM and agentic systems; large-scale messy-data engineering

You build evaluation and monitoring as a matter of course — and know which metric to trust, and how to avoid leakage and train/serve skew

Strong product sense: you turn AI capability into real outcomes, and know when a simpler approach wins

Masters degree in STEM

Nice to have

Demand forecasting / time-series (Prophet, ARIMA-family, gradient-boosted)

Agentic workflows, RAG, or retrieval systems in production

Document understanding / information extraction from unstructured sources

Startup experience or comfort in fast-moving environments

🌴 Perks + BenefitsEquity — own a meaningful piece of the company you’re helping build

Fully paid health coverage through Aetna — we cover 100% of employee premiums

Top-tier dental and vision coverage through Guardian

Carrot Fertility Pro — comprehensive fertility and family-forming support

12 weeks of paid parental leave

Unlimited PTO — plus regular 4-day holiday weekends we actually take

401(k) through Vestwell

Paid relocation support — we’ll help you make the move to NYC

Fully equipped workspace from day one — laptop, monitor, keyboard, and a $200 stipend to personalize your setup

Team perks — catered Friday lunches, team dinners, and unlimited coffee + snacks featuring products from the brands we work with

Confido provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Skills

ML/AIProduct & Technology

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