What you’ll do
- Build and deploy AI/ML models across new asset categories (comics, cards, watches, jewelry, fine art, etc.).
- Balance from-scratch builds with leveraging foundation models and off-the-shelf tools — using the right approach for speed, accuracy, and efficiency.
- Prototype quickly, test with real customer data, and push to production.
- Design workflows that minimize wasted effort while compounding into long-term defensible data + model infrastructure.
What we’re looking for
- Strong background in applied ML (vision, text, multimodal, embeddings, fine-tuning).
- Pragmatic decision-maker: knows when to train, when to adapt, and when to just glue things together.
- Builder mindset — bias toward action, rapid iteration, and comfort with messy or sparse data.
- Excited to join a post-seed startup as a founding team member with broad project ownership.
What we do
At Vardera, we build deep learning models that appraise, grade, and authenticate physical goods from a single image. Our models transform subjective, expert-driven workflows into structured, machine-readable intelligence.
Marketplaces, retailers, and brands use Vardera to reduce fraud, accelerate intake, improve listing quality, and unlock pricing transparency across coins, trading cards, comics, toys, cosmetics, and other high-value goods.
Skills Required
- Strong background in applied ML
- Experience with vision, text, multimodal, embeddings
- Comfort with messy or sparse data
Vardera Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Vardera and has not been reviewed or approved by Vardera.
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Equity Value & Accessibility — Public listings highlight “meaningful early‑stage equity” for technical roles and mention relocation assistance. This suggests equity is a central component of total compensation at the current stage.
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Flexible Benefits — Materials describe a flexible time‑off policy and flexible work hours, with some roles offering hybrid/remote options across Boston and the North US. This points to adaptable policies around time off and scheduling, even within an in‑office‑leaning culture.
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Wellbeing & Lifestyle Benefits — Company‑sponsored outings and happy hours, free snacks/drinks, some meals, and an all‑company offsite are called out. These perks indicate attention to team cohesion and day‑to‑day amenities.
Vardera Insights
What We Do
Vardera builds the AI infrastructure layer for physical asset valuation. Our deep learning models identify, appraise, and authenticate high-value physical goods from a single image. We compress what used to require years of specialized human expertise into a sub-second API call — turning a subjective, offline workflow into structured, machine-readable intelligence that any platform can consume programmatically. Enterprises like eBay use Vardera to power accurate identification and pricing across millions of items. We're active across coins, trading cards, comics, toys, and jewelry, with signed contracts and proofs of concept already in production. Our reference dataset — built across 200 million+ items — is the core of our competitive moat, and it compounds with every transaction we process. The problem we're solving is foundational: the physical goods economy has never had a reliable, scalable valuation layer. Marketplaces guess. Insurers estimate. Retailers absorb shrink. We fix that. Think of what Stripe did for payments, or what Plaid did for financial data. We're building that infrastructure for the $8+ trillion in hard assets changing hands as industries built on decades of offline expertise go digital for the first time. We're a team of nine, based in Boston, and backed by Garuda Ventures and Rackhouse Ventures. We're pre-Series A, moving fast, and hiring across engineering, AI research, and operations.
Why Work With Us
Your work ships into production immediately — we have live enterprise contracts and a 200M+ item dataset that no competitor can replicate. You'll solve genuinely hard computer vision problems at a company small enough that your decisions shape the product, backed by investors who are pushing us to move fast.
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