About Wholesail ⛵️

Wholesale trade, a $55T global market, still operates like the world before the internet and modern payment networks: collection processes are manual and run on paper, short-term credit risk is assumed by the vendor, and businesses rely heavily on legacy ERP systems that don't talk to each other. The result is large, measurable drag — we estimate that vendors spend over $500B a year globally across industries on an inefficient mix of processing, bad debt, lending, credit insurance, software, and labor costs to get paid.

Wholesail is building a financial network from the ground up that connects the systems of vendors and buyers involved in wholesale trade to enable streamlined payment and the transfer of risk to third parties. This will allow vendors to offload risk and eliminate tens of billions of waste — while giving creditworthy buyers better terms and third-party capital, unlocking hundreds of billions (ultimately trillions) in additional sales. The primitives of this network scale across industries and geographies: a universal approach to ERP integrations, modern payment rails, and a live trade-credit bureau to underwrite risk — which we're calling Lighthouse.

The Opportunity: Risk & Capital Products 🚢

Credit is the load-bearing beam of our network. Every time a vendor ships goods before getting paid, someone is taking a risk — today it's the vendor, tomorrow it should be a third party at a fair price. Getting that transfer right is what unlocks the next order of magnitude of sales across the wholesale economy, and the only way to get it right is to underwrite buyers more accurately than anyone else in the industry. Listen to the Visa episode of the Acquired podcast to learn how credit card networks did this for retail trade. 

We think we're uniquely positioned to do this. Through Lighthouse, we're building a live, reciprocal trade-credit bureau: vendors on our network contribute real-time payment behavior on a long tail of SMB buyers that no traditional bureau sees. That data — combined with the bank, ERP, and transaction signals already flowing through Wholesail — is a modeling dataset that doesn't exist anywhere else. The first MLE on this team gets to decide what we build with it.

The problems are real and the stakes are significant. Our models directly shape the terms buyers are offered and the losses Wholesail and our capital partners absorb. There's no established playbook here and no legacy stack to inherit — you'll be setting the direction for how we do modeling, data engineering, and production ML at Wholesail for years to come.

The Role ⚒️

As the founding MLE on the Risk Engineering & Capital Products team, you'll own credit risk modeling end-to-end — from problem framing and data pipeline design, through model development and validation, through production serving and monitoring. You'll work directly with the hiring manager, our engineering and product leadership, and our hands-on advisor Dan Massoni (former Chief Commercial Credit Officer of American Express) to decide what to build, explore features to model and how you’ll scale our approach risk scoring. .

Expect your work to span:

• Modeling. Design, build, and validate credit risk models (PD, LGD, EAD, fraud, exposure sizing, pricing) against our proprietary reciprocal-bureau data and external signals. Choose the right tool for the job — gradient-boosted trees, deep models, LLMs, or classical statistical models — and defend the choice.
• Data engineering. Build the pipelines, feature logic, and training datasets that your models (and future models) run on. At this stage, the MLE owns the full data path; over time we'll grow a team around you.
• Productionization. Ship models into production systems where they make real decisions on real dollars. Own deployment, monitoring, drift detection, and iteration.
• Partnering cross-functionally. Work closely with product, engineering, capital markets, and external lending and insurance partners. Explain your models to people who care about the outcomes but not the math, and translate their constraints back into modeling decisions.
• Shaping the team. Help define the hiring bar and interview process for the data scientists and MLEs who will join after you. You'll have a strong voice in what this team becomes.

Qualifications

• 5+ years of experience building models for production use cases.
• Deep modeling skill: strong command of supervised learning on tabular data, including feature engineering, model selection, hyperparameter tuning, calibration, and rigorous offline and online evaluation.
• Strong data engineering skills — comfortable owning ETL and feature pipelines end-to-end against real, messy production data (SQL and a modern data-processing stack).
• Proficiency in Python and the modern ML ecosystem (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM, Jupyter, etc.).
• Solid statistical reasoning — you can spot leakage, selection bias, label noise, and spurious correlations, and you know why your offline metric doesn't always predict online performance.
• Track record of owning models end-to-end: not just prototyping in a notebook, but taking something from a problem statement to a production system that makes decisions.
• Excellent written and spoken English communication skills; ability to explain model behavior, tradeoffs, and limitations to non-ML audiences (product, ops, capital partners, leadership).
• The spirit of a team player who believes in fostering a healthy and supportive work environment.

Bonus Qualifications

• Direct experience in credit risk modeling — PD/LGD/EAD models, scorecards, underwriting models, exposure management, or collections modeling — particularly for SMB or subprime segments.
• Experience in fintech, lending, payments, fraud, or insurance, or with regulated modeling environments (model risk management, adverse action, fair lending).
• Experience building or operating ML platform components — feature stores, model registries, training orchestration, online serving, monitoring/drift detection.
• Backend engineering depth: comfortable writing production services, designing APIs, and reasoning about distributed systems that consume your models.
• Strong data analysis background — fluency in exploratory analysis, experimentation, and translating data into product and business decisions.
• Experience with LLMs and agentic systems applied to operational problems (document understanding, KYB, entity resolution, agent tooling).
• Experience as the first or founding ML hire at a company, or early on a team that had to build ML infrastructure from scratch.
• Published research, Kaggle placements, or prominent OSS contributions in the ML or data ecosystem.
• BA or BS in Computer Science, Statistics, Mathematics, a related technical field, or equivalent practical experience.
• Advanced degree (MS or PhD) in a quantitative field.

Equal Employment Opportunity

Wholesail is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, national origin, sex, gender identity or expression, sexual orientation, age, marital status, veteran status, disability status, pregnancy, or any other characteristic protected by federal, state, or local law. Employment decisions at Wholesail are based on qualifications, merit, and business needs. We encourage applicants from all backgrounds to apply.

⛵️ About Wholesail

Wholesail is an early stage startup that builds software to connect wholesalers and their buyers by automating and digitizing invoicing and payment in the $5 trillion wholesale trade industry. The company’s first product is an electronic invoicing and payment portal for wholesale sellers, which allows them to get paid faster and decrease losses. For buyers, Wholesail automates the accounts payable & expense management process to save them time and money. Wholesail’s initial industry focus is to build a network effect driven payment business in the highly fragmented $1 trillion US food distribution market, in which invoicing and payment is nearly entirely paper-driven today. 

Wholesail was founded by a team of engineers, product managers, and designers whose prior startups were acquired by OpenTable, and the company is well funded with backing from a half dozen VCs and 20+ angel investors including early executives and team members from Stripe, Square, Dropbox, Yelp, OpenTable and Everlane.

Are you fascinated by how people use products, actively seek to understand the “why” behind product decisions, and approach product building as a craft? Are you passionate about working with other experienced engineers who have a passion for their craft? We'd love to have you join our voyage as a founding member of our product engineering team.  

🛠 About the Role

As an early engineer, you’ll build new features and internal APIs to create value for the suppliers and buyers on our network. Your work will skew towards backend development, but you'll end up working up and down the stack and you’ll spend a lot of time with our sales and support team and our customers to figure out the details of the products you craft. Our small product team wears many hats, so you'll get significant exposure to many areas adjacent to engineering: design, marketing, customer success, sales, and all of the functions of an early stage startup.

As a product oriented member of our engineering team, you’ll couple strong technical execution with an active voice in shaping the product through contribution in product conversations and identifying product/technical tradeoffs. You’ll participate in different styles of execution based on the stage of product development – from iterating quickly on an experiment with intentionally scrappy code to building sustainable systems that serve as a foundation for years to come.

Most significantly, you'll be building the company with us. We are early on our voyage and there are vast uncharted seas ahead. We want to work with fellow Wholesailers who care deeply about the process of building an enduring company: from what values we embrace, how we build our teams, decisions about how we grow our business, and everything in between.

🚀  You’d be great a great fit for our voyage if you… 

• Are naturally curious about and have a keen interest in understanding the "why?" by digging into the nuts-and-bolts of how things work as a first principles thinker.
• Think deeply about products – from building empathy for users, to understanding how a product works, how it fits into a complex system, and how it creates business value.
• Obsess about the details of your craft whether it’s designing a new abstraction layer, building an API for a colleague on the frontend team or shipping a pixel perfect feature.
• Quickly build intuition in new domains and are able to articulate product/engineering tradeoffs early on while also pragmatically handling (and minimizing work on) edge cases.
• Estimate and prioritize when faced with different proposed solutions, to help guide the team towards building the most impactful products.
• Strive to quickly deliver value through rapid product validation cycles and have a keen ability to identify quick wins that create value for customers.
• Use data to guide your understanding of user behavior and inform the decisions you make to maximize building value for customers.
• Strive for end-to-end ownership of your work, are proactive with sharing product ideas/opinions, and seek early feedback from other members of your team.
• Build empathy and relationships with strong listening, written, and verbal communication skills – particularly with non-engineering folks such as designers, PMs, support and sales.

🤝 Engineering Culture & Environment

At Wholesail, we started as a team of senior engineers who are passionate about using technology to build products and tools to improve the lives of buyers and sellers in the wholesale industry. Many of us have a background working on technologies used in the relevant food and payments industries, and together with our cross-functional partners, we strive to learn about the problems that our customers in the wholesale industry are facing, so we can work hard to provide solutions for our buyers and sellers.  

Wholesail provides products that help our customers make payments and process orders, which are essential to our customers' business, and therefore we have a strong focus on security, reliability and system performance. We emphasize good engineering practices, and adopt test-driven development and continuous deployment to iterate quickly and confidently. We identify and eliminate or mitigate the impact of single points of failure in our systems. We also genuinely take pride in the craftsmanship of the code we produce, and we love to work with colleagues whose work we respect. Intermixed with all of this is a sense of good humor, and an earnest enjoyment in working together.

Wholesail’s tech stack starts with Java services running in Kubernetes (GKE on Google Cloud Platform), with PostgreSQL as the database. The frontend is a React-Native single page application that is served statically.  The Wholesail platform integrates with third party services and APIs, including Plaid, Stripe, Modern Treasury and QuickBooks, among other systems. Given the financial and accounting nature of the product, the company has invested in extensive testing infrastructure. In addition to more traditional automated testing, we built orchestration that provisions independent copies of the entire service architecture with candidate releases to test the system, end-to-end, before rolling out new changes.