Senior Backend Engineer, Data & Algorithms

About Us

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About the Company

Most companies still plan their supply chains, meaning quantities, mix, allocations, and timing, in spreadsheets or legacy software. The spreadsheets break as soon as the business moves quickly. The legacy suites take a year to implement and frequently lose to the spreadsheets anyway.

is rebuilding the brain behind the American supply chain. By tackling operations optimization from first principles with an AI-native stack, they deliver unprecedented results at breakneck speed for their customers. Customers include DoorDash, HelloFresh, Archer Meat Snacks, Starface World, LMNT, OOFOS, Cora, and Chomps.

The founders lived this problem before they built the solution. Michael Rossiter and Neal Suidan built Tesla's end-to-end supply chain orchestration system from scratch during the Model 3 ramp, after watching a company that size run on spreadsheets that could not keep up with how fast the business moved. Revenue has grown roughly 10x this year, and they just announced a $12.5M Series A, which is what's funding this round of hiring.

About the Role

This is a backend seat on the core platform team, which builds the product itself rather than deploying it with customers. The work splits roughly into two halves: the data layer underneath, and the optimization logic that runs on top of it.

Strip away the domain and it's constrained optimization over messy real-world data. Atomic runs over a million SKU by location by supplier combinations every day, and customers run 100+ scenarios daily against them. Every number the system produces traces back to the logic that generated it at the unit level, because a planner reviews and approves it before anything moves. That traceability requirement rules out a lot of easy answers.

The data half. Every customer's operation gets normalized into a common data model. That means designing a schema that holds up across genuinely different businesses, building the pipelines that get messy source data into it, and keeping the whole thing fast and clean as volume grows. Polars and Iceberg are used heavily.

The algorithms half. Abstracting how a given supply chain actually works, then writing the models that balance supply against demand, minimize cost, and satisfy the constraints simultaneously.

Some of what that looks like:

• Production planning. What gets produced, in what quantity, when, by which facility, and from what materials, against bills of material, minimum order quantities, run constraints, automation rules, and co-manufacturer capabilities
• Inventory allocation. Weekly positioning across a fulfillment network by SKU, by 3PL, by week, respecting channel splits, transit times, truck thresholds, reservation rules, and eligibility rules between specific SKUs and specific warehouses
• Purchasing. Generating purchase orders against supplier constraints, lead times, contract minimums, and cost, including grouping minimum order quantities across different SKUs that share components
• Scenario response. A tariff lands, a lead time slips, marketing pulls a launch forward, a new co-manufacturer comes online. The system re-plans against the change, lets a planner compare any version to any other, and keeps a full audit trail of every override

The engineering team is three people today and is hiring several more. There are no silos and no plans for any. You take a project start to finish rather than owning a slice of someone else's architecture, and you won't be locked to one half of this role. Data engineering one month, optimization modeling the next, wherever the priority sits. The team is staying deliberately lean so the stack stays clean and coherent.

You do not need a supply chain background. It helps, and it will make you productive faster, but the bar is engineering quality first.

Need-to-Have

• Strong Python and SQL, with real comfort working with tabular data at scale
• Experience designing data models and schemas over messy, inconsistent source data
• Experience writing logic that optimizes against real constraints. This can come from supply chain, logistics, manufacturing, finance, operations research, or anywhere else the problem was genuinely constrained
• Evidence you can reason from first principles and think across a whole application rather than just your piece of it
• A track record of owning projects end to end, including the parts you had to figure out yourself
• Genuine appetite for a small, fast-moving team where priorities shift and scope is wide

Nice-to-Have

• Polars and Iceberg. Experience with Vertica, DynamoDB, MongoDB, or similar also translates
• An operations research background, particularly applied optimization work
• Supply chain, logistics, or manufacturing domain exposure
• AWS depth, especially if you've scaled heavy batch compute at a small company where you owned the whole thing
• Experience with forecasting, simulation, or scenario modeling