Note: The job is a remote job and is open to candidates in USA. Atomic is rebuilding supply chain planning with an AI-native stack for fast-growing businesses. The Senior Software Engineer will work on the core platform, designing a scalable common data model and developing optimization logic for production planning, inventory allocation, purchasing, and scenario response. The role involves owning projects end to end across data engineering, backend modeling, and application development.
Responsibilities
- Production planning. Deciding 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. Channel splits, transit times, truck thresholds, reservation rules, and eligibility rules between specific SKUs and specific warehouses, all encoded as logic rather than living in one planner's head
- 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 first is the common data model that every customer's operation gets normalized into, which means designing a schema that holds up across genuinely different businesses and keeping it fast and clean as volume grows
- The second is the optimization logic that runs on top, abstracting how a given supply chain actually works and then writing the models that balance supply against demand, minimize cost, and satisfy the constraints
Skills
- Strong Python and working fluency with SQL. The requirement underneath both is real comfort working with tabular data at scale
- 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
- Ability to build in a repeatable, scalable way rather than shipping something that works once
- Genuine appetite for a small, fast-moving team where priorities shift and scope is wide
- Experience writing algorithms that optimize against real constraints. This can come from supply chain, logistics, manufacturing, finance, operations research, or anywhere else the problem was genuinely constrained
- Supply chain, logistics, or manufacturing domain exposure
- Polars and Iceberg, both of which are used heavily. Experience with Vertica, DynamoDB, MongoDB, or similar also translates
- An operations research background, particularly applied optimization work
- AWS depth, especially if you've scaled heavy batch compute at a small company where you owned the whole thing
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