What staff augmentation means when it is a pod
Most engineering staff augmentation places a person against a job description and bills by the hour. You get a resume that roughly matches, a contractor on a clock, and a manager on your side who now owns every architecture call and code review the contractor does not do. We do not sell that. We do not sell hourly staff augmentation, and we do not bill for seats on people who are not building.
A pod is the unit instead: a named lead who owns scope and architecture, a bench of senior engineers mixed to fit the actual work, and QA that runs against the same CI gate as any other pull request. It is priced as a flat monthly number, not a rate card, and it is billed as one flat number rather than a timesheet. Builder is $7,500 a month, Growth is $10,000 a month, and Enterprise is scoped in a meeting for three or more parallel tracks.
That difference, capacity priced by the pod instead of hours or headcount billed by the seat, is the whole reason this page exists separately from a generic staffing pitch. Everything else on this page describes what that actually looks like once engineers are inside your codebase.
Who is on the bench
A pod is not a single title repeated. It is mixed from whichever of these the build actually needs, led by a pod lead who owns the roadmap and reviews every pull request before it merges. Nobody sells these roles separately or bills them at different hourly rates: the bench is sized to the plan, and which types staff a given pod is a scoping decision, not a menu with add-on prices.
- Forward-deployed AI engineer: sits with your team, learns how the work actually gets done, then automates the parts nobody enjoys
- AI automation engineer: wires models into the tools you already run so a workflow finishes without a person babysitting it
- AI / LLM engineer: agents, retrieval and evaluations, plus the harness that proves the answer is correct
- ML engineer: training, fine-tuning and scoring models, and the pipelines that keep them fed and monitored in production
- Data engineer: ingestion, warehouses and the joins nobody wants to own, because clean inputs are the difference between a model that works and one that guesses
- Full-stack engineer: ships the whole slice, schema, API and the screen the user touches
- Frontend engineer: the interface your customers judge you on, accessible, fast and built to be extended
- Backend engineer: typed services, background jobs and integrations that hold up under real traffic
- DevOps / platform engineer: your cloud, your pipelines, your monitoring, so deploys become boring on purpose
- QA / test engineer: browser and unit coverage on the paths that cost you money when they break
- Mobile engineer: iOS and Android from one codebase where that fits, native where it does not
- Pod lead: owns scope, architecture and the weekly delivery, and is your single point of contact
How the pod plugs into your team
The pod works in your repository from day one, not a private branch handed over at a milestone. Every change lands as a pull request against your codebase, a named engineer who owns that piece of the system reviews it, interfaces use typed contracts so a schema mismatch fails at compile time, and tests run in CI before anything merges. Schema changes ship as reviewed, versioned migrations, not a manual change run against a live database.
Standups happen in whatever you already run, Slack, Teams or an email thread, so there is no separate tool to adopt. The pod ships working code every week, not a status deck, and you steer priorities in that standup rather than through a change-order process. Growth and Enterprise plans add a bi-weekly or executive-level strategy call on top of the weekly ship for longer-range planning.
The team runs out of Orange County, California and Prishtina, Kosovo on Central European time, so a US-morning standup is usually reviewing work that was tested overnight rather than work that has not started yet. Code, data and infrastructure are yours from week one with no license-back, so if the engagement ends, whatever shipped keeps running exactly as it did the day before.
What it costs versus hiring or a marketplace
A senior AI or ML engineer hired independently is an estimated $250,000 or more a year fully loaded once salary, benefits and recruiting are counted, and it typically takes 3 to 6 months to close that search. A Builder Pod annualizes to $90,000 a year for a pod lead plus a two-engineer bench, and a Growth Pod to $120,000 a year for a lead plus a three-engineer bench across two concurrent tracks, both starting inside 5 business days.
A marketplace match, the kind a freelancer platform like Toptal provides, books a single contractor fast, often inside 48 hours, at an hourly or negotiated rate with no bench behind that one person. That is a fair tool for a narrow, well-specified role with no ongoing relationship, and we would say so before pitching a pod against it. What a pod adds instead is a named lead who owns architecture for the life of the engagement and a bench that covers if one person is out, at a fixed monthly number instead of a metered clock.
Every figure above besides our own published pod prices is an estimate drawn from public wage data and vendor claims, not a guarantee for your specific hire or contract. Treat any number a staffing vendor quotes you directly the same way: verify it against your own scope before comparing it to a flat monthly price.