Python developers vetted per stack, not just the syntax.
Python placements cover wildly different work shapes — Django web backends, FastAPI service tiers, ML pipelines (NumPy/Pandas/PyTorch/scikit-learn), data engineering (Airflow/dbt/Spark), automation scripting. sourceBOLD’s 4-gate technical screen calibrates per-stack: the rubric for a backend Django role differs from the rubric for an ML-pipeline role. Same gauntlet, different question batteries.
Same gauntlet, calibrated per Python stack.
60-minute structured exercise calibrated against the target stack. A Django/FastAPI backend candidate gets exercises on request handling, ORM patterns, serialization, async vs. sync boundaries. A data-engineering candidate gets exercises on schema design, throughput, idempotency. Two human reviews, blind to candidate identity, must agree to pass.
Architecture round, also stack-dependent. Backend: partitioning, caching, idempotency, async patterns. Data engineering: throughput, fault tolerance, schema evolution, late-arriving data. ML: training pipelines, evaluation discipline, drift detection. Same set of questions for each stack; calibrated against engineers already placed.
Two-hour real-codebase session against a benchmark repo in the candidate’s stack. We watch how they handle a failing test, how they reach for documentation, how they reason about edge cases under load. Stack-specific judgment is what G4 catches that résumés don’t.
Full funnel walkthrough at /guides/4-gate-vetting. The per-stack rubric calibration is what makes the 3.9% acceptance rate meaningful across different Python specializations.
Week 1 — shipping by Friday.
- DAY 1Repo access, local environment running, dependencies installed, SOW kickoff doc reviewed. Standup attendance starts on day one.
- DAY 2–4Picks up a contained bugfix or self-scoped backend ticket — typically something that exercises the test suite and the deployment path so the developer gets end-to-end context fast. Your tech lead reviews the PR.
- DAY 5First feature ship — typically a backend endpoint, a data-model addition, or a pipeline step depending on the stack. Touches the test suite so the next week’s work has shared context.
- WEEK 2+Real feature work. Integrates into the standup rotation, code-review rotation, on-call (if applicable). Treated like a senior member of the team — because that’s what they are.
The contracting layer sits underneath all of this. From your team’s day-to-day, the developer is a senior Python contractor in Slack and on PRs. The cross-border tax shape, the bi-weekly invoice cycle, the Wise dispatch in the developer’s local currency — those are sourceBOLD’s operational concerns, not your team’s.
Side-by-side, where each model fits.
Pick by the situation, not by the pitch.
- Pick in-house if you need a full-time Python engineer with benefits, equity, and an at-will employment relationship.
- Pick a US-based contractor if you need US-only data residency (HIPAA / FedRAMP / regulated workloads) or a very short engagement (under four weeks).
- Pick sourceBOLD if you need senior Python capacity on month-to-month engagement — backend, ML, or data engineering — with published pricing and 4–8 hours of US time-zone overlap.
Scale your team.
Surge ahead.
A concise scoping call. Pre-vetted candidates. First standup in a few short weeks.