Doc. TMF-003 · Rev 2026.08 Remote First · Anywhere Crew Recruitment Sheet 2 of 3

Crew Manifest · Positions Open

Join us to launch your career

Code, products, data, experiences, and your career. We are a remote-first company, so you can work from anywhere. Yes, anywhere. Even out of a hammock in the Bahamas.

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Section 01 · Perks

Workclass perks for a workclass team

P-01

Company sponsored certifications for upskilling

P-02

State of the art gear, including a top-spec MacBook Pro

P-03

Flexible work hours

P-04

Regular retreats

Section 02 · Open Positions

Six seats, one crew

Any level of experience. We hire for skills and passion, not for the length of your resume. Expand a role for the full spec.

How we work with AI

Coding agents are part of the toolkit here, not a secret. We expect you to use them well, and to review what they produce as rigorously as anything you wrote by hand. You are welcome to use them in our interviews too; we are more interested in how you direct and verify the work than in watching you type.

ROLE-01 Frontend EngineerTL;DR: TypeScript, React 19, Next.js, Tailwind, Playwright, streaming AI UIs +

Required Skills

Deep TypeScript, and modern React: Server Components, Suspense, streaming, and the transition APIs
App architecture in Next.js or React Router 7, with a real opinion about where rendering should happen
Styling with Tailwind and design tokens; comfortable with container queries, cascade layers, and modern native CSS
Typed data access end to end, via TanStack Query with tRPC or GraphQL/OpenAPI codegen
Testing with Vitest and React Testing Library, Playwright for end-to-end, and visual regression where it earns its keep
Accessibility to WCAG 2.2 and Core Web Vitals (INP in particular) treated as requirements, not polish
Interfaces for AI output: token streaming, interruptible generations, citations, confidence, and human-in-the-loop approval steps
Brownie points for data visualisation at scale: Visx, Observable Plot, or D3 when nothing else will do

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ROLE-02 Backend EngineerTL;DR: Python 3.13, FastAPI, Pydantic, async, Postgres, MCP, tool-calling +

Required Skills

Modern async Python (FastAPI, Pydantic v2, SQLAlchemy 2.0), or an equivalent TypeScript stack
Current Python tooling as a matter of course: uv, ruff, and a type checker in CI
Postgres as a first-class tool, pgvector included; Redis, and event-driven work over Kafka or NATS
API design across REST/OpenAPI, gRPC, and GraphQL, with authorisation and tenancy considered from the start
Durable execution and background work with Temporal, Celery, or arq, plus idempotency you can defend
Building for LLMs: tool and function calling, MCP servers, structured outputs, streaming, and retries around non-deterministic calls
Treating token cost and latency as engineering budgets, with caching and model routing to match
Designing against prompt injection and running agent-executed code in a sandbox, not in your process
Brownie points for OpenTelemetry instrumentation you actually use to debug production

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ROLE-03 Quality & Evals EngineerTL;DR: Playwright, pytest, k6, contract testing, LLM eval harnesses, red-teaming +

Required Skills

Playwright for browser and API automation, with pytest or Vitest underneath
Load and soak testing with k6 or Locust, and the judgement to know what the results mean
Contract testing (Pact, Schemathesis) and property-based testing (Hypothesis, fast-check)
Owning CI quality gates: flake triage, test data management, and keeping the suite fast enough that people run it
Evaluating non-deterministic systems: golden sets, rubric grading, LLM-as-judge calibrated against human labels
Regression suites for model and prompt changes, so a swap can ship without crossed fingers
Adversarial testing: jailbreaks, prompt injection, data exfiltration, and tool-abuse paths
Debugging from traces rather than reproducing by hand
Brownie points for having built an eval harness that changed a shipping decision

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ROLE-04 DevOps EngineerTL;DR: OpenTofu/Terraform, Kubernetes, GitOps, OTel, supply-chain security, GPUs +

Required Skills

Infrastructure as Code with Terraform or OpenTofu, and Pulumi or Ansible where they fit better
Kubernetes in anger, with GitOps delivery through Argo CD or Flux
Software supply-chain security: SBOMs, signing with Sigstore, provenance, and scanning with Trivy or Grype
Policy as code with OPA or Kyverno, and secrets handled through Vault or External Secrets
Observability built on OpenTelemetry (Prometheus, Grafana, Loki, Tempo), with SLOs that drive decisions
Platform engineering instincts: paved roads and self-service beat ticket queues
Running inference workloads: GPU scheduling, vLLM or Triton, model and artifact registries
Sandboxing and egress control for agent workloads, and eval gates as a CI step alongside tests
FinOps as a habit, with cloud and token spend both attributable
Brownie points for a cloud, Kubernetes, or security certification

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ROLE-05 Data EngineerTL;DR: Python, dbt, Iceberg, DuckDB, Dagster, Flink, Kafka, retrieval pipelines +

Required Skills

Lakehouse architecture on Iceberg or Delta over object storage, and knowing when a warehouse is simply the better answer
Transformation with dbt or SQLMesh, modelled well enough that analysts trust it
Orchestration in Dagster, Airflow 3, or Prefect, with assets and lineage rather than a pile of cron
Streaming with Kafka and Flink, and change data capture through Debezium
Query engines chosen on merit: DuckDB, ClickHouse, Trino, Snowflake, BigQuery
Data quality as a contract: expectations, tests, and OpenLineage-based lineage
Retrieval pipelines end to end (parsing, chunking, embedding, and refresh) feeding pgvector, Qdrant, or LanceDB
Hybrid search with reranking, and the evaluation to prove it beats what it replaced
Brownie points for document-heavy pipelines: layout-aware parsing at volume

Send your resume to careers@themoonshotfactory.com Apply →

ROLE-06 AI / ML EngineerTL;DR: PyTorch, Hugging Face, RAG, agents, fine-tuning, evals, MLflow +

Required Skills

PyTorch and the Hugging Face ecosystem, with scikit-learn for the many problems that never needed a network
Working fluency with frontier and open-weight models, and a view on when each is the right call
Retrieval-augmented generation done properly: chunking strategy, hybrid search, reranking, and citation fidelity
Agentic systems: tool use, planning and decomposition, multi-step execution, and recovery when a step fails
Adaptation techniques and their trade-offs: prompting, LoRA/QLoRA fine-tuning, distillation, quantisation
Evaluation as the core discipline: offline suites, online experiments, and honest error analysis
MLOps with MLflow or Weights & Biases, model registries, and drift monitoring after launch
Document AI: layout-aware extraction and vision-language OCR on messy, real-world scans
Brownie points for publications at NeurIPS, ICML, ICLR, or CVPR, or a model card you are proud of

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Section 03 · Contact

Want to build a rocketship?

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Office

2055 Limestone Rd Ste 200C
Wilmington, DE 19808 ↗

Social Networks

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