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Always hiring · ML/AI ← All stacks

ML and AI Engineers — Always Hiring.

We place ML/AI engineers across the United States as roles open with our clients. Submitting your profile adds you to our talent network for current and upcoming consideration.

We routinely place ML and AI engineers with our partner vendors' clients across the US — major tech metros and remote-eligible markets. Roles span LLM application engineering, ML platform / MLOps, applied ML (CV, NLP, ranking, search), and ML infrastructure. We work with engineers from junior through principal, on W-2, 1099, and C2C engagement models. Submit your profile here to be considered for current and upcoming roles. We don't post phantom jobs — we post when we have something real, and we keep your profile in our active bench between engagements.

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What we typically place

  • Applied ML engineers shipping models to production
  • LLM / RAG application engineers (eval frameworks, prompt engineering at scale)
  • ML platform engineers (model serving, feature stores, eval infra)
  • Research-engineer hybrid roles for AI-native teams
  • ML ops / model-monitoring engineers

What we look for

  • Shipped ML in production — not just notebook experiments. Eval, monitoring, rollback experience.
  • For LLM roles: real RAG / agent / eval framework experience, not just prompt engineering
  • Strong Python and at least one of PyTorch / TensorFlow / JAX, plus a serving stack
  • Honest framing of what you built vs what your team built
  • Comfort talking about model failure modes and the cost of getting them wrong

Common interview themes

  • LLM application architecture (RAG, agents, eval pipelines)
  • Feature engineering and feature stores at scale
  • Model serving — latency, batch, A/B testing infrastructure
  • Training-serving skew and how to detect it
  • Failure modes and ethics — when to NOT use ML

Coming soon

More prep guides on the way.

Subscribe to The Edition and we'll let you know when the ML/AI guide drops.

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Current market signal

ML/AI is the most signal-noisy hiring market we run. Half the 'AI Engineer' reqs we see are backend reqs with light ML — those are easier to fill than they read. The harder reqs are genuine applied-ML roles where the engineer must own model evaluation and deployment. LLM/RAG application engineers are in highest demand right now; pure ML infrastructure roles trail. Compensation is bifurcated — research-leaning roles pay 30–50% above applied roles.

Typical engagement

Applied ML roles are typically W-2 contract or full-time. LLM application engineering trends contract-to-hire as teams ramp up. C2C is most common at senior+ where engineers have established LLCs. ABC-test states (CA, NJ, MA, IL) are W-2 only.

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Engagement preference (pick all that work)

Please don't include SSN, date of birth, government ID numbers, or other sensitive personal data on your resume. Standard contact info, work history, and skills are all we need.

Edition Technologies is an equal opportunity employer. We consider qualified candidates without regard to any characteristic protected by law. Read more.

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