Decentralized AI Is Hiring: What Post-Training, Evals and Inference Jobs Really Are

The AI roles showing up on crypto job boards have stopped being crypto jobs with an AI buzzword bolted on. They are real machine-learning pipeline jobs — post-training, evals, inference, agent infrastructure — at companies whose coordination layer happens to be a blockchain. Here is what each of those four titles actually involves, what teams ask for, and how to get into one.

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Ask most crypto job seekers what an "AI × crypto" role is and you'll hear something vague about AI agents that trade. That was 2024. In 2026 the openings look completely different: labs are hiring people to train, evaluate, and serve models, and the crypto part is the network the training runs on.

If you filter these roles out because "I'm not an AI researcher", you're skipping the one corner of this industry where headcount is going up instead of down.

Why These Count as Crypto Jobs at All

Three examples, all currently hiring, all doing the same structural thing — using a chain to coordinate compute that no single company owns:

• Nous Research runs Psyche, a decentralized training network built on Solana, where a smart contract manages consensus state while training gradients move over a separate peer-to-peer mesh. Its DisTrO optimizer is what makes that viable, cutting inter-GPU communication by orders of magnitude. Hermes 4.3 is their first production model post-trained entirely on Psyche — so this is shipping, not a whitepaper.
• Prime Intellect trained INTELLECT-2, a 32B reasoning model, through globally decentralized reinforcement learning across a permissionless swarm of contributors. The accompanying paper describes the pieces they had to invent to do it, including a way to verify work from untrusted inference nodes.
• Gensyn is building an open protocol for the compute and coordination machine intelligence needs, covering verifiable execution, peer-to-peer agent networking, and on-chain information markets.

The pattern: verification, permissionless participation, and paying strangers for compute are crypto problems. That's why these teams sit on our board and not on a generic AI one.

The Four Job Families, Decoded

1. Post-training and alignment. You take a base model and make it useful: supervised fine-tuning, reward modelling, RL-based optimization, domain adaptation. Day to day this is data strategy and pipeline work far more than it is novel research. OKX is hiring a Staff AI Engineer for model post-training and alignment, covering that whole lifecycle through to production inference. They want: hands-on SFT/RLHF experience and a pipeline you owned end to end.

2. Evals. The least glamorous title and probably the best entry point. You build benchmarks, LLM-as-judge systems, and the calibration protocols that tell a lab whether last week's change helped. Nous Research's opening asks for judge calibration work, extending benchmarks like GAIA or a SWE-bench slice with new tasks and automated graders, and failure analysis that feeds back into training data. They want: three years of engineering or data science, solid Python, and real opinions about one eval framework.

3. Inference. Serving models fast and cheaply, which at scale is a GPU performance problem. NEAR AI wants someone who has debugged and optimized SGLang, vLLM, or TensorRT and can exploit modern GPU architectures with PyTorch, Triton, or CUDA. They want: production LLM serving experience, plus a bonus for trusted execution environments — the confidential-compute angle is where the crypto and AI stacks meet.

4. Agent infrastructure. The applied end: orchestration, tool calling, sandboxed execution, guardrails. Coinbase is staffing this heavily — agentic systems for compliance and customer operations — and 0G Labs wants a product engineer building LLM streaming and sandboxed execution around untrusted code. They want: strong backend or full-stack skills and something agentic you actually shipped.

What They Pay

Where employers publish ranges, they're specific. Coinbase lists $218,025–$256,500 base for a staff engineer on core AI automation and $186,065–$218,900 for a senior engineer on core AI infrastructure, both excluding equity and bonus, both remote in the US. For comparison, a Staff Backend Engineer role at Uniswap Labs — no AI in the title — sits at $207,000–$259,000 plus token compensation. The AI premium is real but it isn't a different universe — what's different is how few people clear the bar.

How to Get In Without a PhD

1. Reproduce an eval and publish the writeup. Take a public benchmark, run it against two open models, document where the judge disagrees with humans. That's a weekend, and it's the exact artefact an evals hiring manager wants to see.
2. Contribute to an inference engine. vLLM and SGLang are open source and their issue trackers are full of real work. One merged performance PR beats a certificate.
3. Join a decentralized training run. Psyche and similar networks let you contribute compute and see the coordination layer from the inside — that's the half of the job description crypto-native candidates can already speak to.
4. Apply where these roles are actually tagged. Generic boards bury them. Ours are on the AI × Crypto page, and our broader guide to the category covers salaries and positioning in more depth.

For Employers

If you're hiring at this intersection, name the specialism in the title. "Machine Learning Engineer" attracts everyone and converts nobody; "Machine Learning Engineer, Evals" tells the right person the job is theirs. You can post your role from $19 and reach candidates who already understand both halves.

The Bottom Line

The AI jobs on crypto boards in 2026 are ordinary ML engineering jobs with an unusual substrate underneath. The titles sound intimidating and the actual requirements often aren't — evals in particular rewards careful engineers over researchers. Start with one reproducible artefact, then check what's open right now.