Hire AI Research Engineers
Who Reproduce, Adapt, and Ship Frontier AI

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after we understand your product, stack, timeline, and seniority needs. The goal is not to send resumes quickly; it is to send AI Research Engineers who match the outcome, risk profile, and communication bar for the role.

Yes. You interview the shortlisted engineers before committing. We recommend using the interview to test paper-to-prototype reasoning, experiment design, benchmark selection, ablation thinking, and production relevance. That makes the selection practical for a CTO instead of resume-led.

The first week should produce visible proof that the engineer understands the research question and can evaluate it rigorously. You should see a prototype, benchmark result, or experiment plan with method notes, baseline assumptions, limitations, reproducibility details, and product implications. If progress is unclear, you should know that early, not after a long contract cycle.

A strong hire should produce a research-backed prototype, benchmark, or decision memo that explains the method, baseline, limitations, reproducibility, and product relevance. The outcome should be measurable through benchmark lift, reproducibility, ablation clarity, baseline strength, compute cost, error-profile improvement, and product decision value.

Quality is managed through senior screening, role-specific interview criteria, code or architecture review, documented decisions, and delivery checkpoints. For AI research work, we look for proof across paper reproduction, hypothesis design, baselines, datasets, metrics, ablations, error analysis, experiment tracking, PyTorch or JAX implementation, model optimization, and production handoff.

Yes. The engineer joins your tools, repositories, standups, issue trackers, review process, and communication channels. For AI Research Engineer work, we define the operating model explicitly: papers, hypotheses, experiments, datasets, model checkpoints, assumptions, compute cost, and limitations are written for engineering review.

Yes. Devlyn works with distributed teams and plans overlap windows for interviews, standups, reviews, and escalation. For AI Research Engineer engagements, the communication rhythm is tied to the proof points that matter: benchmark lift, reproducibility, ablation clarity, compute cost, error profile, and product decision value.

NDA and IP assignment are handled before onboarding. Access is scoped to the tools, repositories, datasets, systems, or environments required for the AI Research Engineer scope, and sensitive work is governed through your security rules, audit expectations, and approval process.

Use the risk-free trial to evaluate whether the engineer can turn a research question into a defensible experiment, choose the right benchmark, define a baseline, run ablations, explain failures, and connect results to product decisions. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

You can start with one specialist, add adjacent roles, or move into a pod model depending on the scope. Common expansion paths include product engineering, platform, data, security, QA, DevOps, or architecture support around the core AI Research Engineer work.

Typical options include Reproduce + Recommend ($24,000 fixed scope) 5 weeks, senior research engineer, Senior AI Research Engineer ($7,500/mo) Full-time, 5–10+ years, Research + ML + Infra ($22,000/mo) 3-person pod, 3–6 months. We confirm the right model after discovery so you can compare dedicated hiring, a focused sprint, or a small pod against the risk and timeline of your actual AI Research Engineer requirement.

We can support both models. If you already have strong product and engineering leadership, the engineer can plug into your process. If you need more structure, Devlyn can add delivery oversight, sprint planning, reporting, and senior technical review around experiment design, benchmark quality, ablations, compute cost, reproducibility, and production handoff.

Devlyn reduces the hidden work of sourcing, vetting, onboarding, replacing, and governing specialist engineering talent. For AI research, that matters because the real risk is interesting experiments with no path to production, unclear baselines, weak ablations, dataset leakage, and claims that cannot be defended. You get a shorter path to qualified candidates and a trial structure focused on research proof.

Devlyn is a better fit when AI research affects product direction, model quality, proprietary capability, compute cost, or long-term maintainability. You get vetting, replacement support, delivery governance, IP protection, and continuity around the parts freelancers often skip: reproducibility, ablations, failed-path documentation, compute accounting, handoff-ready code, and decision memos.

An AI Research Engineer is usually the right hire when your team needs to prove whether a model method is worth adopting. Common use cases include frontier method evaluation, paper reproduction, custom model programs, ranking or generation experiments, vision and language model adaptation, quality plateau breakthroughs, ablation studies, benchmark design, experiment tracking, model optimization, and research-to-product handoff. If discovery shows you mainly need application engineering, data pipelines, or MLOps deployment, we will say that before you hire.