Now in private beta · Series A-C startups only

The talent marketplace
built for AI engineers.

Verified skills. Explainable matches. Shortlists in 72 hours, not 12 weeks. Hire AI/ML talent the way the talent expects to be hired.

72hr
Avg shortlist SLA
100%
Skills evidence-verified
3.4x
Faster than LinkedIn
$0
Forever for candidates
How it works

Verification-first hiring

Every profile is evidence-backed. Every match is explainable. No more "PyTorch (self-claimed)."

1

Candidates build evidence-backed profiles

Auto-import from GitHub, Hugging Face, Kaggle, arXiv. Pass role-specific assessments. Pin standout projects. Every skill shows its source.

2

Employers post structured roles

Our AI co-author flags ambiguous JDs, suggests realistic comp bands, and warns about over-specs that collapse the candidate pool.

3

72-hour shortlist with the why

Multi-signal matching (skills + projects + verified credentials + preferences) ranks candidates and explains every recommendation in plain English.

Live preview

See both sides of the marketplace

Click between the candidate profile and the employer's AI-generated shortlist.

PR
Priya R.✓ Verified
Senior ML Engineer · Open to senior IC roles · Remote (US)
📍 Austin, TX 💼 6 yrs experience 💰 Target: $310K–$380K 🎯 Looking: Series B-D startups

Verified Skills

PyTorch 3 projects · assessment
Distributed Training verified · Anthropic
RLHF / Alignment paper + repo
LLM Eval Frameworks assessment
MLOps (Kubeflow, Ray) 2 projects

Credentials

🐙
GitHub: priya-r
2.4K stars · 47 repos · 3 ML libraries maintained
🤗
Hugging Face: 4 model cards
12K+ downloads · sentiment-finetuned-llama-3
📄
arXiv: 2 first-author papers
Cited 47 times · NeurIPS workshop 2024
🏆
Kaggle: Expert tier
Top 2% · 2 silver medals (NLP track)

Featured Projects

Production RAG at scale (DocumentAI Inc.)
Led 4-engineer team rebuilding company's RAG pipeline; cut p95 latency from 4.2s → 380ms while improving relevance by 18%. Open-sourced retrieval-eval-kit.
RAGVector DBsEvalTeam lead
Distributed fine-tuning library
Built and maintain torch-shard, an open-source library for sharded fine-tuning of 7B-70B models on commodity GPUs. 1.2K GitHub stars, used in production at 3 startups.
PyTorchFSDPOpen-source
RLHF ablation study (NeurIPS '24 workshop)
First-author paper measuring reward-model overfitting under different KL-regularization schemes. Methodology adopted by 2 frontier labs (cited).
RLHFResearchPublished

AI Career Score

87/100
Top 8% of senior ML engineers
↑ 4 pts this month
Next steps to reach 92: Complete the Multi-Agent Orchestration assessment · Add a pinned eval-framework project · Connect a LinkedIn reference from a former tech lead.

Senior ML Engineer — Production RAG

Posted 2 days ago · Series B fintech · Remote (US) · $300K–$370K

Shortlist delivered in 38 hours
AI-curated shortlist 12 candidates
PR
Priya R. ✓ Verified
Senior ML Engineer · 6yr · Austin, TX · Open · $310-380K
94%
Match
⚡ Why this match
  • Production RAG experience verified in 2 GitHub projects (cut p95 4.2s→380ms at DocumentAI)
  • Distributed training expertise — maintains open-source torch-shard library (1.2K stars)
  • Team leadership at Series B scale (led 4 ML engineers); comp band aligns; remote-ready
JK
Jordan K. ✓ Verified
Staff ML Engineer · 8yr · NYC · Passive · $340-420K
89%
Match
⚡ Why this match
  • Built RAG eval framework at previous startup; co-authored 1 industry paper on retrieval quality metrics
  • Strong vector DB experience (Pinecone, Weaviate) — verified through 3 production projects
  • Note: Currently passive; comp ceiling slightly above your top of band — may need flexibility
ML
Maya L. ✓ Verified
Senior ML Engineer · 5yr · SF Bay · Open · $290-340K
86%
Match
⚡ Why this match
  • Shipped production LLM features at 2 fintechs — domain match is unusually strong
  • RAG experience verified via assessment (96/100) and 1 GitHub project
  • Earlier-career than other top picks; strong learning trajectory; would benefit from staff-level mentorship
AI-native, not AI-decorated

The AI under the hood

Built by people who ship ML in production. Used by people who ship ML in production.

🎯

Multi-Signal Matching

Candidates and jobs are vectors over declared, verified, demonstrated, and inferred signals. A learned ranker on top — not naive cosine similarity.

📋

AI Resume Scoring

Every candidate gets a grounded LLM analysis: role progression, project scope, gaps, and exact suggestions to strengthen their profile.

💡

Explainable Reasoning

Every match shows top-3 reasons grounded in the candidate's actual evidence. No black boxes, no hallucinated signals, no recruiter mistrust.

🧭

Career Copilot

"You're 70% match for Series-B fintechs. To reach 85%: complete MLOps assessment, add distributed-training project." Brings candidates back weekly.

✍️

JD Co-Author

Flags ambiguity, recommends realistic comp bands, warns when your requirements would shrink the candidate pool below viability.

⚖️

Bias Audit

Quarterly disparate-impact audits, never trained on protected attributes, public model card, transparent appeals process for every match.

Why us

vs. The status quo

There are job boards. There are talent networks. There are recruiters. There's no AI-native, verification-first marketplace built for this category — until now.

Capability LinkedIn Turing AIJobs.com InfiniteAIstaffing
AI/ML niche focus Partial
Skill verification Self-claimed Heavy testing None Evidence-based + AI-graded
Explainable matching
Speed to first interview Weeks Weeks Weeks 48–72 hours
Candidate experience Spammy Heavy gating Passive listing Career copilot
Pricing model Subscription Markup on rate Listing fees Hybrid SaaS + success

Ready to hire AI talent the right way?

Join the founding cohort. Free 30-day trial: 3 vetted candidates or you don't pay.