<?xml version="1.0" encoding="UTF-8"?>
<source>
  <publisher>VAPNIK.AI — #1 Global &amp; Remote AI-Native Data Science Index</publisher>
  <publisherurl>https://vapnik.ai</publisherurl>
  <lastBuildDate>Fri, 13 Nov 2026 12:48:00 GMT</lastBuildDate>
  <job>
    <title><![CDATA[Principal / Staff Statistical Learning, Causal & Quantitative Data Scientist (Remote R&D Pod)]]></title>
    <date><![CDATA[2026-11-13]]></date>
    <referencenumber><![CDATA[VAPNIK-SLT-CORE-01]]></referencenumber>
    <url><![CDATA[https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></url>
    <company><![CDATA[VAPNIK.AI]]></company>
    <city><![CDATA[Remote]]></city>
    <country><![CDATA[Worldwide]]></country>
    <jobtype><![CDATA[Part-Time / Full-Time Remote R&D Pod]]></jobtype>
    <salary><![CDATA[$25–$45 / hr USD]]></salary>
    <education><![CDATA[University Degree (MSc / PhD / Postdoc) or Olympiad Laureate in Data Science, Statistics, Mathematics, or Computer Science]]></education>
    <description><![CDATA[Architect non-asymptotic statistical models, Structural Risk Minimization pipelines, double/debiased causal inference estimators, and conformal prediction intervals for high-dimensional financial, enterprise, and scientific datasets. Canonical Formulation: R(α) ≤ R_emp(α) + √[ ( h(ln(2ℓ/h) + 1) − ln(η/4) ) / ℓ ]. Stack: Statistical Learning Theory, Causal & Bayesian Inference, Conformal Prediction, Quantitative Alpha & Risk, Python / JAX / C++. Flexible 10–25 hrs/week remote R&D pod open worldwide across 190+ countries (strictly excluding Russia, Iran, and North Korea). Apply via https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></description>
  </job>
  <job>
    <title><![CDATA[Frontier LLM Post-Training, LUPI Distillation & Formal RLVR Verifier (Remote R&D Pod)]]></title>
    <date><![CDATA[2026-11-13]]></date>
    <referencenumber><![CDATA[VAPNIK-RLVR-LUPI-02]]></referencenumber>
    <url><![CDATA[https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></url>
    <company><![CDATA[VAPNIK.AI]]></company>
    <city><![CDATA[Remote]]></city>
    <country><![CDATA[Worldwide]]></country>
    <jobtype><![CDATA[Part-Time / Full-Time Remote R&D Pod]]></jobtype>
    <salary><![CDATA[$20–$45 / hr USD]]></salary>
    <education><![CDATA[University Degree (MSc / PhD / Postdoc) or Olympiad Laureate in Data Science, Statistics, Mathematics, or Computer Science]]></education>
    <description><![CDATA[Design privileged-information teacher-student distillation (LUPI), deterministic reward verifiers (RLVR / GRPO / DPO), step-level process supervision, and multi-agent reasoning evaluations across mathematics, algorithms, and data science. Canonical Formulation: max_{π_θ} E_{y ~ π_θ(·|x)}[ R_ver(x, y) ] − β D_KL(π_θ ‖ π_ref). Stack: RL with Verifiable Rewards (RLVR), LUPI / Knowledge Distillation, GRPO / DPO Post-Training, Formal Verification (Lean 4 / Z3), PyTorch / vLLM. Flexible 10–25 hrs/week remote R&D pod open worldwide across 190+ countries (strictly excluding Russia, Iran, and North Korea). Apply via https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></description>
  </job>
  <job>
    <title><![CDATA[Petabyte Columnar OLAP & High-Dimensional Vector Database Kernel Architect (Remote R&D Pod)]]></title>
    <date><![CDATA[2026-11-13]]></date>
    <referencenumber><![CDATA[VAPNIK-DB-KERNEL-03]]></referencenumber>
    <url><![CDATA[https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></url>
    <company><![CDATA[VAPNIK.AI]]></company>
    <city><![CDATA[Remote]]></city>
    <country><![CDATA[Worldwide]]></country>
    <jobtype><![CDATA[Part-Time / Full-Time Remote R&D Pod]]></jobtype>
    <salary><![CDATA[$25–$45 / hr USD]]></salary>
    <education><![CDATA[University Degree (MSc / PhD / Postdoc) or Olympiad Laureate in Data Science, Statistics, Mathematics, or Computer Science]]></education>
    <description><![CDATA[Engineer low-latency HNSW / DiskANN / Product-Quantized vector indexes, SIMD-accelerated columnar analytical query engines, and petabyte-scale feature stores powering real-time RAG and statistical inference. Canonical Formulation: q* = argmax_{x ∈ D} K(q, x) = argmax_{x ∈ D} ⟨φ(q), φ(x)⟩_H. Stack: ClickHouse / DuckDB / Arrow, Qdrant / Milvus / pgvector, HNSW / DiskANN / PQ Kernels, Rust / C++ / CUDA SIMD, Distributed Storage Engines. Flexible 10–25 hrs/week remote R&D pod open worldwide across 190+ countries (strictly excluding Russia, Iran, and North Korea). Apply via https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></description>
  </job>
  <job>
    <title><![CDATA[Doctoral, Olympiad & Graduate Research Fellow in AI-Native Data Science (Remote R&D Pod)]]></title>
    <date><![CDATA[2026-11-13]]></date>
    <referencenumber><![CDATA[VAPNIK-FELLOW-GRAD-04]]></referencenumber>
    <url><![CDATA[https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></url>
    <company><![CDATA[VAPNIK.AI]]></company>
    <city><![CDATA[Remote]]></city>
    <country><![CDATA[Worldwide]]></country>
    <jobtype><![CDATA[Part-Time / Full-Time Remote R&D Pod]]></jobtype>
    <salary><![CDATA[$20–$35 / hr USD]]></salary>
    <education><![CDATA[University Degree (MSc / PhD / Postdoc) or Olympiad Laureate in Data Science, Statistics, Mathematics, or Computer Science]]></education>
    <description><![CDATA[Dedicated remote part-time R&D track for PhD/MSc candidates, postdoctoral researchers, and IMO / ICPC / IOI / Kaggle Grandmaster laureates working strictly within their mathematical and data-engineering specialization. Canonical Formulation: P{ Y_{ℓ+1} ∈ Γ^ε(X_{ℓ+1}) } ≥ 1 − ε. Stack: MSc / PhD / Postdoc Track, IMO / ICPC / Kaggle Caliber, Probability & Optimization, Synthetic Data Curricula, 100% Remote Part-Time. Flexible 10–25 hrs/week remote R&D pod open worldwide across 190+ countries (strictly excluding Russia, Iran, and North Korea). Apply via https://baserow.io/form/5dGFgfNPjhbG9qpSw8A3yGNK72vJQkGmkr__j_qmyn8]]></description>
  </job>
</source>