We are seeking a
Senior AI/ML Engineer
to build an evidence-grounded AI capability that verifies generated claims against approved scientific, clinical, regulatory, and reference materials before human review. The system will retrieve relevant evidence, decompose claims into verifiable assertions, evaluate evidence support, and provide
traceable decisions with citations
. The system must recognize unsupported or contradicted claims and
abstain rather than guess
.

• 
  Build production-grade
  Python/NLP pipelines
  for claim verification and evidence attribution.

• 
  Develop
  hybrid retrieval
  using lexical and vector search to identify relevant evidence.

• 
  Implement
  claim decomposition, natural language inference (NLI), entailment, and
  contradiction detection
  .

• 
  Evaluate whether generated claims are genuinely supported by cited evidence.

• 
  Design
  confidence thresholds, abstention logic, escalation rules, and human-in-the-loop
  workflows
  .

• 
  Build evaluation datasets with expert annotation guidelines and measure
  inter-annotator
  agreement
  .

• 
  Track false approvals, false rejections, abstentions, and other error categories.

• 
  Develop traceable systems that allow decisions to be reconstructed based on
  model version,
  evidence, citations, and reviewer actions
  .

• 
  Work with Medical, Legal, Regulatory, and scientific stakeholders to translate review requirements into technical solutions.

Required Skills

• 
  Strong
  Python
  production engineering.

• 
  NLP / LLM / Generative AI
  development.

• 
  RAG, hybrid search, vector search, and lexical retrieval
  .

• 
  Natural Language Inference (NLI), entailment, contradiction detection
  .

• 
  Claim decomposition and evidence attribution
  .

• 
  LLM/model APIs and production evaluation frameworks.

• 
  AI/ML evaluation, benchmarking, and error analysis
  .

• 
  Human-in-the-loop AI,
  confidence scoring and abstention
  .

• 
  Experience with scientific, technical, regulatory, legal, or other high-stakes content.

• 
  Experience creating
  expert-labeled datasets and annotation guidelines
  .

• 
  Strong understanding of
  traceability, citations, and reproducible AI decisions
  .

Preferred Skills

• 
  Knowledge graphs
  and relationships between claims, evidence, references, products, and indications.

• 
  Deterministic
  rules + ML/LLM decision systems
  .

• 
  Pharmaceutical, biotech, healthcare, regulatory, legal, financial compliance, or scientific

  
  publishing experience.

• 
  Familiarity with
  clinical studies, statistics, scientific literature, and citation practices
  .

• 
  Experience with
  LangChain, LlamaIndex, Hugging Face, PyTorch, or similar NLP/

  
  LLM frameworks
  .