Senior Software Engineer, AI Research

The Role

Own the intelligence layer that transforms enterprise process knowledge, documents, interviews, system logs, into a governed, provenance-rich ontology, and the extraction and learning systems that consume it. Deliver evidence-backed outputs; any hallucination is a product failure.

What You’ll Do

• Own end-to-end design and implementation of the knowledge-graph schema, including hierarchy, provenance, versioning, and extensibility.

• Build grounded-extraction pipelines that attach source evidence and epistemic status to every claim.

• Design and improve multi-context harnesses that manage memory tiers, budget constraints, and compaction for large-scale inference.

• Deliver cross-boundary knowledge distillation workflows that integrate external data sources into the internal ontology.

• Own consolidation frameworks that merge heterogeneous documents, interviews, and event logs into a unified model with conflict resolution.

• Build and iterate model adaptation loops, applying SFT, DPO, and RFT to balance engineering context and model capabilities.

• Design learning-loop infrastructure that captures feedback, drives active learning, and updates the ontology continuously.

• Own RL-environment and curriculum components, defining reward signals and evaluation metrics for safe operation.

• Build production-grade retrieval-generation pipelines that meet quality, latency, and cost budgets.

• Deliver comprehensive evaluation frameworks with blind rubrics and LLM-as-judge validation to measure grounding and hallucination.

What You’ll Bring

• Proven expertise delivering end-to-end AI research systems from prototype to production in regulated settings.

• AI-native velocity as a default mode of working

• Deep mastery of Python, PyTorch, and large-scale model fine-tuning techniques such as SFT, DPO, and RFT.

• Expert knowledge of graph databases and ontology engineering, including schema design, version control, and provenance tracking.

• Strong experience building extraction pipelines that link claims to source evidence and manage epistemic status.

• Hands-on skill with RL environments, curriculum design, and reward modeling for autonomous systems.

• Proficiency in retrieval-augmented generation, latency optimization, and cost-aware scaling.

• Ability to design and run rigorous applied-research experiments, including hypothesis formulation, ablation studies, and statistical validation.

• Excellent judgment to balance model adaptation versus context engineering, and clear communication with cross-functional stakeholders.

• PhD in Artificial Intelligence or Computer Science, or a related field, with at least 4 years of relevant industry experience in Research