Research Team Lead

Serve AI is building the next generation of enterprise intelligence infrastructure for organizations that cannot afford hallucinations, inconsistent outputs, or black box AI. Our deterministic intelligence platform delivers fast, traceable, and auditable answers that organizations can trust across regulated, security conscious, and mission critical environments.

Unlike traditional AI systems that generate probabilistic responses, Serve AI is designed around deterministic execution. Every answer is grounded in verified enterprise knowledge, providing organizations with consistent, explainable, and repeatable results while maintaining complete control over their data and deployment.

We are backed by JAM Fund and a network of investors and executives from leading technology companies including Google, Microsoft, Meta, NVIDIA, OpenAI, Anthropic, Door Dash, Toast, Tesla, CAA, and Major League Baseball.

As an early stage company, every employee has the opportunity to make a meaningful impact. We move quickly, value ownership over bureaucracy, and look for people who enjoy solving difficult problems, building systems from scratch, and helping define the future of enterprise AI. If you thrive in environments where your work directly influences customers, products, and company growth, you’ll fit right in.

Lead the applied research program behind schema discovery, semantic alignment, query understanding, and reliable artifact generation. This is a research leadership role with a production mandate: set the agenda, establish experimental discipline, grow the team, and turn validated methods into capabilities that engineering can operate.

• Define and maintain a research roadmap tied to product risks and measurable platform outcomes.
• Lead work on schema matching, ontology alignment, entity resolution, query intent, confidence calibration, and human in the loop review.
• Establish evaluation protocols, baselines, ablations, decision criteria, and reproducibility standards before major experiments begin.
• Separate model quality from compiler, data quality, and product failures so each problem reaches the right owner.
• Partner with engineering to convert validated methods into versioned, observable production components.
• Preserve Serve AI's governance model: AI can recommend mappings or artifacts, but administrative state changes require explicit approval through the control plane.
• Recruit, coach, and manage a focused team that may include research engineers, scientists, and student researchers.
• Communicate results candidly, including negative findings, limitations, and decisions to stop work.

• Research priorities are explicit, bounded, and connected to customer and platform outcomes.
• Important method choices are supported by reproducible evidence rather than preference.
• Promising work moves into production with owners, tests, monitoring, and rollback criteria.
• The team avoids duplicate experiments and maintains durable records of methods, datasets, and results.

• Experience leading applied ML or research teams from hypothesis through production adoption.
• Technical depth in modern machine learning and strong experimental design judgment.
• A record of building healthy research cadence, mentoring talent, and partnering effectively with engineering and product.
• Clear communication about uncertainty, tradeoffs, and negative results.

• Semantic parsing, information retrieval, embeddings, entity resolution, knowledge graphs, or program synthesis.
• Evaluation of LLM assisted systems where correctness and provenance matter.
• Data integration, semantic layers, or enterprise vertical technology.
• Work in regulated, security sensitive, or on premises environments.

We favor evidence over intuition, versioned artifacts over hidden state, and root cause fixes over patches. We preregister decision rules where practical, preserve negative results, and distinguish measured performance from estimates.