What the work
actually produces.

The situation

A B2B SaaS company had built a strong product but no coherent AI strategy. Their engineering team was running disconnected ML experiments. The board had committed to an AI-first roadmap. They needed someone who could set direction, build a team, and ship — not just advise.

What we placed

We placed a Chief AI Officer with a background spanning applied research at a top AI lab and product leadership at a high-growth startup. She had shipped LLM-powered features at scale and had the organizational credibility to align engineering, product, and executive stakeholders.

The situation

A well-funded AI infrastructure startup needed a Staff-level engineer to own their LLM serving layer. The role required deep knowledge of inference optimization, model quantization, and low-latency serving at scale. Their previous two searches — run by generalist firms — had produced no viable candidates in four months.

What we placed

We identified a Staff Engineer who had spent three years building inference infrastructure at a major AI lab and had recently led the deployment of a sub-100ms serving pipeline for a billion-parameter model. He wasn't on the market. We reached him through a direct relationship.

The situation

A publicly traded fintech company had a data science team of 18 people operating without clear technical leadership. The previous VP had left after 14 months. The team was capable but directionless — shipping incrementally, not strategically. The CEO wanted someone who could build a research culture without losing execution velocity.

What we placed

We placed a VP with a background in quantitative modeling at a hedge fund and data science leadership at a high-growth fintech. She had built teams from 4 to 35, published applied research, and had a track record of translating data science work into measurable business outcomes.

The situation

A fast-growing e-commerce company had ML models in production but no platform to support them. Data scientists were managing their own infrastructure, experiments were hard to reproduce, and model deployment was a manual process that took weeks. They needed someone to build the platform layer from scratch.

What we placed

We placed a Head of ML Platform who had built internal ML tooling at two previous companies — one a major tech platform, one a Series C startup. He understood both the enterprise-grade requirements and the pragmatic constraints of a resource-limited team.

The situation

A seed-stage AI startup was building autonomous agent infrastructure for enterprise workflows. The founding team had strong research credentials but needed a senior engineer who could own the agentic systems architecture — multi-agent orchestration, tool-use frameworks, reliability at scale. This was a role that didn't exist at most companies yet.

What we placed

We found a candidate who had spent two years building agentic systems at a well-known AI lab and had recently led the development of a production multi-agent framework used by enterprise customers. He understood the research landscape and the engineering constraints of shipping agents that actually work.

The situation

A healthcare AI company had a fragmented data infrastructure — multiple pipelines built by different teams, no unified data layer, and a growing backlog of data quality issues affecting model performance. They needed a Director who could consolidate the architecture and build a team capable of supporting a 3x increase in data volume.

What we placed

We placed a Director with deep experience in healthcare data standards (HL7, FHIR), large-scale pipeline architecture, and team leadership. She had previously led a data engineering org of 22 at a health system and understood the compliance constraints that make healthcare data infrastructure uniquely complex.