3 hours ago
Base Salary
$250k - $500k/yr
Responsibilities
- Own Mercor’s code search and retrieval architecture end to end, including dense embeddings, BM25, candidate generation, ranking, and re-ranking.
- Develop retrieval methods that capture code structure, semantics, intent, and difficulty.
- Build systems to select and route tasks to the appropriate code-specific models.
- Design natural-language-to-query translation for precise code and task search.
- Design and operate continuously updated indexing pipelines supporting incremental updates, full rebuilds, and real-time ingestion.
- Optimize embedding dimensionality, quantization, ANN index selection, caching, sharding, serving infrastructure, search speed, and cost.
- Build evaluation harnesses for evolving embeddings, models, and search quality, including re-embedding and A/B testing.
- Define long-term technical strategy for code retrieval and lead high-stakes design reviews.
- Establish offline and online evaluation metrics, testing practices, and quality guardrails.
- Prototype critical systems, ship production code, and unblock teams on difficult retrieval and infrastructure problems.
- Mentor engineers through design reviews, pairing, and technical writing.
- Partner with product, research, and engineering leadership on build-versus-buy decisions, platform investments, and technical hiring.
Requirements
- 8+ years of professional software engineering experience, including 3+ years operating at Senior level or above and a Staff-level record of organization-wide technical impact.
- Deep hands-on expertise with search and retrieval systems, including dense embeddings, lexical scoring, hybrid ranking, and re-ranking.
- Strong understanding of search algorithms and index internals, including vector/ANN indices, inverted indices, and search or vector-database engines.
- Experience making cost, latency, throughput, memory, and infrastructure tradeoffs for high-QPS systems.
- Familiarity with translating natural-language questions into structured search queries through query understanding, semantic parsing, or LLM-assisted query generation.
- Strong systems fundamentals in distributed systems, data modeling, and API design at scale.
- Demonstrated technical leadership and mentorship of junior and senior engineers.
- Fluency with modern AI development tools such as Claude Code, Cursor, and Copilot.
- Excellent communication, ownership, pragmatism, and ability to explain complex technical tradeoffs.
- Experience with code search, code understanding, code embeddings, or code-specific models is preferred but not required.
- Experience training or fine-tuning code embedding models or code-specific LLMs is preferred.
- Experience with learning-to-rank, semantic search, recommendation systems, LLM-based retrieval, RAG patterns, or model routing is preferred.
- Background operating latency-critical services on modern cloud and orchestration infrastructure is preferred.
Benefits
- Generous equity grant vesting over four years
- Relocation support may be available for moves to the Bay Area
- Monthly meal stipend
- Free Equinox membership
- Health insurance
- In-person work five days per week in the San Francisco office
Tech Stack
Elasticsearch
Categories
About Mercor
We find the best experts in every professional domain and put their knowledge to work training frontier models. Through APEX, we measure whether those models can actually perform economically valuable work. We're also bringing that expertise to enterprises: deploying custom AI agents, staffing teams with vetted domain experts, and helping organizations encode their own knowledge into AI systems.