• Own the architecture of Mercor's code search and retrieval systems end to end—hybrid retrieval combining dense code embeddings and BM25, candidate generation, ranking, and re-ranking over code tasks.
• Solve the hard problem of identifying similar code tasks—retrieval that captures code structure, semantics, intent, and difficulty rather than surface text.
• Build the system that identifies and selects the correct code-specific models for a given task, and routes tasks to the right model.
• Design natural-language-to-query translation that turns NLP questions into precise search over code and tasks.
• Design and operate the indexing pipeline so the task index stays fresh and consistent as new tasks, solutions, and results arrive continuously—balancing incremental updates, full rebuilds, and real-time ingestion.
• Make the cost-and-speed tradeoffs that keep search fast and economical at scale: embedding dimensionality and quantization, ANN index choice and parameters, caching, sharding, and serving infrastructure.
• Build the systems and evaluation harnesses that let us continuously evolve embeddings, models, and search quality—safely swapping in new code models, re-embedding corpora, and A/B testing relevance as SOTA advances.
• Define and drive the long-term technical strategy for code retrieval across the organization, and lead the highest-stakes design reviews.
• Establish evaluation metrics, offline/online testing, and quality guardrails so search improvements are measurable and regressions are caught before they ship.
• Stay deeply hands-on: prototype critical systems, ship production code, and unblock teams on their hardest retrieval and infrastructure problems.
• Mentor and grow engineers—junior and senior—through design reviews, pairing, and clear technical writing, raising the technical bar across the org.
• Partner with product, researchers, and engineering leadership on build-vs-buy decisions, platform investments, and technical hiring.

What We're Looking For

• 8+ years of professional software engineering experience, including 3+ years operating at a Senior level or above, with a Staff-level track record of org-wide technical impact.
• Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking.
• Good to have but not required: Experience with code search or code understanding—retrieval over code, code embeddings, or working with code-specific models—and an appreciation for why matching similar code tasks is harder than matching text.
• Strong understanding of the search algorithms and index internals—vector/ANN indices (e.g. HNSW, IVF, product quantization), inverted indices, and engines such as Elasticsearch/OpenSearch, Lucene, FAISS, or vector databases.
• A track record of making the right cost-vs-speed tradeoffs: latency budgets, throughput, memory footprint, and infrastructure spend on high-QPS systems.
• Familiarity translating natural-language questions into structured search queries (query understanding, semantic parsing, or LLM-assisted query generation).
• Excellent systems fundamentals: distributed systems, data modeling, and API design at scale.
• Demonstrated technical leadership and mentorship—you've helped junior and senior engineers grow and level up an engineering team.
• Genuine excitement for agentic development and new technology, fluency with modern AI dev tools (e.g. Claude Code, Cursor, Copilot), and a deep passion for writing great code.
• Excellent communication—able to make complex tradeoffs legible to both engineers and leadership. Strong opinions, loosely held. High ownership, pragmatism, and a bias toward shipping.

Nice to Have

• Experience training or fine-tuning code embedding models or code-specific LLMs.
• Experience with learning-to-rank, semantic search, or recommendation systems in production.
• Familiarity with LLM-based retrieval, RAG patterns, and model routing/selection.
• Background operating latency-critical services on modern cloud and orchestration infrastructure.

Why Mercor

• Impact: Own the code search systems that decide how well Mercor finds, routes, and evaluates the code tasks training the world's frontier models, at a company scaling faster than almost any in its category.
• Ownership: Org-wide technical scope with a direct line to engineering leadership and real authority over technical direction.
• Learning: Work alongside world-class engineers, product leaders, and AI researchers building at the frontier of AI.
• Growth: Shape the engineering organization itself—its standards, its architecture, and its next generation of technical leaders.

Benefits

• Generous equity grant vested over 4 years
• Up to $15K relocation bonus (if moving to the Bay Area)
• A $10K housing bonus (if you live within 0.5 miles of our office)
• A $1.5K monthly stipend for meals
• Free Equinox membership
• Health insurance

Mercor is an equal opportunity employer. We work in-person five days a week in our San Francisco office.

Compensation: $250K - $500K