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About This Role
AI Engineer, Search \& Knowledge Systems
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About Pi
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Pi is building an agentic product security platform for teams that need to secure software at the speed they build it.
Modern development is accelerating, but security knowledge is still scattered across code, tickets, documents, incidents, reviews, and the people who remember why decisions were made. Pi turns that context into institutional security memory, helping teams triage faster, remediate in context, prevent repeat vulnerability classes, and embed security guardrails where engineering work already happens.
We are building for a future where security is not a blocker at the end of the development process. It is part of how software gets designed, reviewed, shipped, and improved.
Read about Pi Security on Forbes!
About The Role
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We are looking for an AI Engineer specializing in search, retrieval, knowledge systems, and relationship discovery.
You will design and build the systems that help Pi understand and connect security\-relevant context across code, pull requests, tickets, documents, incidents, findings, cloud resources, and customer environments. Your work will power the retrieval, grounding, provenance, and relationship modeling behind Pi’s agentic security workflows.
This role is ideal for someone who combines strong software engineering with deep interest in information retrieval, applied AI, knowledge representation, ranking, evaluation, and production systems.
What You’ll Do
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- Build AI\-powered search and discovery systems across structured and unstructured security and engineering data.
- Develop retrieval\-augmented generation pipelines using embeddings, hybrid search, reranking, chunking, metadata filtering, grounding, and citation\-aware generation.
- Build knowledge systems that represent entities, relationships, events, decisions, vulnerabilities, controls, code ownership, services, and provenance.
- Improve relevance, recall, precision, ranking quality, and answer accuracy across search, investigation, and agentic workflows.
- Design systems for entity extraction, entity resolution, ontology design, relationship inference, and semantic enrichment.
- Evaluate and combine lexical search, semantic search, hybrid search, graph\-based retrieval, and agentic retrieval patterns.
- Build evaluation frameworks for retrieval quality, hallucination reduction, grounding, freshness, citation accuracy, and user satisfaction.
- Build ingestion and indexing pipelines that normalize, enrich, connect, and refresh data from multiple customer and product sources.
- Monitor production AI systems, debug retrieval failures, improve latency, and optimize cost/performance tradeoffs.
- Partner with product, backend, frontend, platform, and security teams to turn ambiguous customer needs into reliable knowledge systems.
- Help create the foundation that lets Pi preserve institutional security memory and prevent recurring vulnerability classes.
What We’re Looking For
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- Strong software engineering experience in Python, TypeScript, or similar languages.
- Experience building production search, recommendation, knowledge management, or AI retrieval systems.
- Hands\-on experience with RAG architectures, embedding models, vector search, rerankers, and LLM\-backed workflows.
- Strong understanding of information retrieval concepts such as indexing, ranking, query expansion, relevance scoring, recall/precision, BM25, dense retrieval, and hybrid search.
- Experience working with structured and unstructured data, including code, documents, tickets, logs, metadata, databases, APIs, and event streams.
- Experience designing evaluation methods for search relevance, retrieval quality, and AI\-generated answers.
- Ability to build reliable, observable, production\-grade systems.
- Strong product judgment: you can translate ambiguous user needs into practical search, knowledge, and retrieval systems.
- Strong security instincts around authorization, tenant isolation, data exposure, provenance, and safe handling of customer context.
- Ability to work in a fast\-moving startup environment with ownership, autonomy, and good judgment.
Technologies We Use
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- Python
- TypeScript
- Embedding models
- Rerankers
- Lexical, semantic, and hybrid search
- Vector search
- PostgreSQL
- Graph\-based data modeling
- Workflow orchestration systems
- Data ingestion and indexing pipelines
- Evaluation and observability tooling
- Docker
Nice To Have
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- Experience with knowledge graphs, graph databases, graph embeddings, ontology design, or taxonomy management.
- Experience with entity linking, entity resolution, relationship extraction, or semantic enrichment.
- Experience with LLM orchestration, agentic search, tool use, or multi\-step reasoning systems.
- Experience with NLP techniques such as named entity recognition, classification, summarization, clustering, topic modeling, or semantic similarity.
- Experience with data pipelines for ingesting, transforming, indexing, and refreshing large datasets.
- Experience with cloud platforms and production AI infrastructure.
- Experience with security products, developer tools, code analysis, cloud security, enterprise search, legal tech, finance, healthcare, or research platforms.
Example Projects
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- Build a hybrid search system that combines keyword search, semantic search, metadata filters, and graph traversal.
- Design a knowledge system that connects repositories, services, pull requests, tickets, findings, vulnerabilities, cloud resources, owners, and decisions.
- Build a RAG system that produces grounded answers with citations, confidence signals, and traceable source context.
- Create pipelines for extracting entities and relationships from code, tickets, documents, security findings, logs, and cloud metadata.
- Develop relevance evaluation datasets and automated tests for retrieval quality, grounding, and answer accuracy.
- Improve agentic workflows by giving AI systems better context, better retrieval, and better understanding of customer\-specific security history.
Success In This Role Looks Like
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- Users can find the right security and engineering context faster and with higher confidence.
- AI\-generated answers are grounded, cited, and reliable.
- Relationships that were previously hidden across code, tickets, documents, findings, and infrastructure become discoverable and useful.
- Search relevance, retrieval accuracy, grounding quality, and system latency measurably improve over time.
- Knowledge systems are maintainable, observable, and extensible as new data sources are added.
- The product helps customers understand risk, act faster, and prevent the same security issues from recurring.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PI Security LLC, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PI Security LLC AI Hiring
PI Security LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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