AI Engineer, Search and Context Job at Her (SF, NYC, or Remote (USA))

AI Research Engineer, Search and Context

AI Research Engineers at Hex partner with product teams to build industry-leadin...

Location

United States , SF, NYC, or Remote

Salary:

225000.00 - 285000.00 USD / Year

Her

Expiration Date

Until further notice

Requirements

Experience building and measuring high quality search and recommendation systems
Experience getting AI/ML capabilities into production and serving real users
A lot of enthusiasm for applications of AI to real business problems
Understanding of core MLOps/SW Architecture concepts for modern ML-based applications
Comfortable working in both Python & JS/TS
Experimentalist mindset
Interest in the data space, and a love of shipping great products and building tools that empower end users to do more
Experience maintaining a high quality bar for design, correctness, and testing

Job Responsibility

Experimenting with new agentic techniques for search, discovery, and context management
Designing and implementing the architecture for our scalable search and indexing pipelines
Working at the cutting edge of production AI applications deployed to real customers

What we offer

Market-benched salary & equity
Comprehensive health benefits
Flexible paid time off

Fulltime

Staff AI Context Engineer

MagicSchool is seeking a Staff AI Context Engineer to architect and enhance the ...

Location

United States

Salary:

205000.00 - 240000.00 USD / Year

EdTech Jobs

Expiration Date

Until further notice

Requirements

Deep Knowledge Systems Experience: 5+ years building large-scale information systems with at least 2+ years in staff/senior roles. Extensive hands-on experience with RAG systems, knowledge graphs, or semantic search platforms in production environments.
Graph Database Expertise: Deep experience with graph databases (Neo4j, Neptune, or similar), including schema design, query optimization (Cypher, Gremlin), and building graph-based applications.
RAG & Retrieval Mastery: Demonstrated expertise building production RAG systems including embedding selection, chunking strategies, hybrid search, reranking, and retrieval evaluation. Familiarity with vector databases (pgvector, Pinecone, Weaviate, Qdrant).
Embedding & NLP Background: Strong understanding of embedding models (sentence transformers, domain-specific embeddings), fine-tuning approaches, and semantic similarity. Experience with document processing, entity extraction, and text chunking for optimal retrieval.
Technical Stack: Strong coding skills in Python and/or TypeScript/Node.js. Experience with our stack (TypeScript, Node.js, PostgreSQL, NextJS, Supabase) plus graph databases and vector stores. Familiarity with LLM APIs and context management patterns.
Information Architecture: Deep understanding of information retrieval theory, semantic search, knowledge representation, and strategies for organizing complex domain knowledge for both human and AI consumption.
Leadership & Impact: Track record of architecting complex knowledge systems, making high-leverage technical decisions about information architecture, and mentoring engineers on sophisticated retrieval and graph concepts.

Job Responsibility

Knowledge Graph & Semantic Architecture: Architect and implement graph-based knowledge systems (Neo4j, Neptune, etc) that represent educational content relationships, standards alignments, prerequisite chains, curriculum coherence, learning progressions, and pedagogical connections.
Graph Schema & Ontology Development: Design and evolve ontologies and schemas for educational content, defining entity types (standards, concepts, skills, assessments), relationship semantics, and property models.
GraphRAG Implementation: Build GraphRAG systems that combine knowledge graph traversal with vector similarity, enabling agents to retrieve contextually connected educational materials.
Retrieval Pipeline Architecture: Architect and implement sophisticated retrieval-augmented generation pipelines including hybrid search (dense + sparse), multi-stage retrieval, reranking strategies, and query understanding.
Embedding & Vectorization Strategy: Design and operationalize embedding pipelines for educational content, selecting and fine-tuning embedding models, implementing chunking strategies, and managing vector stores at scale.
Retrieval Evaluation & Optimization: Design evaluation pipelines that measure retrieval precision, recall, MRR, and NDCG across educational content types. Continuously optimize retrieval quality.
Document Ingestion & Processing: Build robust ingestion systems that process structured and unstructured educational content, extracting entities, relationships, and metadata for knowledge base population.
Semantic Parsing & Extraction: Implement NLP pipelines for educational content that extract key concepts, prerequisite relationships, learning objectives, and pedagogical metadata.
Memory & Context Management: Invent and operationalize memory compaction mechanisms, session state management, and cross-conversation memory patterns that allow agents to maintain coherence across extended teaching workflows.
Context Evaluation & Monitoring: Design evaluation frameworks that measure retrieval precision, token relevance, attention allocation, and reasoning coherence as context evolves across sessions.

What we offer

Flexibility of working from home.
Unlimited time off.
Choice of employer-paid health insurance plans. Dental and vision are also offered at very low premiums.
Generous stock options, vested over 4 years.
401k match.
Monthly wellness stipend.

Fulltime

Senior Context Engineer, AI Systems

MagicSchool is seeking a Senior Context Engineer for AI Systems to design and op...

Location

United States

Salary:

160000.00 - 190000.00 USD / Year

EdTech Jobs

Expiration Date

Until further notice

Requirements

4+ years building distributed systems
Hands-on experience with RAG systems, knowledge graphs, or semantic search platforms in production environments
Strong coding skills in Python, TypeScript/Node.js
Experience with our stack (TypeScript, Node.js, PostgreSQL, NextJS, Supabase) or similar
Proficiency with LLM APIs (OpenAI, Anthropic, etc.) and their context management patterns
Experience with Model Context Protocol (MCP), context window optimization for specific model families, or building context-aware agent frameworks
Understanding of or interest in how educational content is structured (standards, curricula, taxonomies), privacy requirements (FERPA/COPPA), and how context needs differ across teaching scenarios
Experience with agent evaluation, measuring context quality/relevance, or instrumentation for attention budget tracking

Job Responsibility

Architect and optimize how MagicSchool's AI agents reason, remember, and operate within complex educational workflows
Design context management systems that determine what information our agents see, how they maintain state across multi-turn interactions, and how they retrieve knowledge
Implement the technical foundation of how AI agents manage their 'mental workspace'
Design and implement context curation systems for product features
Build memory compaction mechanisms and state management patterns
Implement monitoring and evaluation for retrieval precision and reasoning coherence
Build dynamic, runtime data fetching that enable agents to autonomously pull relevant curriculum content, student data, and educational resources
Build token-efficient tool APIs and retrieval layers for product teams
Partner with Product to translate educational workflows into optimal context configurations
Work with evaluations researchers, platform engineers, and others to implement memory modules, retrieval adapters, and human-in-the-loop correction systems

What we offer

Unlimited time off
Choice of employer-paid health insurance plans
Dental and vision offered at very low premiums
Generous stock options, vested over 4 years
401k match
Monthly wellness stipend

Fulltime

Principal Software Engineer - Microsoft AI Search

Location

United States , Redmond

Salary:

142800.00 - 274800.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience

Job Responsibility

Set technical direction for AI-powered image search, shaping how intelligent systems understand user intent, adapt to context, and deliver meaningful visual experiences at global scale
Own high-impact product and platform areas end to end, translating complex user needs into durable system designs and production-ready solutions
Apply large language models and multimodal AI in production, integrating reasoning and grounding capabilities to improve relevance, usefulness, and overall experience quality
Enable intelligent, adaptive experiences, ensuring image search systems make holistic decisions that guide users through exploration, refinement, and decision-making
Design and operate scalable platforms, driving high standards for performance, reliability, and efficiency under real-world workloads
Define success metrics and experimentation strategies, using data and user feedback to validate impact, guide tradeoffs, and inform long-term technical direction
Influence across teams and raise the engineering bar, partnering with product, design, data science, and platform teams, mentoring engineers, and guiding architecture and design reviews

Fulltime

Azure Apps and AI Solution Engineer

Are you curious, passionate about building intelligent applications, and ready t...

Location

Canada , Toronto

Salary:

128000.00 - 222400.00 CAD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science, Information Technology, Engineering or related field AND 6+ years technical pre-sales or technical consulting experience OR equivalent experience
Hands-on experience designing production-grade, intelligent applications on Azure using AI Foundry, cloud-native app platform services (AKS, Container Apps, App Service), with the ability to guide customers through end-to-end AI application modernization decisions
Hands‑on experience building and shipping generative AI workloads on Azure, including RAG architectures, multi-agent orchestration, LLM engineering fundamentals (chunking/embedding strategy, hybrid search, reranking, eval-driven development), and integration with Azure AI Search
Solid understanding of end‑to‑end AI application architecture concepts, including model selection, prompt engineering, tool calling and MCP (Model Context Protocol), content safety, observability, and responsible AI practices
Experience building and deploying cloud-native applications across the Azure app platform—AKS, Container Apps, App Service, and Functions—and integrating AI capabilities via API Management, Copilot Studio, and GitHub Copilot to support intelligent, enterprise-scale workloads
Ability to guide customers through application and AI platform modernization decisions, balancing architecture, governance, cost, and performance considerations to enable AI‑ready and enterprise‑scale outcomes

Job Responsibility

Solid technical foundation designing and modernizing Cloud & AI solutions on Azure, partnering with customers to move from legacy environments to secure, scalable cloud‑native architectures
Ability to lead technical migration and modernization discussions, applying structured approaches (e.g., 6R strategy) to guide customer decisions
Experience influencing technical decision makers (architects, platform leads, engineering managers) by translating complex architecture into clear, defensible solutions
Solid understanding of hybrid and cloud‑native architectures, including networking fundamentals (virtual networks, secure connectivity, routing, performance considerations)
Knowledge of Azure security and compliance principles, including identity, networking security, data protection, and alignment to regulatory and compliance frameworks
Hands‑on technical mindset with the ability to design, validate, and explain architectures, not just describe services
Proven collaboration skills working with customers, partners, engineering teams, and account stakeholders to deliver outcomes
Commitment to continuous learning, leveraging Microsoft training resources, hands‑on labs, and certifications to deepen Cloud & AI expertise
Drive technical sales by using technical demos, proof of concepts, and technical architecture accelerators to influence solution design and enable deployments
Lead architecture sessions and technical workshops to accelerate Cloud & AI adoption

Fulltime

Software Engineer, Search Quality and Ranking

We are looking for a engineer to join our small but nimble Search team whose mis...

Location

United States , New York; San Francisco

Salary:

160000.00 - 220000.00 USD / Year

Notion

Expiration Date

Until further notice

Requirements

Experience in a technically challenging domain (latency sensitive systems
recommender systems
etc)
Energized by learning new technologies
Holistic problem solver with clear understanding of context
Communicate nuanced ideas clearly
Impact driven and prioritize projects accordingly
Curious and willing to adopt AI tools

Job Responsibility

Build and maintain core search APIs
Build new ranking models that bring meaningful quality improvements while keeping latency low
Build out core ML infra (using Anyscale / Dynamo)
Improve ranking across all sorts of different recall sources, like Elasticsearch and vector databases

What we offer

Highly competitive cash compensation, equity, and benefits

Fulltime

AI and Machine Learning Engineer

HPE Labs - AI and Machine Learning Engineer. This role has been designed as ‘Hyb...

Location

United States , Andover

Salary:

136500.00 - 260500.00 USD / Year

Hewlett Packard Enterprise

Expiration Date

Until further notice

Requirements

PhD in Computer Science or related fields with a focus on data engineering and data science, in particular Machine Learning, Deep Learning, and/or data management for AI
Familiarity with AI, Machine Learning and Deep Learning algorithms
Experience with Generative AI: Large Language Models, Time Series Foundation Models, Diffusion Models, etc.
Expertise with end-to-end pipelines for AI and Machine Learning and in particular the data layer underlying the pipelines (e.g., DVC, Pachyderm, Common Metadata Framework)
Experience in AI model development lifecycle, ML/deep learning frameworks and MLOps platforms (e.g. Pytorch/Tensorflow, MLFlow, Kubeflow)
Experience with agentic AI platforms (e.g., LangGraph, CrewAI, ADK, etc.)
Strong programming skills in Python, C/C++, with high proficiency in data structures and algorithms
Experience with CI/CD code development
Outstanding analytical and problem solving skills

Job Responsibility

Research and development of advanced technologies in Data-centric and Trustworthy AI, including data and knowledge context retrieval, filtering, prioritization, generative AI model materialization, advanced reasoning and validation, to improve quality of AI agentic workflows
Development of capture, management, search, enhancement and interpretation of meta-data and lineage for AI pipelines that enable reproducibility, reuse and optimization of pipelines
Discovery, selection and usage of relevant high quality data for trustworthy AI outcomes across multiple AI applications
Development, evaluation and testing of Foundation AI models for different modalities: Natural Language Processing - NLP, Large Language Models - LLM, Time Series Analysis, Computer Vision, AI for Science, etc., and augmentation of AI models with structured knowledge (i.e., knowledge infused learning)

What we offer

Health & Wellbeing
Personal & Professional Development
Unconditional Inclusion

Fulltime

Gen AI Engineering and Scaled AI Transformation

Location

Canada , Mississauga

Salary:

145100.00 - 217700.00 USD / Year

Citi

Expiration Date

Until further notice

Requirements

10+ years of progressive experience in software engineering, ML, or AI platforms, with 5+ years leading senior engineers and architects
3+ years of hands‑on experience deploying LLM‑based systems in production environments at enterprise scale
Demonstrated authority across commercial and open‑source LLM ecosystems (e.g., OpenAI, Anthropic, Google, Llama), including model selection, fine‑tuning, and hosting strategies
Proven ability to define enterprise-wide GenAI standards, reference architectures, and reusable accelerators
Demonstrated leadership in establishing prompt engineering standards and orchestration patterns
Experience optimizing latency, throughput, accuracy, and token cost across large‑scale GenAI workloads
Bachelor’s degree/University degree or equivalent experience
Master’s degree preferred

Job Responsibility

Acts as a senior technical authority on Large Language Models, including both commercial and open‑source ecosystems (OpenAI, Gemini, Claude, Llama)
Leads model selection and deployment strategy, balancing use‑case fit, data sensitivity, cost efficiency, latency, accuracy, and regulatory constraints
Guides decisions on hosted vs. private vs. fine‑tuned models, ensuring optimal trade‑offs between performance, control, and operational risk
Establishes enterprise standards for LLM lifecycle management, including upgrades, regression validation, and decommissioning
Demonstrates hands‑on leadership in building GenAI applications using LangChain, LangGraph, LlamaIndex, and Hugging Face, translating experimentation into production systems
Architects agentic and multi‑step workflows, enabling tool‑use, reasoning chains, state management, and orchestration at enterprise scale
Sets reusable reference patterns and accelerators for GenAI adoption across application teams
Ensures solutions are built with enterprise-grade reliability, explainability, and extensibility
Designs and delivers robust RAG architectures that ground GenAI outputs in trusted, auditable enterprise data
Leads implementation of vector databases and embedding strategies (pgvector, Pinecone, Weaviate, FAISS), aligned with data access and security models

Fulltime

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AI Engineer, Search and Context

Job Description

Job Responsibility

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