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

AI Application Engineer

As an AI Application Engineer at Rearc, you'll contribute to the technical excel...

Location

United States

Salary:

Not provided

Rearc

Expiration Date

Until further notice

Requirements

2+ years of experience in AI engineering, machine learning (ML), or related fields, bringing valuable expertise in building and deploying intelligent systems
Strong understanding of state of the art techniques in generative AI, including large language models (LLMs), text generation and other foundation models
Familiarity with AI orchestration tools (e.g. LangGraph, CrewAI, Bedrock Agents, smolagents, etc)
Experience in fine-tuning, prompt engineering or otherwise adapting generative models for specific use cases
Experience with AI model evaluation, including human-in-the-loop and LLM judge paradigms
Familiarity with NLP libraries and frameworks
Hands-on experience in implementing Retrieval Augmented Generation (RAG) architectures and integrating retrieval systems with generative models
Knowledge of at least one vector store or database (e.g. Opensearch, Pinecone, PostgreSQL with pgvector) and techniques for similarity search
Familiarity with common data ingestion/ETL patterns for populating knowledge bases
Experience with implementing LLM tool calling (either directly, via an orchestration framework, or using Model Context Protocol (MCP) clients)

Job Responsibility

Collaborate with Colleagues – Work closely with colleagues to understand customers' business objectives and technical challenges, contributing to the design and development of effective GenAI solutions tailored to client needs
Apply GenAI Principles – Utilize modern tools and frameworks like LangGraph, to build scalable, reliable, and maintainable Compound AI systems. Leverage your understanding of AI fundamentals to ensure every project meets rigorous industry and ethical standards
Adapt to the latest Technologies & Patterns – continue to research, learn, and stay abreast of the most recent state of the art for AI application development
Promote Knowledge Sharing –Bolster our culture of continuous learning by sharing knowledge about AI engineering best practices through blog posts, articles, and internal talks. Support a collaborative environment that fosters shared expertise and ongoing innovation across our community

Senior AI Engineer

As a Senior AI Engineer on our AI Engineering team, you will be responsible for ...

Location

United States; Canada

Salary:

195000.00 - 298000.00 USD / Year

Apollo.io

Expiration Date

Until further notice

Requirements

8+ years of software engineering experience with a focus on production systems
1.5+ years of hands-on LLM experience (2023-present) building real applications with GPT, Claude, Llama, or other modern LLMs
Demonstrated experience building customer-facing, scalable LLM-powered products with real user usage
Experience building multi-step AI agents, LLM chaining, and complex workflow automation
Deep understanding of prompting strategies, few-shot learning, chain-of-thought reasoning, and prompt optimization techniques
Expert-level Python skills for production AI systems
Strong experience building scalable backend systems, APIs, and distributed architectures
Experience with LangChain, LlamaIndex, or other LLM application frameworks
Proven ability to integrate multiple APIs and services to create advanced AI capabilities
Experience deploying and managing AI models in cloud environments (AWS, GCP, Azure)

Job Responsibility

Build and productionize advanced AI systems powered by Large Language Models (LLMs) and intelligent agents
Work on critical Apollo capabilities including AI Assistant, Autonomous AI Agents, Deep Research Agents, Conversational Assistant, Semantic Search, Search Personalization, and AI Power Automation features
Build sophisticated multi-agent systems that can reason, plan, and execute complex sales workflows
Develop systems that maintain conversational context across complex multi-turn interactions
Build scalable large language model and agentic platforms
Build back-end systems necessary to support the agents
Develop and improve recommendation systems and search relevance algorithms
Build models for automatic company keywords, people keywords, and industry classification
Create intelligent matching and suggestion engines
Design and deploy production LLM systems

What we offer

equity
company bonus or sales commissions/bonuses
401(k) plan
at least 10 paid holidays per year, flex PTO, and parental leave
employee assistance program and wellbeing benefits
global travel coverage
life/AD&D/STD/LTD insurance
FSA/HSA and medical, dental, and vision benefits

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

New

Principal Software Engineer

Are you looking for an opportunity to work with the latest Azure offerings and p...

Location

India , Bangalore

Salary:

Not provided

Microsoft Corporation

Expiration Date

Until further notice

Requirements

10–12+ years of experience in software engineering, with significant experience building scalable backend or distributed systems
Strong programming expertise in one or more languages such as Python, Go, Java, or C#, with experience designing production-grade services and APIs
Experience building AI-powered applications, including integrating LLMs, implementing agent or Copilot workflows, and orchestrating multi-step AI interactions
Hands-on experience with LLM application frameworks and orchestration tools such as Semantic Kernel, LangChain, or similar agent frameworks
Familiarity with retrieval-augmented generation (RAG) architectures, vector databases, embeddings, and semantic search systems
Experience evaluating and improving model performance through prompt design, evaluation frameworks, fine-tuning, or feedback loops
Solid understanding of distributed systems concepts including scalability, reliability, observability, caching, and asynchronous processing
Experience deploying and operating AI workloads in cloud environments (preferably Azure), including containerized services and GPU-enabled infrastructure
Understanding of Responsible AI practices, including model governance, safety, privacy, and evaluation of AI behaviour in production systems
Ability to work across product, research, and engineering teams to translate product scenarios into scalable AI system architectures

Job Responsibility

Design, build, and operate scalable AI systems that power intelligent product experiences, including Copilot and agent-driven workflows
Architect and implement backend services that support multi-step AI interactions, including orchestration pipelines, context management, memory/state persistence, and tool execution
Integrate large language models (LLMs), APIs, and internal services to enable context-aware, human-in-the-loop experiences across customer scenarios
Build and maintain data and inference pipelines that support model training, fine-tuning, evaluation, and real-time inference across diverse data sources
Evaluate, benchmark, and tune AI/ML models (LLMs and traditional models) to meet product requirements for accuracy, latency, reliability, and safety
Implement robust retrieval, grounding, and knowledge integration mechanisms (e.g., RAG systems, semantic indexing, vector search) to power intelligent applications
Collaborate with product managers, software engineers, and researchers to translate product vision into production-ready AI capabilities and measurable outcomes
Ensure reliability, observability, and governance of AI systems, including monitoring model performance, data quality, and responsible AI practices
Build reusable platforms, APIs, and tools that enable teams to rapidly develop AI-powered features and self-service intelligent applications

Fulltime

Senior Research Engineer, LLM Evaluation and Behavioral Analysis

Together AI is building the fastest, most capable open-source-aligned LLMs and i...

Location

United States , San Francisco

Salary:

220000.00 - 270000.00 USD / Year

Together AI

Expiration Date

Until further notice

Requirements

Strong engineering skills with Python, evaluation tooling, and distributed workflows
Experience working with LLMs or transformer-based models, particularly in model evaluation, testing, or red-teaming
Ability to reason clearly about qualitative behavior, edge cases, and model failure patterns
Experience designing experiments, building datasets, and interpreting noisy behavioral signals
Understanding of function calling and structured output formats
Familiarity with GPU or distributed compute environments
Hands-on experience evaluating function-calling models, agentic systems, or tool-augmented LLM pipelines
Experience with multi-turn or multi-step reasoning tasks
Familiarity with inference systems, distributed infrastructure, or post-training workflows
Passion for discovering subtle behaviors, surprising model gaps, or edge-case failures

Job Responsibility

Build and iterate on evaluation frameworks that measure model performance across instruction following, function calling, long-context reasoning, multi-turn dialog, safety, and agentic behaviors
Develop specialized evaluation suites for: Function calling — argument correctness, schema adherence, tool selection, multi-function planning, and error recovery
Agentic workflows — task decomposition, multi-step planning, self-correction, and autonomous tool-use sequences
Tool-augmented interactions — search, retrieval, code execution, API-driven actions
Create CI/CD automated pipelines for A/B comparisons, regression detection, behavioral drift monitoring, and adversarial probing
Design and curate high-quality evaluation datasets, especially nuanced or challenging cases across domains
Collaborate with researchers and engineers to diagnose failures, triage regressions, and guide data selection, shaping strategies, objective design, and system improvements
Work with engineering teams to build dashboards, reports, and internal tools that help visualize behavior changes across releases
Operate in a fast-paced, high-impact environment with deep technical ownership and close partnership with world-class model researchers and infra engineers

What we offer

competitive compensation
startup equity
health insurance
other benefits

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 AI Engineer

We are looking for an experienced and exceptional AI / ML Engineer (Voice Agents...

Location

India , Hyderabad

Salary:

Not provided

Workato

Expiration Date

Until further notice

Requirements

Bachelor's degree in Computer Science, or a related field, or equivalent practical experience
5+ years in backend software development using modern programming languages (e.g., Python (strongly preferred!), Golang or Java)
Demonstrated experience building production AI systems including chatbots, virtual assistants, and automated support agents using SLMs, LLMs (commercial, open-source models)
Demonstrated strong foundational understanding and appreciate first principles thinking in ML, NLP (transformer based models)
Expertise in natural language understanding (NLU) and intent classification for customer query interpretation, entity extraction, dialogue state tracking and conversation flow management for building a reliable framework for context engineering
Expertise in tuning streaming ASR and TTS engines, Speech to Speech models for context and domain aware transcriptions and naturalness in voice
Expertise in conversation mining for identifying customer intents, root cause analysis, sentiment, resolution, policy adherence for not just auditing but truly understanding conversations for business outcomes across large enterprise scale
Expertise in working with use case based SLMs for realtime agent coaching and recommendations
Expertise in building knowledge bases and FAQ systems with dynamic content retrieval and self-learning capabilities from support interactions
Experience implementing multi-channel support automation across chat, email, voice, and messaging platforms with consistent context handling

Job Responsibility

Design and implement advanced AI/ML systems with a focus on SLMs, LLMs, AI Agents, and Search architectures
Build conversational AI interfaces that handle multi-turn low latency chat/voice customer interactions, maintain context across sessions, and seamlessly escalate to human agents when necessary
Build production-grade AI pipelines for data processing, model training, fine-tuning, benchmarking (dual-control, fluid model etc.) and serving at scale
Implement feedback loops and continuous learning systems that incorporate customer satisfaction metrics, agent corrections, LLM evaluations, human evaluations and conversation outcomes to improve model performance over time. Reinforce organizational policies based on knowledge bases, conversational data and memory systems
Create AI based analytics dashboards and reporting tools to track automation effectiveness, tracing for identifying bottlenecks, identify common customer pain points, and measure key performance indicators like resolution time, containment rate, and customer satisfaction scores. Quality assurance, management to get actionable insights from customer conversations and create evals for the current generation agents
Lead technical initiatives for AI system integration into existing products and services
Collaborate with data scientists and ML researchers to implement and productionize new AI approaches and models

What we offer

vibrant and dynamic work environment
multitude of benefits they can enjoy inside and outside of their work lives

Sr. Staff UX/UI Engineer - AI-Powered Design Systems

We are seeking an innovative UX/UI engineer who has mastered the integration of ...

Location

India , Bangalore

Salary:

Not provided

GE Vernova

Expiration Date

Until further notice

Requirements

Master’s Degree in Computer Science, Electrical Engineering, or related disciplines
3+ years of experience as a UX designer, UI engineer, product designer, or front-end engineer similar role
2+ years of using design tools like Figma, Sketch, or Adobe XD to create interactive prototypes and design systems
2+ years of experience implementing designs in production using HTML, CSS, and JavaScript/TypeScript
Experience with modern front-end frameworks (React, Vue, or Angular) and state management
Portfolio demonstrating shipped products with thoughtful user experiences and polished interfaces
Experience designing and implementing responsive, mobile-first interfaces
Understanding of accessibility standards and inclusive design principles
Experience with version control systems (Git) and collaborative development workflows
Must be willing to work out of an office located in Bangalore JFWTC Campus

Job Responsibility

Design and prototype user interfaces that make complex AI capabilities accessible and intuitive for users
Create design systems and component libraries that standardize AI interaction patterns across products
Conduct user research to understand how people interact with AI features and iterate based on behavioral insights
Design conversational interfaces, prompt builders, and feedback mechanisms for AI-powered features
Develop information architecture for applications incorporating semantic search, recommendations, and AI-generated content
Create responsive, accessible designs that meet WCAG 2.1 AA standards and Section 508 compliance
Transform designs into production-ready code using modern front-end frameworks (React, Vue, Angular)
Build reusable UI components that elegantly handle AI states (loading, streaming responses, error handling)
Implement real-time interfaces for AI features including chat interfaces, live previews, and progressive disclosure
Develop interactive visualizations for AI model outputs, confidence scores, and decision explanations

What we offer

Relocation Assistance Provided: Yes

Fulltime

AI Research Engineer, Search and Context

Her

Location:
United States , SF, NYC, or Remote

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Additional Information:

Job Posted:
March 21, 2026

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Job Posted:March 21, 2026

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