Research Engineer, Retrieval & Search, Applied Engineering Job at OpenAI (San Francisco)

Machine Learning Research Engineer

You will be part of Kiddom’s Data Science team, building the foundation of our s...

Location

United States , San Francisco; New York

Salary:

175000.00 - 250000.00 USD / Year

Kiddom

Expiration Date

Until further notice

Requirements

Have 5+ years of industry experience applying machine learning to solve real-world problems with large, complex datasets, with 1–2 years in a technical leadership role
Proven track record designing, evaluating, and deploying ML/AI systems in production environments that drive measurable business impact, ideally in recommendation, personalization, search, or workflow optimization
Strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and common ML toolkits (scikit-learn, XGBoost, TensorFlow/PyTorch)
Strong analytical skills and ability to break down complex problems into measurable hypotheses and experiments
Excellent communication skills with a history of cross-functional collaboration with product, design, and engineering stakeholders

Job Responsibility

Architect and scale machine learning systems for search, personalization, and recommendations that power Kiddom’s teacher helper and insight engine
Develop evaluation-first development workflows to measure how models improve teaching efficiency, lesson planning, and student learning outcomes
Fine-tune machine learning models with feedback signals from teachers and students to align outputs with instructional goals and classroom needs
Design intelligent discovery pipelines that combine semantic retrieval, curriculum alignment, and real-time personalization
Build agentic assistants that help teachers plan lessons, adapt instruction, and reduce repetitive tasks
Collaborate closely with product managers, designers, and curriculum experts to translate high-level educational goals into scalable ML-powered systems
Coach and mentor junior ML engineers and data scientists, fostering technical and professional growth

What we offer

Competitive salary
Meaningful equity
Health insurance benefits: medical (various PPO/HMO/HSA plans), dental, vision, disability and life insurance
One Medical membership (in participating locations)
Flexible vacation time policy (subject to internal approval). Average use 4 weeks off per year
10 paid sick days per year (pro rated depending on start date)
Paid holidays
Paid bereavement leave
Paid family leave after birth/adoption. Minimum of 16 paid weeks for birthing parents, 10 weeks for caretaker parents. Meant to supplement benefits offered by State
Commuter and FSA plans

Fulltime

Research Engineer, GenAI

You will be part of Kiddom’s Data Science team, building the foundation of our s...

Location

United States , San Francisco; New York

Salary:

175000.00 - 250000.00 USD / Year

Kiddom

Expiration Date

Until further notice

Requirements

5+ years of industry experience applying machine learning to solve real-world problems with large, complex datasets
1–2 years in a technical leadership role
Proven track record designing, evaluating, and deploying ML/AI systems in production environments that drive measurable business impact, ideally in recommendation, personalization, search, or workflow optimization
Strong programming skills in Python
Fluency in data manipulation (SQL, Pandas) and common ML toolkits (scikit-learn, XGBoost, TensorFlow/PyTorch)
Strong analytical skills and ability to break down complex problems into measurable hypotheses and experiments
Excellent communication skills with a history of cross-functional collaboration with product, design, and engineering stakeholders

Job Responsibility

Architect and scale machine learning systems for search, personalization, and recommendations that power Kiddom’s teacher helper and insight engine
Develop evaluation-first development workflows to measure how models improve teaching efficiency, lesson planning, and student learning outcomes
Fine-tune machine learning models with feedback signals from teachers and students to align outputs with instructional goals and classroom needs
Design intelligent discovery pipelines that combine semantic retrieval, curriculum alignment, and real-time personalization
Build agentic assistants that help teachers plan lessons, adapt instruction, and reduce repetitive tasks
Collaborate closely with product managers, designers, and curriculum experts to translate high-level educational goals into scalable ML-powered systems
Coach and mentor junior ML engineers and data scientists, fostering technical and professional growth

What we offer

Meaningful equity
Health insurance benefits: medical (various PPO/HMO/HSA plans), dental, vision, disability and life insurance
One Medical membership (in participating locations)
Flexible vacation time policy (subject to internal approval). Average use 4 weeks off per year
10 paid sick days per year (pro rated depending on start date)
Paid holidays
Paid bereavement leave
Paid family leave after birth/adoption. Minimum of 16 paid weeks for birthing parents, 10 weeks for caretaker parents. Meant to supplement benefits offered by State
Commuter and FSA plans

Fulltime

New

Senior Software Engineer (Search / Retrieval)

Workato transforms technology complexity into business opportunity. As the leade...

Location

United States , Palo Alto

Salary:

Not provided

Workato

Expiration Date

Until further notice

Requirements

Bachelors/Masters/PhD degree in Statistics, Mathematics or Computer Science, or another quantitative field
7+ years of backend engineering experience with 3+ years in search, information retrieval, or related fields
Strong proficiency in Python
Hands-on experience with search engines (Opensearch or Elasticsearch)
Strong understanding of information retrieval concepts spanning traditional methods (TF-IDF, BM25) and modern neural search techniques (vector embeddings, transformer models)
Experience with text processing, NLP, and relevance tuning
Experience with relevance evaluation metrics (NDCG, MRR, MAP)
Experience with large-scale distributed systems
Strong analytical and problem-solving skills
Strong communication abilities to explain technical concepts

Job Responsibility

Lead the design, development, and optimization of intelligent search systems that leverage machine learning at their core
Build end-to-end retrieval pipelines that incorporate advanced techniques in query understanding, ranking, and entity recognition
Lead the development of advanced our search cluster that can scale to millions of documents across customers and data sources
Deploy learning-to-rank models that optimize relevance using behavioral signals, embeddings, and structured feedback
Build and scale robust Entity Recognition pipelines that enhance document understanding, enable contextual disambiguation, and support entity-aware retrieval
Architect next-gen search infrastructure capable of supporting highly dynamic document corpora and real-time indexing
Drive improvements in query construction, indexing and search performance
Be up-to-date with the latest improvements in search and indexing technologies
Collaborate with product and applied research teams to translate user needs into data-informed search innovations
Produce clean, scalable code and influence system architecture and roadmap across the relevance and platform stack

New

Senior Product Manager, Data & Retrieval

Harvey is building the AI platform for the world’s top legal and professional se...

Location

United States , San Francisco

Salary:

178500.00 - 241500.00 USD / Year

Harvey

Expiration Date

Until further notice

Requirements

5+ years of experience building or managing search, retrieval, recommendation, or data platforms at scale
Experience working with complex, heterogeneous, or domain-specific datasets with structured + unstructured data
Understanding of modern retrieval methods, including hybrid search (lexical + vector), dense retrieval, re-ranking, embeddings, chunking strategies, and index optimization
Hands-on experience with LLMs or RAG frameworks (evaluation, grounding, hybrid pipelines, query rewriting, LLM-as-a-judge, retrieval metrics)
Ability to partner with engineers on technical architecture, with enough depth to challenge assumptions, propose solutions, and influence design
A product mindset for search—balancing user needs, domain complexity, and system constraints to propose high-leverage improvements

Job Responsibility

Drive the roadmap and strategy for Harvey’s “Data Factory”, ensuring we scale our data 100x through new platforms that build the ‘legal index’ of the world
Work with internal operations and external data providers to methodically expand coverage, accelerate execution, and improve dataset quality
Own and evolve Harvey’s end-to-end data architecture—from ingestion and transformation to storage, indexing, and retrieval—ensuring performance, reliability, and scalability for LLM-powered products
Partner with Applied AI engineers to build and optimize retrieval systems, embeddings, search models, and evaluation frameworks
Architect and oversee large-scale ingestion pipelines that aggregate, normalize, and continuously update millions of heterogeneous legal documents across global jurisdictions
Collaborate cross-functionally with Product Engineering, Applied AI, Research, and Platform teams to deliver high-quality production systems that support reasoning, summarization, and legal research workflows

What we offer

Comprehensive health, dental and vision coverage
retirement benefits (401k match up to 4%)
flexible PTO
equity plan
bonus

Fulltime

Seo / Aeo Lead

At PhantomBuster, we are looking for a forward-thinking SEO/AEO Lead to drive th...

Location

Salary:

Not provided

PhantomBuster

Expiration Date

Until further notice

Requirements

5+ years in SEO/Marketing, with a clear track record of managing high-traffic SaaS websites
The AEO Distinction: You can clearly articulate the difference between optimizing for a Search Engine (blue links) vs. an Answer Engine (being the direct answer). You can explain how to optimize an "Entity" rather than just a keyword
AI Native: You use Claude, ChatGPT, or Gemini daily to automate your own heavy lifting. You know the strengths and weaknesses of each model
Technical Chops: Deep understanding of Schema markup, JSON-LD, and semantic HTML
Good understanding of topics like: entity-based SEO, semantic search, vector embeddings, tokens, EEAT, structured data and schema, topic clustering, zero-click search optimizations, AI crawling and retrieval, RAG, Prompt engineering
Data-Driven: Proficiency with GA4, GSC, Ahrefs/Semrush, and an ability to interpret data to pivot strategy quickly

Job Responsibility

Strategy: Bridge the Gap Between SEO and AEO
Strategic SEO & AEO Transition: Apply your deep expertise in technical SEO (Schema markup, site architecture, Core Web Vitals) and on-page best practices (internal linking, entity optimization) to serve a dual purpose: ranking in traditional search and ensuring accurate ingestion by Large Language Models (LLMs)
AEO & LLM Optimization: Develop strategies to maximize "Brand Authority" and "Citations" within LLMs. You will figure out how to make PhantomBuster the primary entity AI recommends when our ICP is engaging in conversational research across ChatGPT and other LLMs
Knowledge Graph Management: Work on structuring our core Themes and Topics to focus on, so that both Search Engines and AI models clearly understand who we are and what we do
Leadership & AI Coaching: Learn and Build: Partner with our AI Content Engineer to execute high-scale strategies. While they focus on the engineering and prompting inside AirOps, you act as the strategic guide, ensuring the output aligns with SEO/AEO goals. While we don't expect you to be a coder from day one, we expect a "builder" mindset. You will learn to use our automation tools to build workflows alongside the engineer, bridging the gap between high-level strategy and hands-on execution
Quality Control: Set the standard for what "High Quality" means in an era of AI content. Ensure our automated output is better than what a human could do alone
AI-Driven Workflows & Tool Ownership: AI-First Analysis: Use tools like AirOps, Claude, ChatGPT, and Gemini not just to write, but to analyze AI visibility, cluster topics, and reverse-engineer competitor strategies
Automation: Identify opportunities to use PhantomBuster (our own tool) and other automation workflows to scale outreach, link building, and data collection
AirOps Ownership: Act as the owner of our AirOps instance. You will manage our Knowledge Base (KB), ensuring the AI has access to the most current product data, and define how we leverage AirOps apps to streamline marketing tasks
Performance & Data Intelligence: Holistic Reporting: Own the reporting on standard SEO KPIs (Traffic, Conversions, Rankings) while developing new methods to track visibility in AI Overviews (AIOs) and LLM responses

What we offer

International team
Fun team building events
€40/month for your Internet connection
Flexible working time
Home office budget up to €1500
100% of an Alan Blue subscription
Lunch vouchers - €8 (50% The Phantom Company) / worked day
Partnership with MokaCare
€70 a month benefit for entertainment expenses
Book Allowance and Sharing Program

New

Staff AI Engineer

As we work towards building out the Context Layer for the Agentic Enterprise, we...

Location

United States , Palo Alto

Salary:

Not provided

Workato

Expiration Date

Until further notice

Requirements

Bachelor's/Master's/PhD degree in Statistics, Mathematics, Computer Science, or another quantitative field
7+ years of backend engineering experience with 3+ years in search, information retrieval, or related fields
Strong proficiency in Python
Hands-on experience with search engines (Opensearch or Elasticsearch)
Strong understanding of information retrieval concepts spanning traditional methods (TF-IDF, BM25) and modern neural search techniques (vector embeddings, transformer models)
Experience with text processing, NLP, and relevance tuning
Experience with relevance evaluation metrics (NDCG, MRR, MAP)
Experience with large-scale distributed systems
Proficiency in Knowledge Graph construction and optimization is a plus
Strong analytical and problem-solving skills

Job Responsibility

Lead the development of advanced query understanding systems that parse natural language, resolve ambiguity, and infer user intent
Design and deploy learning-to-rank models that optimize relevance using behavioral signals, embeddings, and structured feedback
Build and scale robust Entity Recognition pipelines that enhance document understanding, enable contextual disambiguation, and support entity-aware retrieval
Architect next-gen search infrastructure capable of supporting highly dynamic document corpora and real-time indexing
Create and maintain graph-based knowledge systems that enhance LLM capabilities through structured relationship data
Drive improvements in query rewriting, intent classification, and semantic search, using both statistical and neural methods
Own the design of evaluation frameworks for offline/online relevance testing, A/B experimentation, and continual model tuning
Collaborate with product and applied research teams to translate user needs into data-informed search innovations
Produce clean, scalable code and influence system architecture and roadmap across the relevance and platform stack

What we offer

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

New

Director of Product, Search & Discovery

We are building a new Search & Discovery product organization at Scribd.com, and...

Location

United States; Canada; Mexico , San Francisco; Atlanta; Austin; Boston; Chicago; Dallas; Denver; Houston; Jacksonville; Los Angeles; Miami; New York City; Phoenix; Portland; Sacramento; Salt Lake City; San Diego; Seattle; Washington, D.C.; Ottawa; Toronto; Vancouver; Mexico City

Salary:

142500.00 - 275000.00 USD / Year

Scribd

Expiration Date

Until further notice

Requirements

10+ years of product management experience
5+ years managing PM teams or leading multi-disciplinary product groups
Demonstrated success owning discovery, personalization, search, or ranking experiences at scale in a consumer technology environment
Deep technical fluency in modern ML and AI systems, including retrieval and ranking, semantic embeddings, LLMs, retrieval augmented generation, feature stores, model evaluation, and real-time personalization
A strong track record of translating user needs, data insights, and competitive trends into breakthrough discovery features, ideally including ML or LLM powered experiences
Proven ability to lead through ambiguity, make strategic tradeoffs, and balance long-term platform investments with near-term business results
Exceptional written, visual, and verbal communication skills, including experience presenting to executives and cross-functional partners
Experience running large-scale A/B tests, user research, and multi-surface experimentation programs
Bachelor’s degree in a technical or related field (Computer Science, Engineering, Mathematics, HCI) or equivalent experience

Job Responsibility

Set the multi-year Discovery strategy
Define and drive the long-term vision for Scribd’s discovery ecosystem, including recommendations, search, ranking, and semantic content understanding
Articulate how we move from document-level retrieval to knowledge-level discovery using foundational models and LLMs
Align this vision with company priorities in engagement, retention, and subscription growth, balancing foundational ML investments with high-impact product improvements
Lead and grow a world-class product organization
Serve as the founding product leader for the Search & Discovery group, managing, mentoring, and inspiring a team of product managers across discovery surfaces and platforms
Recruit and develop top PM talent, set a high bar for product craft, and build a culture that combines deep technical curiosity with strong user empathy
Establish durable cross-functional operating rhythms with Engineering, ML, Data Science, Design, Content, and Trust & Safety
Build next generation discovery systems using LLMs and ML
Oversee the evolution of Scribd’s personalization and retrieval stack, combining semantic embeddings, behavioral signals, hybrid retrieval, and LLM-powered reasoning

What we offer

Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
12 weeks paid parental leave
Short-term/long-term disability plans
401k/RSP matching
Onboarding stipend for home office peripherals + accessories
Learning & Development allowance
Learning & Development programs
Quarterly stipend for Wellness, WiFi, etc.
Mental Health support & resources
Free subscription to the Scribd Inc. suite of products

Fulltime

Applied Scientist 2

We’re building the next-generation Grounding Service that powers the latest AI a...

Location

China , Beijing

Salary:

Not provided

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research)
OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
OR equivalent experience.
Minimum of 2 years of hands-on experience designing and building search, retrieval, or ranking systems.
Proven track record of shipping LLM-powered or Retrieval-Augmented Generation (RAG) systems into production environments.
Solid coding skills and solid foundation in machine learning, with the ability to implement and optimize models effectively.
Demonstrated ability to lead through ambiguity, make principled trade-offs, and deliver measurable impact in cross-functional, fast-paced settings.

Job Responsibility

Owns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact. Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images.
Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust. Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs.
Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web. Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems.
Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology. Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property.
Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs. Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively.
Identifies and solves significant business problems using novel, scalable, and data-driven solutions. Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work.
Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review. Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols.
Communicates clearly through design documentation, progress updates, and presentations to executives and customers. Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.

Fulltime

Research Engineer, Retrieval & Search, Applied Engineering

OpenAI

Location:
United States , San Francisco

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Additional Information:

Job Posted:
February 21, 2026

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