This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Key Responsibilities

Customer Integration & Deployment

• Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
• Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
• Deploy and configure AI models and agents within customer security and compliance boundaries

AI Agent Development

• Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
• Architect multi-agent systems that orchestrate between different models, tools, and data sources
• Implement evaluation frameworks to measure agent performance and iterate toward business objectives
• Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement

Prompt Engineering & Optimization

• Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
• Build and maintain prompt libraries, templates, and best practices for customer use cases
• Conduct systematic prompt experimentation and A/B testing to improve model outputs
• Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate

Technical Leadership & Collaboration

• Serve as the primary technical point of contact for strategic enterprise accounts
• Collaborate with customer data scientists, ML engineers, and software developers to ensure smooth integration
• Provide technical training and knowledge transfer to customer teams
• Work closely with Scale's product and engineering teams to translate customer needs into product improvements
• Document technical architectures, integration patterns, and best practices

Problem Solving & Innovation

• Debug complex technical issues across the entire stack, from data pipelines to model outputs
• Rapidly prototype solutions to unblock customers and prove out new use cases
• Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
• Identify opportunities for productization based on common customer patterns

Required Qualifications

• 4+ years of software engineering experience with strong fundamentals in data structures, algorithms, and system design
• Production Python expertise with experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
• Strong problem-solving skills with the ability to navigate ambiguous requirements and rapidly iterate toward solutions
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences

Preferred Qualifications

• Agent Development Wiz
• Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
• Experience building and deploying AI agents or autonomous systems in production
• Knowledge of vector databases and semantic search systems
• Contributions to open-source AI/ML projects

• Infrastructure Guru
• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
• Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
• Previous work in a devops, platform, or infra role

• Customer Product Whisperer
• Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
• Domain expertise in verticals like finance, healthcare, government, or manufacturing
• Experience with technical enablement or teaching programs

Sample Projects

The following are some examples of the types of projects we’ve worked on with customers. All of these projects leverage customer data, integrate directly into customers’ existing systems, and are deployed on their infrastructure.

• Deep Research for Due Diligence
• Churn Prediction
• Data Extraction Voice Agent

Compensation

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Pay Transparency

For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $216,000-$270,000 USD

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Responsibilities

• Own major full-stack product areas, driving features from design through production deployment.
• Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness.
• Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components.
• Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences.
• Deliver experiments and new features quickly, maintaining high quality and tight feedback loops with customers.
• Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP.
• Adapt quickly,learning new technologies, frameworks, and tools as needed across the stack.

Ideal Experience

• 5+ years of full-time engineering experience, post-graduation.
• Strong experience developing full-stack applications using React, TypeScript, and Python.
• Experience scaling or shipping products at high-growth startups.
• Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome).
• Proficiency with SQL and modern API development.
• Experience with Kubernetes, containerization, and microservice architectures.
• Experience working with at least one major cloud provider (AWS, GCP, or Azure).

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

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We're looking for a Senior Gameplay Programmer with a Unity background to help us implement our new music course. You will help build the best possible user experience for our learners through high-quality interactive lessons.

Responsibilities:

• Implement the new music course within the Duolingo app using embedded Unity.
• Productionize the existing prototype, turning it into robust, release-ready code.
• Work embedded within the Duolingo app architecture to ensure seamless integration of Unity.
• Collaborate with designers and product partners to refine the look and feel, ensuring a high-quality, gamified user experience.
• Proactively identify issues and opportunities to improve the learner experience.
• Engage in rapid prototyping to test new music mechanics and interaction models.
• Ensure technical mistakes or inconsistencies are fixed as quickly as possible.

Requirements:

• Strong experience with Unity development (C#), specifically for mobile platforms (iOS/Android).
• Background in the gaming industry with a portfolio demonstrating high-quality game feel and polish.
• Sharp UX instincts and experience implementing complex UI/UX designs.
• Strong business sense, with proven experience working effectively with multi-functional team members (e.g. engineers, product, and design).
• Thrives in a fast-paced, data-driven, team-oriented environment.
• Strong interest in Duolingo and our mission.

Exceptional candidates will have:

• Experience embedding Unity as a library (UaaL) into native mobile applications.
• Background in music, music theory, or audio programming.
• Experience in web/mobile localization workflows and tools.
• Familiarity with mobile gaming, with an understanding of game mechanics.

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Our team works on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You'll work on complex, real-world ML problems at massive scale, and contribute to technical strategy, architecture, and long-term ML roadmap.

Responsibilities:

• Design, build, and deploy production-grade machine learning models and systems at scale
• Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
• Build scalable data and model pipelines with strong reliability, observability, and automated retraining
• Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems
• Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions
• Improve system performance across latency, throughput, and model quality metrics
• Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment

Basic Qualifications:

• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
• ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
• Experience improving measurable metrics through applied machine learning

Preferred Qualifications:

• Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems
• Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
• Experience working with real-time systems and low-latency production environments
• Background in feature engineering, model optimization, and production monitoring
• Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
• Advanced degree in Computer Science, Machine Learning, or related quantitative field

Potential Teams:

• Ads Measurement Modeling
• Ads Targeting and Retrieval
• Advertiser Optimization
• Ads Marketplace Quality
• Ads Creative Effectiveness
• Ads Foundational Representations
• Ads Content Understanding
• Ads Ranking
• Feed Relevance
• Search and Answers Relevance
• ML Understanding
• Notifications Relevance

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

Pay Transparency:

This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is: $185,800-$260,100 USD

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Your responsibilities will include:

• Designing, building, and deploying production-grade machine learning models and systems at scale
• Owning the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
• Building scalable data and model pipelines with strong reliability, observability, and automated retraining
• Working with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems
• Partnering cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions

You'll work on a wide range of high-impact problems across the Reddit ecosystem, including recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, and multimodal embedding systems.

To be successful in this role, you'll need:

• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
• ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions

Preferred qualifications include experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems, familiarity with distributed systems and large-scale data processing, and experience working with real-time systems and low-latency production environments.

At Reddit, we're committed to building a workforce representative of the diverse communities we serve. We're proud to be an equal opportunity employer and are committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.

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You'll work with enterprise clients to understand their unique challenges, lead a team that architects specific AI solutions, and ensure successful deployment and adoption of AI systems in production environments.

This is a Management role that combines deep engineering and AI expertise, leading a team, and working on customer-facing problems. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Customer Integration & Deployment

Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements.

Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs).

Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows.

Deploy and configure AI models and agents within customer security and compliance boundaries.

AI Agent Development

Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation.

Architect multi-agent systems that orchestrate between different models, tools, and data sources.

Implement evaluation frameworks to measure agent performance and iterate toward business objectives.

Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement.

Prompt Engineering & Optimization

Create sophisticated prompt engineering strategies optimized for customer-specific domains and data.

Build and maintain prompt libraries, templates, and best practices for customer use cases.

Conduct systematic prompt experimentation and A/B testing to improve model outputs.

Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate.

Leadership & Collaboration

Serve as the Engineering Manager and technical point of contact for strategic enterprise accounts.

Lead a team that is collaborating with customer data scientists, ML engineers, and software developers to ensure smooth integration.

Work closely with Scale's product and engineering teams to translate customer needs into product improvements.

Document technical architectures, integration patterns, and best practices.

Problem Solving & Innovation

Debug complex technical issues across the entire stack, from data pipelines to model outputs.

Rapidly prototype solutions to unblock customers and prove out new use cases.

Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems.

Identify opportunities for productization based on common customer patterns.

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The role is ideal for a senior modeler who also enjoys developing, productionizing models and leading technical direction across teams. The successful candidate will be able to provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.

In addition to the above responsibilities, the successful candidate will be expected to:

• Collaborate with infra/platform teams to ensure scalable, reliable training/serving (latency, cost, observability, rollout safety).
• Partner with signal-consuming teams (ranking, retrieval, integrity, ads) to define signal contracts, adoption patterns, and success metrics.
• Own the full ML lifecycle: data/labeling strategy (human labels + weak supervision), training pipelines, offline evaluation, online experimentation, deployment, and monitoring/retraining.
• Provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.

Nice to have: experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring; familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.

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In this role, you will own the quality of results for two distinct but deeply connected 'users': the AI Agent and the Human User. You will optimize the retrieval layer that allows Large Language Models (LLMs) to reason over data they weren't trained on, ensuring they have the 'ground truth' needed to synthesize accurate, high-stakes business actions. You will also improve the traditional search experience so employees can find assets and answers through intuitive, high-recall interfaces.

This is a challenging problem that requires balancing traditional keyword-based search with semantic vector search. You will fine-tune ranking models that satisfy both human readability and LLM-ready context. You will also connect the dots across structured SQL tables, unstructured docs, and real-time business metrics.

If you want to solve the hardest problems in data discovery and see your work power the decision-making of thousands of people, let's talk.

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Our team is responsible for building systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You will work on high-impact systems that improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems.

As a Senior Machine Learning Engineer, you will:

• Design, build, and deploy production-grade machine learning models and systems at scale
• Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring
• Build scalable data and model pipelines with strong reliability, observability, and automated retraining
• Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems
• Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions
• Improve system performance across latency, throughput, and model quality metrics
• Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment

Basic Qualifications:

• 3-5+ years of experience building, deploying, and operating machine learning systems in production
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals
• ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)
• Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)
• Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure
• Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions
• Experience improving measurable metrics through applied machine learning

Preferred Qualifications:

• Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems
• Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)
• Experience working with real-time systems and low-latency production environments
• Background in feature engineering, model optimization, and production monitoring
• Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale
• Advanced degree in Computer Science, Machine Learning, or related quantitative field

Potential Teams:

• Ads Measurement Modeling
• Ads Targeting and Retrieval
• Advertiser Optimization
• Ads Marketplace Quality
• Ads Creative Effectiveness
• Ads Foundational Representations
• Ads Content Understanding
• Ads Ranking
• Feed Relevance
• Search and Answers Relevance
• ML Understanding
• Notifications Relevance

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

Pay Transparency:

This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/. To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is $216,700-$303,400 USD

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The ideal candidate will have a strong background in information retrieval, machine learning, and software engineering. You will be working on a highly complex and challenging problem, balancing traditional keyword-based search with semantic vector search, and fine-tuning ranking models to satisfy both human readability and LLM-ready context.

Responsibilities:

• Own the quality of results for our AI agents and human users
• Optimize the retrieval layer for LLMs to reason over data they weren’t trained on
• Improve the traditional search experience for employees to find assets and answers through intuitive, high-recall interfaces
• Build the guardrails and relevance scoring to ensure our AI stays grounded in reality

Requirements:

• Strong background in information retrieval, machine learning, and software engineering
• Experience with Lucene/Elasticsearch, embeddings, and ranking algorithms
• Understanding of relevance metrics (nDCG, MRR, Precision@K)
• Ability to work on a highly complex and challenging problem

Benefits:

• Comprehensive benefits and perks that meet the needs of all employees
• Opportunity to work on a highly complex and challenging problem
• Collaborative and dynamic work environment

About Databricks:

Databricks is a data and AI company that provides a platform for unifying and democratizing data, analytics, and AI. Over 10,000 organizations worldwide, including many Fortune 500 companies, rely on Databricks.

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The Payments AI/ML Foundation team builds the shared platforms, services, and guardrails that enable product and data teams across Airbnb Payments to deliver trustworthy, efficient, and scalable AI capabilities.

As a Senior Software Engineer on Payments AI/ML Foundation, you will:

• Design and own core platform components that power AI across Airbnb Payments
• Partner with product, infra, data science, and operations to translate ambiguous requirements into robust systems with clear SLAs
• Build tooling and automation that makes AI development safer and faster
• Raise the bar on reliability, performance, and governance for models and agents in production

A typical day for this role will involve envisioning, championing, and supporting the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems.

You will work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for AI/ML models, drive engineering decisions, and quantify impact.

Hands-on productionize and operate AI/ML solution and pipelines at scale, including both batch and real-time use cases.

Lead, mentor, challenge and grow enthusiastic, collaborative AI/ML culture within the organization.

Your expertise should include:

• A degree in Computer Science or equivalent qualification
• 7+ years of industry experience in backend/platform engineering (or equivalent), including ownership of production systems
• Strong programming skills in Python, plus solid data engineering foundations
• Good to have experience with AI frameworks (Langraph, Strand etc), orchestration (Airflow/Kubeflow), and streaming/processing (Kafka/Spark/Ray)
• Proven track record building observability for AI systems (metrics/logging/traces), with automated alerting, dashboards, and SLO management
• Experience designing model governance and safety guardrails (prompt/version control, red-teaming, policy checks, approvals)
• Demonstrated ability to drive performance/cost optimizations (profiling, batching, caching, quantization, autoscaling) in high-traffic environments
• Practical familiarity with vector search/embedding pipelines (indexing strategies, consistency, reindexing, retention)

Our commitment to inclusion and belonging:

Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions.

All qualified individuals are encouraged to apply.

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With tools that amplify your impact and a culture that backs your ambition, you won't just contribute. You'll make things happen–fast.

The Opportunity:

ZoomInfo built the industry's most sophisticated GTM data acquisition infrastructure. Now we're applying that same rigor to context engineering,the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement.

This role architects the context layer powering our AI intelligence across Copilot, GTM Studio, and MarketingOS. You'll transform how ZoomInfo's agentic workflows access, compress, and deliver precisely the right information at exactly the right moment.

The impact is organization-wide: every AI interaction, every intelligent recommendation, every autonomous agent action depends on the context infrastructure you’ll build.

We've transitioned to AI-first product thinking company-wide. The context pipelines exist but remain nascent,creating a rare opportunity to define architectural patterns and platform standards that compound value across multiple product teams in the years to come.

What You'll Do:

Architect Context Acquisition Pipelines

Design and optimize how ZoomInfo retrieves, transforms, and delivers context from our semantic data layer, memory systems, and data producers. You'll balance retrieval quality against latency and cost constraints, implementing hybrid search strategies, intelligent caching, and context compression techniques that maintain information density while respecting token budgets.

Own the Context Layer Platform

Build infrastructure serving multiple product teams,Copilot, GTM Studio, MarketingOS,as internal customers. Establish API contracts, developer experience standards, and integration patterns that accelerate feature velocity.

Maintain the delicate balance between providing flexible building blocks and opinionated solutions that encode best practices.

Drive Quality Through Measurement

Implement evaluation frameworks using RAGAS metrics and custom benchmarks. Monitor retrieval precision, context relevance, hallucination rates, and system performance in production.

Translate quality signals into architectural improvements, working closely with ML engineers to iterate on embedding models, reranking strategies, and retrieval algorithms.

Navigate Emerging Research

Context engineering evolves weekly. You'll continuously evaluate innovations,GraphRAG for multi-hop reasoning, test-time compute scaling, multimodal retrieval, compression techniques,determining which advances warrant production investment versus which remain academic curiosities.

Bring external best practices to ZoomInfo while contributing learnings back to the broader community.

Orchestrate Cross-Functional Execution

Translate between three distinct worlds: ML engineers optimizing retrieval algorithms, platform engineers building scalable infrastructure, and product teams shipping customer features.

Establish communication cadences, prioritization frameworks, and decision-making processes that balance urgent requests against strategic platform development.

What You’ll Bring:

• 4-6 years of product management experience with 2+ years in ML/AI infrastructure

• Direct experience with production RAG systems, vector databases, or semantic search, context management

• Experience with graph databases (e.g. Neo4j)

• Track record building platform products serving multiple internal or external customers

• Familiarity with context compression, embedding models, and retrieval evaluation frameworks

• History of defining product vision in nascent technical domains where best practices are still emerging

Who You Are:

Technical Foundation

Expert-level understanding of RAG system architecture,you can discuss embedding dimensionality trade-offs, vector database indexing strategies, and reranking approaches with depth.

You've built or significantly contributed to production retrieval systems, not just managed them at arm's length.

Python and SQL proficiency enables you to review code, analyze retrieval issues, and prototype solutions for concept validation.

Platform Product Mindset

Experience building infrastructure products where internal engineering teams are your customers.

You measure success through downstream product velocity improvements and developer satisfaction scores, not just uptime metrics.

You understand platform economics,how each additional team using your infrastructure increases its value through shared learnings and amortized costs.

Intellectual Velocity

You read recent research papers from arXiv, ACL, NeurIPS.

You prototype emerging techniques to understand their practical constraints.

You maintain strong opinions weakly held, updating your architectural assumptions as evidence accumulates.

The discipline moves too fast for static expertise,continuous learning is non-negotiable.

Strategic Communication

You translate between technical depth and business impact fluently.

You can explain to executives why implementing GraphRAG takes 6 months but unlocks $10M in product capabilities.

You can communicate to engineers why business constraints require shipping 'good enough' in 3 weeks rather than 'optimal' in 3 months.

You influence without formal authority through data, clear reasoning, and earned credibility.

The Environment:

Reporting & Collaboration

Report to the Senior Product Director for Context Engineering, Semantic Data Layer, and Agentic Memory within ZoomInfo's Intelligence team.

Work alongside PMs responsible for signals and ML scoring/recommendation models.

Together, you ensure our agentic workflows fill context windows with high-quality, information-dense content exactly when needed.

Pace & Problems

Fast-moving engineering team that understands the space.

Company-wide AI adoption push creates both urgency and opportunity.

Expect interesting problems: How do we maintain sub-200ms retrieval latency at scale?

When does GraphRAG justify its indexing cost?

How do we balance context freshness with cache efficiency?

You'll shape answers that become architectural patterns across the organization.

Impact

Define a nascent discipline at a company that's already AI-first in product thinking and organizational structure.

Your architectural decisions compound,every improvement to context quality multiplies across Copilot, GTM Studio, MarketingOS, and future products we haven't imagined yet.

This is infrastructure work with direct line-of-sight to customer value.

#LI-PS1 #LI-remote

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As a Staff Software Engineer for Search Quality, you will drive the technical direction of ranking, relevance, evaluation, and quality initiatives across Databricks' next-generation Search product. You'll design and build the systems, models, and evaluation frameworks that ensure our Search stack delivers accurate, high-quality results across diverse multimodal datasets and query patterns.

The impact you will have:

• Lead the technical vision for Search Quality, shaping the ranking architecture, relevance modeling stack, and evaluation systems that power Databricks' next-generation retrieval experiences.

• Identify and solve challenges in ranking, query understanding, and hybrid retrieval , advancing state-of-the-art techniques in vector, keyword, and multimodal search.

• Design and train production-ready ranking and reranking models with strong guarantees around quality, latency, and resource efficiency.

• Partner closely with research, product, and infra teams to define metrics, evaluation methodologies, and experimentation strategies for new retrieval features and model architectures.

• Drive end-to-end engineering efforts , from early prototyping to production rollout , ensuring correctness, reliability, and measurable improvements to relevance.

• Build and operate resilient, low-latency services for ranking, evaluation, and relevance signal processing.

• Champion excellence in ML and search engineering, mentoring teammates and elevating design, code quality, and scientific rigor across the team.

• Shape Databricks' long-term roadmap for retrieval quality, ranking infrastructure, and the foundations for retrieval-driven AI products.

What we look for:

• 10+ years of experience building large-scale search, ranking, recommendation, or ML-driven relevance systems.

• Deep expertise in Search Quality, including ranking models, signals, query understanding, and evaluation methodologies.

• Strong understanding of relevance metrics and evaluation frameworks.

• Familiarity with vector search, keyword search, hybrid retrieval, and embedding-based semantic retrieval.

• Solid foundation in algorithms, data structures, and system design for performance-critical ranking and retrieval systems.

• Proven ability to deliver high-impact technical initiatives with clear business or product outcomes.

• Strong communication skills and ability to collaborate across teams in fast-moving environments.

• Strategic and product-oriented mindset with the ability to align technical execution with long-term vision.

• Passion for mentoring, growing engineers, and fostering technical excellence.

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This role goes beyond building models; it's about designing agentic systems, AI-assisted workflows, and data-driven decision engines that help the company scale faster than headcount.

You will be at the forefront of developing the next generation of autonomous systems that power both customer-facing products and internal operations, from optimizing payment experiences to accelerating development, testing, troubleshooting, and product creation.

Responsibilities:

• Architect, build, and deploy LLM-powered applications that augment and automate key workflows across Yuno, from customer service and new product development to internal agentic workflows that optimize critical company processes.

• Design autonomous AI systems that can execute technical analysis, testing, troubleshooting, and decision-making at scale.

• Develop AI-driven tools that create measurable business impact, improving efficiency, accelerating innovation, and driving revenue growth.

Operational Intelligence & Scalability:

• Identify areas across Yuno where AI can generate efficiency gains, reducing manual work, optimizing processes, and enabling smarter operations.

• Build AI systems that enable Yuno to scale without exponential growth in headcount, thereby creating sustainable productivity and organizational intelligence.

Data & Model Optimization:

• Lead fine-tuning and contextual optimization of AI models using Yuno's proprietary data.

• Continuously refine performance through structured feedback loops, observability metrics, and user interaction data.

• Ensure adherence to privacy, compliance, and ethical AI principles in all model development.

Cross-Functional Collaboration & Innovation:

• Work closely with data, product, and infrastructure teams to define the long-term AI roadmap for Yuno.

• Experiment with emerging AI technologies to identify new capabilities that can drive differentiation and strategic advantage.

• Contribute to a culture of experimentation, rapid iteration, and continuous learning across the AI function.

The skills you need:

• 5+ years of professional experience in AI/ML development, with at least 2 years focused on LLMs, RAG, or agentic systems.

• Strong engineering and product mindset, capable of balancing technical depth with strategic impact.

• Exceptional communication and collaboration skills to work across product, engineering, and data functions.

• Passion for pushing the boundaries of applied AI and creating systems that drive real business transformation.

• Willing to work from the Hyderabad office.

Minimum Qualifications:

• LLMs & RAG: Proven experience designing and deploying systems using models like GPT, Claude, Gemini, or similar, including Retrieval-Augmented Generation (RAG) pipelines and contextual retrieval systems.

• AI Agents & Multi-Agent Systems: Hands-on experience with Crew.ai, LangChain, LangGraph, or similar frameworks to build orchestrated agentic workflows.

• Fine-Tuning & Context Engineering: Expertise in supervised fine-tuning (SFT), LoRA, or custom dataset adaptation for domain-specific tasks.

• Programming & Frameworks: Proficiency in Python, GO, and AI/ML libraries such as PyTorch, TensorFlow, or JAX.

• AI Infrastructure: Experience designing scalable, production-ready AI systems in AWS, GCP, or Azure, with a deep understanding of vector databases, model serving, and inference optimization.

• Observability & Monitoring: Familiarity with LangSmith, LangFuse, or equivalent tools for tracking, debugging, and evaluating LLM performance.

• API Integration: Expertise in integrating AI systems with RESTful APIs and internal platforms to create seamless, usable products.

Preferred Qualifications:

• Experience using LlamaIndex, Hugging Face, or similar open-source frameworks.

• Strong understanding of context optimization, embedding strategies, and knowledge retrieval.

• Familiarity with MLOps, CI/CD pipelines, and production deployment best practices.

• Prior experience in fintech, payments, or other data-intensive and regulated industries.

• Analytical rigor and ability to translate business objectives into measurable AI outcomes.

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We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid's network covers 12,000 financial institutions across the US, Canada, UK and Europe.

The AI Applications Team You will have the opportunity to join as one of the founding members of this newly formed team that is dedicated to consolidating and rapidly scaling our successful bets so far, and grow with the team in our quest to accelerate Plaid's transformation into an AI-first company.

In this role you will lead projects that enable and scale our business with our largest AI customers and partners, starting with personal finance use cases and expanding into many others; examples include:

• Develop and evolve the preferred integration pattern for Plaid with AI providers - from API adaptations to building the official Plaid MCP Servers, and beyond

• Redefine how Plaid's consumer link experience embed into conversational interfaces in the most seamless way

• Architect the trust layer for the future of agentic commerce that will become the industry standard

Additionally you will be expected to scale and extend our existing successful bets on AI-powered customer experience; examples include:

• Make the next step-function improvement in our homegrown customer support agent

• Land our multi-turn and multi-agent system that powers a truly delightful experience for our customers; define how to scalably run offline evaluation for complex multi-turn open-ended tasks; research and prototype how Human-In-The-Loop - Reinforcement Learning (RLHF) can power an insights flywheel; pioneer the architecture for customer-specific long-term memory, etc.

• Extend our agentic system to support other critical parts of the customer journey, starting with areas with the highest ROI - top-of-funnel product recommendation, customer onboarding and risk diligence, customer activation and assistance for faster productionization, as well as upselling and cross-selling of Plaid products

You will have a front row seat to all the latest industry developments. Over time, with the skills and experience you develop and hone on this team, you can become an influential voice in defining where AI < Fintech will be heading longer term.

Responsibilities

• Build across the stack. Design, develop, and maintain scalable backend services and APIs, as well as intuitive, high-quality frontend applications that bring those systems to life.

• Work with other AI engineers, software engineers and machine learning engineers to architect, design and implement GenAI-powered products and features

• Collaborate across functions to understand user needs, propose and implement AI-powered solutions where they’re expected to have the highest impact

• Design and execute rapid experiments to push the boundaries on potential business impact from emerging AI capabilities, with a focus on minimal viable testing approaches

• Balance creative exploration of possibilities with rigorous evaluation of technical feasibility, product potential and business impact

Requirements

• Experience building backend services and working with microservices or service-oriented architectures

• Strong working knowledge of HTML, CSS, JavaScript, and modern frontend frameworks or libraries, with comfort building user-facing experiences

• Hands-on experience working with LLMs to build products and shipping them to product with iterating with real user feedback - including but not limited to:

• Prompt engineering

• Fine-tuning

• Retrieval augmented generation (RAG)

• Semantic search

• Vector database and embedding models

• Agent orchestration framework

• Evaluation and monitoring framework of open-ended tasks

• Streaming and SSE

• Common UX and design patterns for GenAI-powered products

• Strong debugging and monitoring experience for production systems

• Ability to deeply understand customer and user needs through user research and rapid experimentation - be your own technical PM

• Ability to balance divergent thinking (exploring possibilities) with convergent thinking (evaluating feasibility), which is critical for driving 0 ->1 projects

• Extremely curious and passionate about working in GenAI applications space

Nice-to-Haves

• Experience training and/or serving ML models in production, or fine-tuning LLMs for domain-specific use cases

• Comfortable operating in privacy/PII-sensitive environments and applying compliance mitigations

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On this team, you will build and operate the ML infrastructure and product services that enable trust and intelligence across Plaid's network. You'll own feature engineering, offline training and batch scoring, online feature serving, and real-time inference so model outputs directly power partner-facing fraud & trust products and bank intelligence features.

Responsibilities

• Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows).
• Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact).
• Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses.
• Build and operate offline training pipelines and production batch scoring for bank intelligence products.
• Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring.
• Implement model CI/CD, model/version registry, and safe rollout/rollback strategies.
• Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs.
• Ensure offline and online parity, data lineage, and automated validation / data contracts to reduce regressions.
• Optimize inference performance and cost for real-time scoring (batching, caching, runtime selection).
• Ensure fairness, explainability and PII-aware handling for partner-facing ML features; maintain auditability for compliance.
• Partner with platform and cross-functional teams to scale the ML/data foundation (graph features, sequence embeddings, unified pipelines).
• Mentor engineers and document team standards for ML productization and operations.

Qualifications

• Must-haves:
• Strong software engineering skills including systems design, APIs, and building reliable backend services (Go or Python preferred).
• Production experience with batch and streaming data pipelines and orchestration tools such as Airflow or Spark.
• Experience building or operating real-time scoring and online feature-serving systems, including feature stores and low-latency model inference.
• Experience integrating model outputs into product flows (APIs, feature flags) and measuring impact through experiments and product metrics.
• Experience with model lifecycle and operations: model registries, CI/CD for models, reproducible training, offline & online parity, monitoring and incident response.
• Nice to have:
• Experience in fraud, risk, or marketing intelligence domains.
• Experience with feature-store products (Tecton / Chronon / Feast / internal) and unified pipelines.
• Experience with graph frameworks, graph feature engineering, or sequence embeddings.
• Experience optimizing inference at scale (Triton/ONNX/quantization, batching, caching).

Additional Information

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable.

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LLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.

Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.

Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.

Build document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.

Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.

Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.

Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.

Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.

Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.

Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

Collaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.

Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.

Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

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At Cresta, we are on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioural best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster.

As a Senior Machine Learning Engineer, you will lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches. You will architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.

Responsibilities:

• Lead the design and development of Cresta's next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches.
• Architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems.
• Design, deploy, and optimize LLM-powered systems, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration, and domain-adapted models.
• Improve reasoning, planning, and tool-use capabilities in real-world AI applications.
• Develop evaluation strategies for complex, non-deterministic systems, including offline benchmarking, online experimentation, and LLM-as-a-judge methodologies.
• Diagnose and mitigate real-world failure modes such as hallucinations, retrieval errors, tool misuse, prompt brittleness, and multi-step reasoning breakdowns.
• Define and measure quality metrics (e.g., accuracy, faithfulness, task completion, latency, cost, robustness) to improve system reliability and performance.
• Optimize AI systems for scalability, latency, security, and cost efficiency in production environments.
• Collaborate cross-functionally with product, frontend, and backend teams to integrate AI capabilities seamlessly into Cresta's platform.
• Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta's AI systems.

Qualifications:

• Bachelor's degree in Computer Science, Mathematics, or a related field; Master's or Ph.D. preferred.
• 5–8+ years of industry experience building and deploying machine learning systems in production, including significant experience working with LLMs.
• Strong expertise in NLP, Generative AI, transformer architectures, embeddings, and retrieval systems.
• Proven experience designing and deploying Retrieval-Augmented Generation (RAG) systems in enterprise environments.
• Experience building and evaluating complex agentic or multi-step LLM workflows.
• Strong knowledge of modern ML frameworks and tools (e.g., PyTorch, TensorFlow, Hugging Face) and distributed/cloud-based infrastructure.
• Demonstrated ability to optimize real-time ML systems for performance, scalability, and reliability.
• Strong technical leadership skills, with the ability to influence cross-functional decisions and raise the engineering bar.

Perks & Benefits:

• We offer Cresta employees a variety of medical, dental, and vision plans, designed to fit you and your family's needs.
• Paid parental leave to support you and your family.
• Monthly Health & Wellness allowance.
• Work from home office stipend to help you succeed in a remote environment.
• Lunch reimbursement for in-office employees.
• PTO: 3 weeks in Canada.

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We believe in a world where homes are owned by regular people, not corporations. Our mission is to provide authentic belonging experiences, empowering residents to become homeowners and homeowners to achieve financial freedom.

The Role

This is a builder role with direct P&L impact. You will own and scale the homeowner supply growth engine (across acquisition, onboarding, activation, and retention). You’ll be the GM of your own system, driving strategy and execution for a full-stack, AI-first funnel. You’ll ship fast, measure everything, and bring a growth obsession that matches our ambition.

Responsibilities

• Build an AI-native homeowner acquisition funnel. • Personalized onboarding experiences that adapt dynamically based on user attributes, preferences, and model predictions. • Automated pricing, trust-building, and documentation flows that reduce friction and compress time-to-value. • Predictive scoring and routing systems to prioritize the right leads, with the right incentives, at the right moment. • Internal AI copilots that handle verification, follow-up, and workflow automation, freeing humans to do only what humans must. • A scalable measurement framework across the funnel, with source attribution and model-driven forecasting.

Your Mandate

• Own strategy and execution for Belong’s homeowner funnel, from first click to full activation. • Identify bottlenecks and explode them with product. • Partner with leaders to deeply link paid acquisition with in-product performance, and ensure sales and operations can deliver on the promise of Belong. • Define success metrics, instrument everything, and build dashboards that replace guesswork with precision. • Write crisp PRDs, wireframe smart workflows, and ship frequently. • Operate as both a strategic thinker and a tactical executor, you will lead and build.

What You Bring

• 2+ years of experience in product management with ownership over growth funnels, ideally in a B2C or marketplace company. • You’ve driven CAC down, LTV up, and activation time down with product, not just marketing. • You’ve shipped AI-powered product features and understand LLMs, embeddings, and prompt engineering. • You’ve built experiments with rigor and made hard calls with imperfect data. • You understand user psychology and can design onboarding flows that convert skeptics into champions. • You communicate clearly, lead cross-functional pods, and get things shipped. • You play to win.

What Success Looks Like In Year 1

• Homeowner acquisition costs are down, speed to activation is up, and conversion is accelerating. • AI is integrated across the funnel, delivering measurable improvements in cost, quality, and experience. • Experimentation velocity is high, insights are trustworthy, and every major decision has a clear metric trail. • The growth engine is humming, and you’re in the driver’s seat.

Qualifications

• 2+ years of product management experience, preferably in consumer marketplaces or high-growth startups • Demonstrated success driving growth initiatives in customer acquisition and retention • Experience working across the AARRR funnel (Acquisition, Activation, Retention, Referral, Revenue) • Strong understanding of UX principles and ability to turn concepts into high-fidelity wireframes and designs. • Proven ability to lead cross-functional teams in a fast-moving, ambiguous environment • Excellent communication, collaboration, and stakeholder management skills • Familiarity with experimentation frameworks and statistical methods • Experience integrating or working with AI technologies to enhance product capabilities • Entrepreneurial mindset with a bias for action and a focus on delivering value quickly • And you're pithy.

Additional Information

Who You Are

You don’t want to maintain someone else’s system, you want to build your own. You’re competitive. You run toward outcomes, not away from problems. You are customer-obsessed but ruthless about the business. You think in feedback loops. You crave autonomy and accountability. You’ve been underestimated before, and you’re not done proving people wrong.

You want to build a lasting product, and you know growth is where the game is won or lost.

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Job Responsibilities

• Conduct data analysis to support payment analysis, marketing analysis, product performance, customer behavior analysis, and business operations.
• Build and maintain recurring and ad-hoc dashboards/reports using business intelligence tools.
• Translate business questions into analytical tasks and deliver clear, data-driven answers and recommendations.
• Support root cause investigations for anomalies and contribute to continuous improvement of reporting accuracy.
• Collaborate with data engineers, product managers, and other teams to ensure smooth data pipeline and reliable reporting.
• Support the integration of AI tools (e.g., LLMs) into reporting and analysis workflows under guidance from senior team members.
• Document metrics definitions, dashboard logic, and report processes for consistency and transparency.

Pre-Requisites

• Bachelor's degree in Statistics, Mathematics, Computer Science, Business Analytics, or a related field.
• 3+ years of experience in data analysis or business intelligence roles.
• Proficiency in SQL and experience with one or more data analysis tools (Python, R, or Excel).
• Hands-on experience with data visualization tools like Tableau, Power BI, or QuickSight.
• Strong analytical thinking, attention to detail, and ability to manage multiple tasks.
• Clear communication skills with the ability to work across business and technical teams.

Benefits

• Competitive salary
• Comprehensive benefits package
• Opportunities for professional growth and development
• Collaborative and dynamic work environment
• Recognition and rewards for outstanding performance
• Access to cutting-edge technology and tools
• Flexible work arrangements and work-life balance

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We are looking for a Backend AI Engineer to join our team and help build and scale backend systems that power AI Agents for the gaming industry, handling millions of customer support tickets at scale.

In this role, you will work on high-throughput backend services, AI-powered workflows, and retrieval-based systems that support large-scale customer support automation. You will collaborate closely with AI Engineers, ML Engineers, and Product teams to design, build, deploy, and operate reliable backend systems that integrate AI components such as RAG pipelines and AI Agent orchestration.

This is an excellent opportunity to work on production-grade AI systems operating at massive scale, where performance, reliability, and observability are critical.

What You’ll Do:

• Design, develop, and maintain scalable backend services and APIs using Python and frameworks like FastAPI.
• Build and integrate backend workflows supporting AI Agents and customer support automation use cases.
• Develop secure and performant REST APIs for internal and external consumers.
• Integrate AI components such as RAG pipelines, embedding services, and inference APIs into backend systems.
• Work with databases including MongoDB, YugabyteDB, and PostgreSQL to support high-scale, low-latency workloads.
• Containerize applications using Docker and deploy/manage services on Kubernetes (K8s).
• Deploy and operate services on AWS and GCP cloud platforms.
• Implement and maintain CI/CD pipelines, and actively participate in CM/CR release processes.
• Use observability tools such as Grafana, Elasticsearch, and Kibana to monitor system health, debug issues, and improve reliability.
• Write clean, testable, and maintainable code; participate in code reviews and design discussions.
• Troubleshoot and resolve production issues related to scalability, performance, and reliability.

Requirements

• 3–5 years of experience in backend software development.
• Strong hands-on experience with Python and backend frameworks, especially FastAPI.
• Experience building backend APIs and workflow integrations.
• Solid understanding of databases such as MongoDB, PostgreSQL, and YugabyteDB.
• Hands-on experience with Docker and working knowledge of Kubernetes.
• Familiarity with AWS and GCP cloud services.
• Good understanding of CI/CD concepts, build pipelines, and CM/CR release processes.
• Working knowledge of observability and logging systems like Grafana, Elasticsearch, and Kibana.
• Basic to intermediate understanding of AI systems, especially Retrieval-Augmented Generation (RAG).
• Strong problem-solving skills and ability to work effectively in a collaborative team environment.

Good to Have / Plus Skills

• Experience with Jenkins for CI/CD automation.
• Familiarity with MCP concepts and AI orchestration frameworks.
• Hands-on exposure to AI/LLM-based systems, embeddings, or vector search.
• Experience or familiarity with Clojure.
• Knowledge of the Gaming industry or Customer Support domain.
• Experience working in high-scale, high-traffic production systems.

Bonus Points

• Experience building or operating AI Agents in production environments.
• Exposure to distributed systems, event-driven architectures, or message queues.
• Interest in AI reliability, evaluation, and system observability at scale.

Benefits

• Hybrid setup
• Worker's insurance
• Paid Time Offs
• Other employee benefits to be discussed by our Talent Acquisition team in India.

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As a Senior Product Manager, you will work at the nexus of product and research, driving execution in partnership with engineers, language engineers, data scientists and researchers. You will develop and execute on LLM platform strategy for Copilot that extend language model's capabilities. You will prototype approaches by steering language models to drive response quality across a wide range of scenarios. You will identify and prioritise platform, orchestration and language model issues that impact quality, factuality and safety and working with engineers and researchers to find a path to resolution.

You will define and build measurable evaluations with relevant datasets to demonstrate quality improvements. You will define, deploy and manage experiments in production that impact language model's tool use, driving measurable improvements in relevance for and engagement with Copilot users. You will partner with product teams to scale tool building and work with inference, agents and orchestration teams to resolve dependencies. You will be accountable to own the status of key projects, proactively identifying risks and proposing solutions to ensure timely delivery.

Responsibilities include:

• Developing and executing on LLM platform strategy for Copilot that extend language model's capabilities
• Prototyping approaches by steering language models to drive response quality across a wide range of scenarios
• Identifying and prioritising platform, orchestration and language model issues that impact quality, factuality and safety and working with engineers and researchers to find a path to resolution
• Defining and building measurable evaluations with relevant datasets to demonstrate quality improvements
• Defining, deploying and managing experiments in production that impact language model's tool use, driving measurable improvements in relevance for and engagement with Copilot users
• Partnering with product teams to scale tool building and working with inference, agents and orchestration teams to resolve dependencies
• Being accountable to own the status of key projects, proactively identifying risks and proposing solutions to ensure timely delivery

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As a Mid-Market Account Executive on Anthropic's Industries team, you'll drive the adoption of safe, frontier AI by securing strategic deals with mid-market companies in key verticals, unlocking new value streams throughout their business. You'll leverage your consultative sales expertise and technical acumen to propel revenue growth while becoming a trusted partner to customers, helping them embed and deploy AI while uncovering its full range of capabilities.

In collaboration with GTM, Product, Engineering, and cross-functional teams, you'll continuously refine our value proposition, sales methodology, and market positioning to ensure differentiated value across the landscape.

Responsibilities:

• Contribute to the Mid-Market GTM strategy, identifying new use cases within your assigned verticals, winning new business, and sharing feedback with cross-functional teams

• Drive strategic expansion within key accounts in established verticals and new logo acquisition within emerging verticals

• Own a revenue target and all aspects of the sales cycle from prospecting to close, including finding key workflows to automate and agentify

• Become a trusted advisor to customers, understanding their unique needs and crafting tailored AI solutions. Co-innovate with customers and sell on the product roadmap while appropriately setting expectations

• Collaborate extensively with cross-functional partners including Product, Customer Success, Legal, Marketing, and Applied AI to help bring new solutions to market and provide feedback to shape roadmaps

• Develop sales collateral, proposals, and presentations to effectively position Anthropic's AI products. Continuously refine sales tactics and share best practices

You may be a good fit if you have:

• 3+ years of enterprise B2B sales experience in technology, preferably in SaaS, API solutions, or emerging technologies

• Experience automating workflows or building automations to handle the repeatable components of a sales process

• Proven experience exceeding revenue targets in fast-paced organizations by effectively managing an evolving pipeline and sales process

• Excellent communication skills and the ability to present confidently and build connections across all customer levels, from ICs to C-level executives

• A knack for bringing order to chaos and an enthusiastic “roll up your sleeves'' mentality. You are a true team player

• A strategic, analytical approach to assessing markets combined with creative, tactical execution to capture opportunities

• A passion for and/or experience with advanced AI systems. You feel strongly about ensuring frontier AI systems are developed safely

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We

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Location

São Paulo

Employment Type

Full time

Department

About the Team

OpenAI’s AI Success Engineer team partners with the world’s most ambitious organisations to translate cutting-edge AI into real business value. We guide customers from first deployment through scaled enterprise adoption. Our work spans technical integration and enablement, workflow transformation, and sustained program and product delivery.

Our customers range from fast-growing digital natives to the largest global enterprises, government agencies, and educational institutions. Every engagement is an opportunity to shape how AI changes work, productivity, and innovation. This role sits at the centre of that mission.

About the Role

The AI Success Engineer role is the primary post-sales point of contact for OpenAI’s most important customers. You are responsible for driving account health and adoption, ensuring technical readiness, identifying new use cases, and delivering measurable value to our customers with OpenAI’s ambitiously growing platform.

This role blends technical depth, program management, customer advisory, and product influence. You will partner deeply with customer teams, map workflows, lead configuration, oversee deployment plans, and guide customers toward high-impact use cases that showcase the full value of our platform.

You will work closely with Sales, Solutions Architecture, Product, and Research to ensure the customer experience is connected and successful across every touchpoint. Success in this role means accelerating adoption, increasing customer activation depth, guiding strategic use cases that get to production, and helping customers demonstrate tangible business impact.

This role is based in São Paulo (office 3x a week) and we provide relocation support to new employees.

In this role, you will:

• Lead the technical relationship for post-sale customers and act as their trusted advisor on deployment, adoption, and value realisation.

• Own account health, adoption velocity, and ongoing technical deployment and success across your portfolio.

• Be an expert in all of OpenAI products across our API and agentic platform, Codex, ChatGPT Enterprise, and more and conduct technical enablement and configuration sessions across them.

• Identify and validate use cases by embedding with customer teams to understand workflows and pain points.

• Lead account-level coordination across multiple work streams, including new product activation, change management, and customer rollout and deployment planning.

• Build strong relationships with executive sponsors and technical stakeholders and help align business goals with OpenAI capabilities.

• Translate customer objectives into an actionable adoption roadmap with clear sequencing, milestones, and KPIs.

• Partner with Solutions Architecture, Product, Engineering and Research by surfacing customer feedback, field patterns, and technical blockers and act as a cross-functional navigator who keeps teams aligned, informed, and moving toward customer outcomes.

• Guide value realisation and measure impact through baselines, KPI definition, and post-deployment reporting.

• Facilitate workshops on use case design, adoption best practices, champion building, and internal enablement.

• Help drive expansions by identifying high-leverage opportunities where OpenAI’s platform can power new workflows or lines of business.

• Serve as the technical advisor for existing customer implementations by guiding and optimising account setup, configuration, etc.

You’ll thrive in this role if you:

• 8+ years of experience in technical customer-facing roles such as technical account management, technical GenAI consulting or deployment roles, solutions architecture, technical delivery leadership, customer architecture or engineering, or other deep technical enterprise adoption work.

• Deep, hands-on knowledge of OpenAI product capabilities, APIs, SDKs, connectors, and common integration patterns and able to explain model behaviour, limitations, technical trade-offs, embeddings, retrieval augmentation, and approaches to fine-tuning or custom model usage.

• Understanding and familiarity with coding languages like Python or JavaScript, and comfort with REST APIs, SDKs, automation, CI/CD, containers, and cloud platforms.

• Can translate technical concepts into clear business language and help customers understand the strategic impact of AI technologies.

• Can show a strong record of driving technical deployments with hands-on on customer work and owning impactful adoption and value for large enterprise customers with complex environments and multiple stakeholders.

• Are comfortable embedding with customers to map workflows, identify requirements, and diagnose adoption challenges.

• Have excellent project and program management instincts and can lead multi-work stream initiatives with clarity and structure.

• Enjoy being a thought partner for C-level stakeholders while also diving deep with technical teams.

• Operate with high ownership and can manage fast decision-making, context switching, and dynamic customer needs.

• Have a strong record of driving technical deployments with hands-on on customer work and owning impactful adoption and value for large enterprise customers with complex environments and multiple stakeholders.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of human endeavour.

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Research Engineer / Research Scientist - Foundations Retrieval IC

Location

San Francisco

Employment Type

Full time

Department

Research

Compensation

• $445K – $555K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Foundations Research team works on high-risk, high-reward ideas that could shape the next decade of AI. Our goal is to advance the science and data that enable our training and scaling efforts, with a particular focus on future frontier models. Pushing the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science.

About the Role

We’re looking for a researcher focused on our embedding retrieval efforts. You’ll work with a team of world-class research scientists and engineers developing foundational technology that enables models to retrieve and condition on the right information, at the right time. This includes designing new embedding training objectives, scalable vector store architectures, and dynamic indexing methods.

This work will support retrieval across many OpenAI products and internal research efforts, with opportunities for scientific publication and deep technical impact.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

Responsibilities

• Tackle embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.

• Collaborate with a team of researchers and engineers building end-to-end infrastructure for training, evaluating, and integrating embeddings into frontier models.

• Drive innovation in dense, sparse, and hybrid representation techniques, metric learning, and learning-to-retrieve systems.

• Collaborate closely with Pretraining, Inference, and other Research teams to integrate retrieval throughout the model lifecycle

• Contribute to OpenAI’s long-term vision of AI systems with memory and knowledge access capabilities rooted in learned representations.

You Might Thrive in This Role If You Have

• Proven experience leading high-performance teams of researchers or engineers in ML infrastructure or foundational research.

• Deep technical expertise in representation learning, embedding models, or vector retrieval systems.

• Familiarity with transformer-based LLMs and how embedding spaces can interact with language model objectives.

• Research experience in areas such as contrastive learning, supervised or unsupervised embedding learning, or metric learning.

• A track record of building or scaling large machine learning systems, particularly embedding pipelines in production or research contexts.

• A first-principles mindset for challenging assumptions about how retrieval and memory should work for large models.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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About the Team

OpenAI’s AI Success Engineer team partners with the world’s most ambitious organisations to translate cutting-edge AI into real business value. We guide customers from first deployment through scaled enterprise adoption. Our work spans technical integration and enablement, workflow transformation, and sustained program and product delivery.

About the Role

The Success Engineer role is the primary post-sales point of contact for a set of OpenAI’s most important customers. You are responsible for driving account health and adoption, ensuring technical readiness, identifying new use cases, and delivering measurable value to our customers with OpenAI’s platform.

This role blends technical leadership, program management, customer advisory, and product influence. You will partner deeply with customer teams, map workflows, lead configuration and enablement, oversee deployment plans, and guide customers toward high-impact use cases that showcase the full value of our platform.

You will work closely with Sales, Solutions Architecture, Product, and Research to ensure the customer experience is connected and successful across every touchpoint. Success in this role means accelerating adoption, increasing customer activation depth, guiding strategic use cases that get to production, and helping customers demonstrate tangible business impact.

In this role, you will:

• Lead the technical relationship for post-sale customers and act as their trusted advisor on deployment, adoption, and value realization

• Own account health, adoption velocity, and ongoing technical deployment and success across your portfolio

• Conduct technical enablement and configuration sessions across our broad product portfolio

• Identify and validate use cases by embedding with customer teams to understand workflows and pain points

• Lead account level coordination across multiple workstreams, including new product activation, change management, and customer rollout and deployment planning

• Build strong relationships with executive sponsors and technical stakeholders and help align business goals with OpenAI capabilities

• Translate customer objectives into an actionable adoption roadmap with clear sequencing, milestones, and KPIs

• Partner with Solutions Architecture, Product, Engineering and Research by surfacing customer feedback, field patterns, and technical blockers and act as a cross-functional navigator who keeps teams aligned, informed, and moving toward customer outcomes

• Guide value realization and measure impact through baselines, KPI definition, and post-deployment reporting

• Facilitate workshops on use case design, adoption best practices, champion building, and internal enablement

• Help drive expansions by identifying high-leverage opportunities where OpenAI’s platform can power new workflows or lines of business

• Serve as the technical advisor for existing customer implementations by guiding and optimizing account setup, configuration, etc.

You’ll thrive in this role if you:

• 8+ years of experience in technical customer-facing roles such as technical account management, technical GenAI consulting or deployment roles, solutions architecture, technical delivery leadership, or deep technical enterprise adoption work

• Deep, hands-on knowledge of OpenAI product capabilities, APIs, SDKs, connectors, and common integration patterns and able to explain model behavior, limitations, technical tradeoffs, embeddings, retrieval augmentation, and approaches to fine-tuning or custom model usage

• Can translate technical concepts into clear business language and help customers understand the strategic impact of AI technologies

• Are comfortable embedding with customers to map workflows, identify requirements, and diagnose adoption challenges

• Have excellent project and program management instincts and can lead multi-workstream initiatives with clarity and structure

• Enjoy being a thought partner for C-level stakeholders while also diving deep with technical teams

• Operate with high ownership and can manage fast decision making, context switching, and dynamic customer needs

• Have a strong record of driving technical deployments with hands-on on customer work and owning impactful adoption and value for large enterprise customers with complex environments and multiple stakeholders

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

About the Team

OpenAI’s AI Success Engineer team partners with the world’s most ambitious organisations to translate cutting-edge AI into real business value. We guide customers from first deployment through scaled enterprise adoption. Our work spans technical integration and enablement, workflow transformation, and sustained program and product delivery.

About the Role

The Success Engineer role is the primary post-sales point of contact for a set of OpenAI’s most important customers. You are responsible for driving account health and adoption, ensuring technical readiness, identifying new use cases, and delivering measurable value to our customers with OpenAI’s platform.

This role blends technical leadership, program management, customer advisory, and product influence. You will partner deeply with customer teams, map workflows, lead configuration and enablement, oversee deployment plans, and guide customers toward high-impact use cases that showcase the full value of our platform.

You will work closely with Sales, Solutions Architecture, Product, and Research to ensure the customer experience is connected and successful across every touchpoint. Success in this role means accelerating adoption, increasing customer activation depth, guiding strategic use cases that get to production, and helping customers demonstrate tangible business impact.

In this role, you will:

• Lead the technical relationship for post-sale customers and act as their trusted advisor on deployment, adoption, and value realization

• Own account health, adoption velocity, and ongoing technical deployment and success across your portfolio

• Conduct technical enablement and configuration sessions across our broad product portfolio

• Identify and validate use cases by embedding with customer teams to understand workflows and pain points

• Lead account level coordination across multiple workstreams, including new product activation, change management, and customer rollout and deployment planning

• Build strong relationships with executive sponsors and technical stakeholders and help align business goals with OpenAI capabilities

• Translate customer objectives into an actionable adoption roadmap with clear sequencing, milestones, and KPIs

• Partner with Solutions Architecture, Product, Engineering and Research by surfacing customer feedback, field patterns, and technical blockers and act as a cross-functional navigator who keeps teams aligned, informed, and moving toward customer outcomes

• Guide value realization and measure impact through baselines, KPI definition, and post-deployment reporting

• Facilitate workshops on use case design, adoption best practices, champion building, and internal enablement

• Help drive expansions by identifying high-leverage opportunities where OpenAI’s platform can power new workflows or lines of business

• Serve as the technical advisor for existing customer implementations by guiding and optimizing account setup, configuration, etc.

You’ll thrive in this role if you:

• 8+ years of experience in technical customer-facing roles such as technical account management, technical GenAI consulting or deployment roles, solutions architecture, technical delivery leadership, or deep technical enterprise adoption work

• Deep, hands-on knowledge of OpenAI product capabilities, APIs, SDKs, connectors, and common integration patterns and able to explain model behavior, limitations, technical tradeoffs, embeddings, retrieval augmentation, and approaches to fine-tuning or custom model usage

• Can translate technical concepts into clear business language and help customers understand the strategic impact of AI technologies

• Are comfortable embedding with customers to map workflows, identify requirements, and diagnose adoption challenges

• Have excellent project and program management instincts and can lead multi-workstream initiatives with clarity and structure

• Enjoy being a thought partner for C-level stakeholders while also diving deep with technical teams

• Operate with high ownership and can manage fast decision making, context switching, and dynamic customer needs

• Have a strong record of driving technical deployments with hands-on on customer work and owning impactful adoption and value for large enterprise customers with complex environments and multiple stakeholders

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

About the Team

OpenAI’s AI Success Engineer team partners with the world’s most ambitious organisations to translate cutting-edge AI into real business value. We guide customers from first deployment through scaled enterprise adoption. Our work spans technical integration and enablement, workflow transformation, and sustained program and product delivery.

About the Role

The Success Engineer role is the primary post-sales point of contact for a set of OpenAI’s most important customers. You are responsible for driving account health and adoption, ensuring technical readiness, identifying new use cases, and delivering measurable value to our customers with OpenAI’s platform.

This role blends technical leadership, program management, customer advisory, and product influence. You will partner deeply with customer teams, map workflows, lead configuration and enablement, oversee deployment plans, and guide customers toward high-impact use cases that showcase the full value of our platform.

You will work closely with Sales, Solutions Architecture, Product, and Research to ensure the customer experience is connected and successful across every touchpoint. Success in this role means accelerating adoption, increasing customer activation depth, guiding strategic use cases that get to production, and helping customers demonstrate tangible business impact.

This role can be based in Delhi, Mumbai, or Bengaluru.

In this role, you will:

• Lead the technical relationship for post-sale customers and act as their trusted advisor on deployment, adoption, and value realization

• Own account health, adoption velocity, and ongoing technical deployment and success across your portfolio

• Conduct technical enablement and configuration sessions across our broad product portfolio

• Identify and validate use cases by embedding with customer teams to understand workflows and pain points

• Lead account-level coordination across multiple workstreams, including new product activation, change management, and customer rollout and deployment planning

• Build strong relationships with executive sponsors and technical stakeholders and help align business goals with OpenAI capabilities

• Translate customer objectives into an actionable adoption roadmap with clear sequencing, milestones, and KPIs

• Partner with Solutions Architecture, Product, Engineering, and Research by surfacing customer feedback, field patterns, and technical blockers and act as a cross-functional navigator who keeps teams aligned, informed, and moving toward customer outcomes

• Guide value realization and measure impact through baselines, KPI definition, and post-deployment reporting

• Facilitate workshops on use case design, adoption best practices, champion building, and internal enablement

• Help drive expansions by identifying high-leverage opportunities where OpenAI’s platform can power new workflows or lines of business

• Serve as the technical advisor for existing customer implementations by guiding and optimizing account setup, configuration, etc.

You’ll thrive in this role if you:

• 8+ years of experience in technical customer-facing roles such as technical account management, technical GenAI consulting, solutions architecture, technical delivery leadership, or deep technical enterprise SaaS adoption work

• Deep, hands-on knowledge of OpenAI product capabilities, APIs, SDKs, connectors, and common integration patterns and able to explain model behavior, limitations, technical tradeoffs, embeddings, retrieval augmentation, and approaches to fine-tuning or custom model usage

• Can translate technical concepts into clear business language and help customers understand the strategic impact of AI technologies

• Are comfortable embedding with customers to map workflows, identify requirements, and diagnose adoption challenges

• Have excellent project and program management instincts and can lead multi-workstream initiatives with clarity and structure

• Enjoy being a thought partner for C-level stakeholders while also diving deep with technical teams

• Operate with high ownership and can manage fast decision making, context switching, and dynamic customer needs

• Have a strong record of driving technical deployments with hands-on on customer work and owning impactful adoption and value for large enterprise customers with complex environments and multiple stakeholders

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

XML job scraping automation by YubHub

About the Team

OpenAI’s AI Success Engineer team partners with the world’s most ambitious organisations to translate cutting-edge AI into real business value. We guide customers from first deployment through scaled enterprise adoption. Our work spans technical integration and enablement, workflow transformation, and sustained program and product delivery.

About the Role

The Success Engineer role is the primary post-sales point of contact for a set of OpenAI’s most important customers. You are responsible for driving account health and adoption, ensuring technical readiness, identifying new use cases, and delivering measurable value to our customers with OpenAI’s platform.

This role blends technical leadership, program management, customer advisory, and product influence. You will partner deeply with customer teams, map workflows, lead configuration and enablement, oversee deployment plans, and guide customers toward high-impact use cases that showcase the full value of our platform.

You will work closely with Sales, Solutions Architecture, Product, and Research to ensure the customer experience is connected and successful across every touchpoint. Success in this role means accelerating adoption, increasing customer activation depth, guiding strategic use cases that get to production, and helping customers demonstrate tangible business impact.

In this role, you will:

• Lead the technical relationship for post-sale customers and act as their trusted advisor on deployment, adoption, and value realization

• Own account health, adoption velocity, and ongoing technical deployment and success across your portfolio

• Conduct technical enablement and configuration sessions across our broad product portfolio

• Identify and validate use cases by embedding with customer teams to understand workflows and pain points

• Lead account level coordination across multiple workstreams, including new product activation, change management, and customer rollout and deployment planning

• Build strong relationships with executive sponsors and technical stakeholders and help align business goals with OpenAI capabilities

• Translate customer objectives into an actionable adoption roadmap with clear sequencing, milestones, and KPIs

• Partner with Solutions Architecture, Product, Engineering and Research by surfacing customer feedback, field patterns, and technical blockers and act as a cross-functional navigator who keeps teams aligned, informed, and moving toward customer outcomes

• Guide value realization and measure impact through baselines, KPI definition, and post-deployment reporting

• Facilitate workshops on use case design, adoption best practices, champion building, and internal enablement

• Help drive expansions by identifying high-leverage opportunities where OpenAI’s platform can power new workflows or lines of business

• Serve as the technical advisor for existing customer implementations by guiding and optimizing account setup, configuration, etc.

You’ll thrive in this role if you:

• 8+ years of experience in technical customer-facing roles such as technical account management, technical GenAI consulting, solutions architecture, technical delivery leadership, or deep technical enterprise SaaS adoption work

• Deep, hands-on knowledge of OpenAI product capabilities, APIs, SDKs, connectors, and common integration patterns and able to explain model behavior, limitations, technical tradeoffs, embeddings, retrieval augmentation, and approaches to fine-tuning or custom model usage

• Can translate technical concepts into clear business language and help customers understand the strategic impact of AI technologies

• Are comfortable embedding with customers to map workflows, identify requirements, and diagnose adoption challenges

• Have excellent project and program management instincts and can lead multi-workstream initiatives with clarity and structure

• Enjoy being a thought partner for C-level stakeholders while also diving deep with technical teams

• Operate with high ownership and can manage fast decision making, context switching, and dynamic customer needs

• Have a strong record of driving technical deployments with hands-on on customer work and owning impactful adoption and value for large enterprise customers with complex environments and multiple stakeholders

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Applied AI

Compensation

• $230K – $385K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the team

The Applied team at OpenAI safely brings cutting-edge technology to the world. We have released groundbreaking products such as ChatGPT, Plugins, DALL·E, and APIs for GPT-4, GPT-3, embeddings, and fine-tuning. Our team also manages large-scale inference infrastructure. With much more on the horizon, our impact continues to grow.

Our customers create fast-growing businesses using our APIs, enabling product features previously unimaginable. ChatGPT exemplifies the current scope of possibilities. We prioritize the responsible use of our powerful tools, valuing safe deployment over unchecked expansion.

Within Applied Engineering, the Financial Engineering team ensures that our products are monetized effectively to accommodate customers' varying needs and scales. Collaborating closely with the GTM and Finance teams, we strive to tailor our billing stack to our evolving internal requirements. We seek an experienced engineer to architect and refine our billing systems, enhancing their functionality to meet the demands of our increasingly complex and expansive product offerings.

In this role, you will:

• Architect and build the next generation of billing and monetization systems at OpenAI.

• Develop across the stack to create comprehensive billing integrations for our range of ChatGPT and API users.

• Design a versatile billing platform suitable for both subscription and usage-based offerings, ensuring scalability and enterprise readiness/flexibility.

• Construct and integrate tools that empower internal teams to seamlessly incorporate billing data into their workflows.

• Collaborate closely with a wide array of stakeholders, including the Product, Data, Finance, and Go-To-Market teams, as well as fellow engineers.

You might thrive in this role if you:

• Possess a minimum of 5 years of professional software engineering experience, with added experience in payments, billing, or monetization seen as a bonus.

• Enjoy engaging with various partners, particularly those outside of engineering.

• Have a keen and innate desire to learn and acquire new skills, coupled with a strong ability to impart that knowledge clearly and succinctly to others.

• Bring significant experience in developing (and redeveloping) production systems to launch new product capabilities and to handle scaling challenges.

• Are deeply invested in creating an exceptional user experience, taking pride in crafting products that address customer needs.

• Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.

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Microsoft AI are looking for a talented Member of Technical Staff - Principal Backend Engineer, Copilot Memory and Personalization at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

As a Principal Backend Engineer, you will design and evolve large-scale data architectures that support Copilot memory and personalization, spanning batch, streaming, and serving paths. You will build and operate high-quality personalization and memory data pipelines, including signal ingestion, normalization, enrichment, aggregation, memory generation, and full lifecycle management. You will enable memory and personalization features by exposing well-designed datasets, APIs, and feature interfaces for downstream product and ML consumers. You will work closely with PMs, applied ML, and product engineering to translate product intent into robust data systems and measurable outcomes. You will act as a technical leader for memory and personalization data systems, influencing architecture and standards across multiple teams. You will lead design reviews, unblock complex technical problems, and make principled trade-offs in ambiguous spaces.

Accountabilities

• Design and evolve large-scale data architectures that support Copilot memory and personalization, spanning batch, streaming, and serving paths.
• Build and operate high-quality personalization and memory data pipelines, including signal ingestion, normalization, enrichment, aggregation, memory generation, and full lifecycle management.

The Candidate we're looking for

Experience:

• 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Experience building and deploying machine learning or large language model (LLM) applications at scale.
• Experience designing and implementing large-scale embedding, retrieval, and ranking systems.

Personal attributes:

• Thrive in a fast-paced, collaborative environment and are comfortable making progress in ambiguity.

Benefits

• Software Engineering IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year.
• Software Engineering IC6 – The typical base pay range for this role across the U.S. is USD $163,000 – $296,400 per year.

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What you'll do

Implement AI solutions to enable efficiency, expansion, and transformation across EA Experiences, directly aligned with EA Experiences' goals. You will:

• Stay informed on the evolution of AI technologies and opportunities to drive efficiency, expansion, and transformation
• Have a deep understanding of AI technologies and solutions, specifically focusing on how they can be integrated into business processes and systems to enhance efficiency
• Collaborate EA Experiences teams to understand their business, processes, opportunities, and challenges.
• Identify opportunities for developing creative AI solutions that enable our teams to focus on creative and innovative work that keeps our fans at the center of what we do
• Be an AI evangelist, promoting the use of AI as a force-multiplier

What you need

• 4+ years experience rapidly prototyping and deploying robust production solutions incorporating software engineering, infrastructure, architectural, and security best practices
• Excellent Python development skills are required, C#, JavaScript, HTML, and CSS are strongly preferred
• Experience using agentic AI software engineering solutions such as Cursor, GitHub CoPilot, or Claude Code
• Extensive experience engineering AI solutions and excellent working knowledge of:
• Prompt and context engineering
• AI agents, agentic architectures, tool calling, and Model Context Protocol (MCP)
• AI assistants, RAG, embeddings, and vector databases
• Model tuning, evaluation, benchmarking, and guardrails
• An excellent understanding of architecture, design principles, and cloud services, such as:
• AWS (Azure, and GCP cloud services are optional)
• Enterprise architecture and microservices design
• Observability and telemetry
• Infrastructure as code and CI/CD
• Data science experience is beneficial
• Content production (e.g. marketing) is strongly beneficial
• Experience navigating the legal, ethical, and security implications for AI
• Thrive working both collaboratively and independently
• Excellent creative, critical thinking, and problem solving skills
• Enjoy and prioritize continual learning- and staying up to date with the newest technologies and solutions and can understand how they can benefit EA Experiences
• Demonstrated ability to clearly and succinctly articulate complex concepts at a senior level
• Experience integrating AI solutions specifically with AAA Console/PC games and businesses is beneficial
• Experience with game engines is beneficial (Unity, Unreal)

Why this matters

This role is a key part of our EA Experiences team, and will help us to create innovative AI solutions that keep our fans at the center of everything we do. As an AI Solutions Engineer, you will be working closely with teams across EA Experiences to develop and implement cutting-edge AI solutions that provide increased efficiency and help our teams focus on what matters most, our fans.

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Microsoft AI are looking for a talented Member of Technical Staff - Principal Platform Engineer, Copilot Memory and Personalization at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

About the Role

As a Principal Platform Engineer, you will design and build large-scale Copilot memory and personalization systems leveraging search, embeddings, retrieval, ranking, and Retrieval-Augmented Generation (RAG). You will apply subject-matter expertise in cross-product features, collaborating with appropriate stakeholders to drive project plans, release plans, and deliverables across multiple groups. You will hold accountability as a Designated Responsible Individual (DRI), mentoring engineers across products/solutions, working on-call to monitor system/product/service for degradation, downtime, or interruptions.

Accountabilities

• Design and build large-scale Copilot memory and personalization systems leveraging search, embeddings, retrieval, ranking, and Retrieval-Augmented Generation (RAG).
• Apply subject-matter expertise in cross-product features, collaborating with appropriate stakeholders to drive project plans, release plans, and deliverables across multiple groups.

The Candidate we're looking for

Experience:

• 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.

Technical skills:

• Experience building and deploying machine learning or large language model (LLM) applications at scale.
• Experience designing and implementing large-scale embedding, retrieval, and ranking systems.

Personal attributes:

• Thrive in a fast-paced, collaborative environment and are comfortable making progress in ambiguity.

Benefits

• Software Engineering IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year.
• Software Engineering IC6 – The typical base pay range for this role across the U.S. is USD $163,000 – $296,400 per year.

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Microsoft AI are looking for a talented Senior Product Manager, Copilot AI at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI landscape.

About the Role

As a Senior Product Manager, you will drive Copilot model capabilities such as tool-use to ensure that the language models that power Microsoft Copilot deliver high quality responses to our users whilst being grounded, reliable, and cost-efficient. You will work at the nexus of product and research, driving execution in partnership with engineers, language engineers, data scientists and researchers.

Accountabilities

• Develop and execute on LLM platform strategy for Copilot that extend language model’s capabilities.
• Prototype approaches by steering language models to drive response quality across a wide range of scenarios.

The Candidate we're looking for

Experience:

• 5+ years experience in product management.
• 3+ years of experience leading ambiguous product areas, defining requirements, developing roadmaps, and working with multi-disciplinary teams to execute them.

Technical skills:

• Hands-on experience with LLM APIs (e.g. OpenAI, Anthropic, Azure OpenAI), embeddings, vector databases, and tool use.
• Hands-on experience with prompt design, context window management, and model evaluation.

Personal attributes:

• Abundance of positive energy, empathy, and kindness.
• Highly effective.

Benefits

• Competitive salary.
• Benefits and other compensation.

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What you'll do

The Senior Machine Learning Engineer will be responsible for architecting and building core components of the search platform and model stack. This will include designing, training, and optimizing large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models.

• Relentlessly push search quality forward — through models, data, tools, or any other leverage available
• Architect and build core components of the search platform and model stack
• Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models

What you need

The successful candidate will need to have a deep understanding of search and retrieval systems, including quality evaluation principles and metrics. They will also need to have a proven track record with large-scale search or recommender systems, and strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.

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What you'll do

Work directly with game teams/partners (internal clients) to understand their offerings/domain and create AI products and solutions to solve for their use cases.

What you need

• Graduate degree in Computer Science, Engineering, AI/ML, or a related quantitative field encouraged.
• 4+ years of experience building AI, ML, or data-driven systems in production environments.

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What you'll do

Implement AI solutions to enable efficiency, expansion, and transformation across EA Experiences, directly aligned with EA Experiences' goals. You will:

• Stay informed on the evolution of AI technologies and opportunities to drive efficiency, expansion, and transformation
• Have a deep understanding of AI technologies and solutions, specifically focusing on how they can be integrated into business processes and systems to enhance efficiency
• Lead collaborative efforts with EA Experiences teams to understand their business, processes, opportunities, and challenges.
• Identify opportunities for developing creative AI solutions that enable our teams to focus on creative and innovative work that keeps our fans at the center of what we do
• Be an AI evangelist, promoting the use of AI as a force-multiplier

What you need

• 4 years + experience rapidly prototyping and deploying robust production solutions incorporating software engineering, infrastructure, architectural, and security best practices
• Python development skills, C#, JavaScript, HTML, and CSS is beneficial
• Experience using agentic AI software engineering solutions such as Cursor
• Extensive experience engineering AI solutions and excellent working knowledge of:
• Prompt and context engineering
• AI agents, agentic architectures, tool calling, and Model Context Protocol (MCP)
• AI assistants, RAG, embeddings, and vector databases
• Model tuning, evaluation, benchmarking, and guardrails
• A comprehensive understanding of architecture and design principles, and cloud services, such as:
• AWS, Azure, and GCP cloud services
• Enterprise architecture and microservices design
• Observability and telemetry
• Infrastructure as code and CI/CD
• Experience navigating the legal, ethical, and security implications for AI
• Thrive working both collaboratively and independently
• Excellent creative, critical thinking, and problem solving skills
• Enjoy and prioritize continual learning- and staying up to date with the newest technologies and solutions and can understand how they can benefit EA Experiences
• Demonstrated ability to clearly and succinctly articulate complex concepts at a senior level
• Experience integrating AI solutions specifically with AAA Console/PC games and businesses is beneficial

XML job scraping automation by YubHub