In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production-grade, governed, multi-agent systems. You will apply leading GenAI frameworks and LLM platforms , including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases,while operating across the full Agentic SDLC.

P&C Insurance knowledge and experience is a significant plus. Additionally, familiarity with core insurance platforms like Guidewire, DuckCreek or Majesco will be extremely helpful to succeed in this role.

We are looking for candidates across all levels of experience and expertise - junior through senior level AI Product Engineers.

Responsibilities

• Partner directly with Financial Services clients to identify, prioritize, and shape Agentic AI use cases across customer operations, underwriting, claims, risk, compliance, finance, and technology.
• Lead client workshops to define agent personas, responsibilities, autonomy boundaries, human-in-the-loop checkpoints, and escalation logic.
• Translate evolving business needs into agentic product backlogs, roadmaps, and MVP definitions.
• Support executive conversations around GenAI platform strategy, operating models, vendor selection, and scale-out approaches.

Agentic Platform & Architecture Design

• Design and implement multi-agent architectures using modern GenAI tooling, including:
• Planner, executor, reviewer/critic, and supervisor agents
• Tool-calling and function-calling agents
• Memory-enabled agents (conversation, semantic, episodic, and structured memory)
• Leverage LangChain and LangGraph for agent orchestration, workflows, and control flow.
• Apply DSPy and declarative prompt optimization techniques for repeatability, performance tuning, and regression control.
• Design agent interaction patterns such as hierarchical agents, collaborating agents, and event-driven agent workflows.
• Define standardized agent contracts, interfaces, and schemas to enable reuse and scale.

Agentic SDLC & Engineering Delivery

• Own delivery across the full Software Development Lifecycle (SDLC), extending it into a formal Agentic SDLC, including:
• Agent design specifications and behavior contracts
• Prompt, policy, and tool versioning
• Simulation environments and offline evaluation
• Automated testing of agent flows and guardrails
• Controlled rollout, telemetry-driven optimization, and continuous learning
• Build production-grade AI services primarily using Python, integrating:
• LLM providers such as Anthropic (Claude), OpenAI, and open-source models
• Retrieval-Augmented Generation (RAG) using vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate)
• Implement CI/CD pipelines for agent code, prompts, and policies.
• Integrate GenAI agents with client systems via APIs, workflow engines, event streams, and data platforms.

Observability, Evaluation & Optimization

• Implement agent observability including tracing, decision logging, tool usage, and failure analysis.
• Apply evaluation frameworks for hallucination detection, consistency checks, and fitness scoring.
• Design feedback loops incorporating human-in-the-loop review and reinforcement.
• Monitor cost, latency, throughput, and behavioral drift across deployed agents.

Governance, Risk & Financial Services Compliance

• Design Agentic AI platforms aligned with Financial Services regulatory expectations, including:
• Auditability and traceability of agent decisions
• Model and prompt explainability
• Data privacy and security controls
• Resilience and fail-safe mechanisms
• Embed guardrails and policies addressing hallucination risk, bias, unauthorized actions, and escalation failures.
• Produce documentation supporting risk, compliance, internal audit, and regulator engagement.

Team Leadership & Firm Contribution

• Provide technical leadership and mentorship to consulting delivery teams.
• Contribute to internal GenAI accelerators, agent frameworks, and reusable assets.
• Support RFPs, proposals, and client solution designs with credible GenAI and agentic architectures.
• Participate in thought leadership on Agentic SDLC, GenAI engineering, and responsible autonomy.

XML job scraping automation by YubHub

We are seeking an experienced and innovative AI Product Engineer – Agentic Platforms to join our Financial Services Artificial Intelligence & Business Lines (FS-ABL) practice. This role is ideal for a consulting technologist with deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC, who can partner directly with clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments.

In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production-grade, governed, multi-agent systems. You will apply leading GenAI frameworks and LLM platforms , including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases,while operating across the full Agentic SDLC.

P&C Insurance knowledge and experience is a significant plus. Additionally, familiarity with core insurance platforms like Guidewire, DuckCreek or Majesco will be extremely helpful to succeed in this role.

We are looking for candidates across all levels of experience and expertise - junior through senior level AI Product Engineers.

Responsibilities

Client Advisory & Product Vision

Partner directly with Financial Services clients to identify, prioritize, and shape Agentic AI use cases across customer operations, underwriting, claims, risk, compliance, finance, and technology.

Lead client workshops to define agent personas, responsibilities, autonomy boundaries, human-in-the-loop checkpoints, and escalation logic.

Translate evolving business needs into agentic product backlogs, roadmaps, and MVP definitions.

Support executive conversations around GenAI platform strategy, operating models, vendor selection, and scale-out approaches.

Agentic Platform & Architecture Design

Design and implement multi-agent architectures using modern GenAI tooling, including:

Planner, executor, reviewer/critic, and supervisor agents

Tool-calling and function-calling agents

Memory-enabled agents (conversation, semantic, episodic, and structured memory)

Leverage LangChain and LangGraph for agent orchestration, workflows, and control flow.

Apply DSPy and declarative prompt optimization techniques for repeatability, performance tuning, and regression control.

Design agent interaction patterns such as hierarchical agents, collaborating agents, and event-driven agent workflows.

Define standardized agent contracts, interfaces, and schemas to enable reuse and scale.

Agentic SDLC & Engineering Delivery

Own delivery across the full Software Development Lifecycle (SDLC), extending it into a formal Agentic SDLC, including:

Agent design specifications and behavior contracts

Prompt, policy, and tool versioning

Simulation environments and offline evaluation

Automated testing of agent flows and guardrails

Controlled rollout, telemetry-driven optimization, and continuous learning

Build production-grade AI services primarily using Python, integrating:

LLM providers such as Anthropic (Claude), OpenAI, and open-source models

Retrieval-Augmented Generation (RAG) using vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate)

Implement CI/CD pipelines for agent code, prompts, and policies.

Integrate GenAI agents with client systems via APIs, workflow engines, event streams, and data platforms.

Observability, Evaluation & Optimization

Implement agent observability including tracing, decision logging, tool usage, and failure analysis.

Apply evaluation frameworks for hallucination detection, consistency checks, and fitness scoring.

Design feedback loops incorporating human-in-the-loop review and reinforcement.

Monitor cost, latency, throughput, and behavioral drift across deployed agents.

Governance, Risk & Financial Services Compliance

Design Agentic AI platforms aligned with Financial Services regulatory expectations, including:

Auditability and traceability of agent decisions

Model and prompt explainability

Data privacy and security controls

Resilience and fail-safe mechanisms

Embed guardrails and policies addressing hallucination risk, bias, unauthorized actions, and escalation failures.

Produce documentation supporting risk, compliance, internal audit, and regulator engagement.

Team Leadership & Firm Contribution

Provide technical leadership and mentorship to consulting delivery teams.

Contribute to internal GenAI accelerators, agent frameworks, and reusable assets.

Support RFPs, proposals, and client solution designs with credible GenAI and agentic architectures.

Participate in thought leadership on Agentic SDLC, GenAI engineering, and responsible autonomy.

Benefits

This position comes with competitive compensation and benefits package:

1. Competitive salary and performance-based bonuses

1. Comprehensive benefits package

1. Career development and training opportunities

1. Flexible work arrangements (remote and/or office-based)

1. Dynamic and inclusive work culture within a globally known group

1. Private Health Insurance

1. Retirement Benefits

1. Paid Time Off

1. Training & Development

1. Note: Benefits differ based on employee level

XML job scraping automation by YubHub

We are seeking an experienced and innovative AI Product Engineer – Agentic Platforms to join our Financial Services Artificial Intelligence & Business Lines (FS-ABL) practice. This role is ideal for a consulting technologist with deep expertise in modern GenAI tooling, agentic system design, and enterprise SDLC, who can partner directly with clients to envision, design, develop, and deploy Agentic AI platforms in regulated environments.

In this role, you will work at the intersection of client advisory, AI product engineering, and delivery execution, helping banks, insurers, and capital markets firms transition from GenAI pilots to production-grade, governed, multi-agent systems. You will apply leading GenAI frameworks and LLM platforms , including Anthropic, OpenAI, LangChain, LangGraph, DSPy, and vector databases,while operating across the full Agentic SDLC.

P&C Insurance knowledge and experience is a significant plus. Additionally, familiarity with core insurance platforms like Guidewire, DuckCreek or Majesco will be extremely helpful to succeed in this role.

We are looking for candidates across all levels of experience and expertise - junior through senior level AI Product Engineers.

Responsibilities:

Client Advisory & Product Vision

Partner directly with Financial Services clients to identify, prioritize, and shape Agentic AI use cases across customer operations, underwriting, claims, risk, compliance, finance, and technology.

Lead client workshops to define agent personas, responsibilities, autonomy boundaries, human-in-the-loop checkpoints, and escalation logic.

Translate evolving business needs into agentic product backlogs, roadmaps, and MVP definitions.

Support executive conversations around GenAI platform strategy, operating models, vendor selection, and scale-out approaches.

Agentic Platform & Architecture Design

Design and implement multi-agent architectures using modern GenAI tooling, including:

Planner, executor, reviewer/critic, and supervisor agents

Tool-calling and function-calling agents

Memory-enabled agents (conversation, semantic, episodic, and structured memory)

Leverage LangChain and LangGraph for agent orchestration, workflows, and control flow.

Apply DSPy and declarative prompt optimization techniques for repeatability, performance tuning, and regression control.

Design agent interaction patterns such as hierarchical agents, collaborating agents, and event-driven agent workflows.

Define standardized agent contracts, interfaces, and schemas to enable reuse and scale.

Agentic SDLC & Engineering Delivery

Own delivery across the full Software Development Lifecycle (SDLC), extending it into a formal Agentic SDLC, including:

Agent design specifications and behavior contracts

Prompt, policy, and tool versioning

Simulation environments and offline evaluation

Automated testing of agent flows and guardrails

Controlled rollout, telemetry-driven optimization, and continuous learning

Build production-grade AI services primarily using Python, integrating:

LLM providers such as Anthropic (Claude), OpenAI, and open-source models

Retrieval-Augmented Generation (RAG) using vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate)

Implement CI/CD pipelines for agent code, prompts, and policies.

Integrate GenAI agents with client systems via APIs, workflow engines, event streams, and data platforms.

Observability, Evaluation & Optimization

Implement agent observability including tracing, decision logging, tool usage, and failure analysis.

Apply evaluation frameworks for hallucination detection, consistency checks, and fitness scoring.

Design feedback loops incorporating human-in-the-loop review and reinforcement.

Monitor cost, latency, throughput, and behavioral drift across deployed agents.

Governance, Risk & Financial Services Compliance

Design Agentic AI platforms aligned with Financial Services regulatory expectations, including:

Auditability and traceability of agent decisions

Model and prompt explainability

Data privacy and security controls

Resilience and fail-safe mechanisms

Embed guardrails and policies addressing hallucination risk, bias, unauthorized actions, and escalation failures.

Produce documentation supporting risk, compliance, internal audit, and regulator engagement.

Team Leadership & Firm Contribution

Provide technical leadership and mentorship to consulting delivery teams.

Contribute to internal GenAI accelerators, agent frameworks, and reusable assets.

Support RFPs, proposals, and client solution designs with credible GenAI and agentic architectures.

Participate in thought leadership on Agentic SDLC, GenAI engineering, and responsible autonomy.

Benefits

This position comes with competitive compensation and benefits package:

Competitive salary and performance-based bonuses

Comprehensive benefits package

Career development and training opportunities

Flexible work arrangements (remote and/or office-based)

Dynamic and inclusive work culture within a globally known group

Private Health Insurance

Retirement Benefits

Paid Time Off

Training & Development

XML job scraping automation by YubHub

The future of entertainment is interactive, and you can help lead that future by growing and enriching how hundreds of millions of people find joy and belonging, forge friendships, and celebrate their lived experiences through the work we do every single day, together.

As an AI Enablement Engineer, you will be the hands-on AI architect and engineer, reporting to the Head of AI Enablement for EA Experiences. You will design, create, and deploy AI-powered solutions that transform content creation, data and analytics, and transform our business processes and systems.

Responsibilities:

• Architect end-to-end AI solutions: Design and implement AI-native solutions and multi-agent workflows that transform content creation, data and analytics, and core business processes, from idea through production deployment.
• Drive AI-enabled content creation: Build and evolve AI pipelines, assistants, and tools that help creative, marketing, and franchise teams ideate, generate, localize, and optimize content (image, video, copy, interactive experiences) at scale while staying on-brand and fan-first.
• Lead data, knowledge, and RAG architectures: Define and implement data lakes, vector stores, knowledge bases, and knowledge graphs to power intelligent assistants and solutions.
• Productionize and integrate AI systems: Own the path from prototype to production,integrating AI solutions with existing platforms, services, and workflows, and defining observability, reliability, and performance baselines.
• Embed guardrails, safety, and governance: Implement guardrails, policies, and evaluation frameworks that address IP, privacy, security, safety, and bias; define acceptance criteria, red/amber/green thresholds, and incident handling for AI-powered systems.
• Standardize architectures, patterns, and tooling: Create and maintain reusable reference architectures, design patterns, SDKs, and templates for agents, multi-agent workflows, RAG, and LLM/vision/diffusion integrations to accelerate adoption across teams.
• Partner and co-create with cross-functional teams: Collaborate with creative, product, marketing, data, engineering, operations, and legal partners to identify high-value AI opportunities, shape requirements, and ensure solutions are usable, scalable, and aligned to business goals.
• Lead experimentation and continuous improvement: Run “prove, pilot, production” cycles, define success metrics, analyze performance and fan impact, and continuously refine models, prompts, workflows, and UX based on data and stakeholder feedback. Develop and lead AI first engineering processes to accelerate product, engineering, and delivery.
• Evangelize and upskill the organization: Provide thought leadership on AI, share best practices, host demos and workshops, and coach teams on how to leverage agents, LLMs, vision/diffusion models, and AI workflows as force-multipliers in their day-to-day work.

Your Qualifications

• 8+ years of experience architecting, prototyping, and deploying robust production solutions that incorporate software engineering, infrastructure, architectural, and security best practices.
• Strong Python development skills; experience with C#, JavaScript, HTML, and CSS is beneficial.
• Hands-on experience using agentic AI software engineering solutions (e.g., Kiro, Cursor, or similar) to accelerate development and experimentation.
• 3+ years implementing AI solutions driving measurable business impact.
• 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, evaluation, benchmarking, and guardrails.
• Experience tuning language and diffusion models, labeling and tagging data, and architecting data pipelines.
• Deep understanding of architecture and design principles, and cloud services, specifically AWS, Azure and GCP are beneficial.
• Enterprise architecture and microservices design; observability and telemetry; infrastructure as code and CI/CD pipelines.
• Experience navigating the legal, ethical, and security implications for AI, including data privacy, IP, safety, and responsible use of Generative AI.
• 4+ years experience leading cross-functional teams and collaborate effectively with global teams.
• Experience approaching a problem from different angles, analyzing pros and cons of different solutions, and analytical problem-solving skills, with the ability to reframe problems, explore multiple options, and land pragmatic, high-impact solutions.
• Excellent ability to clearly communicate complex concepts simply and clearly. Weave an engaging narrative that clearly communicates complex concepts and demonstrates business impact.
• Customer- and player-centric mindset, with a passion for building solutions that empower teams and delight fans.
• Enjoy and prioritize continual learning – staying up to date with the newest AI technologies, models, tools, and solution patterns, and understanding how they can benefit EA Experiences.
• Thrive working both collaboratively and independently in a fast-paced, dynamic environment; comfortable operating in a startup-like setting within a large organization.

XML job scraping automation by YubHub

The EA Experiences group (XO) is dedicated to ensuring great experiences for our growing communities centered around our world-renowned brands, including fan-favorites like Apex, Battlefield, EA SPORTS FC, Madden NFL and The Sims.

As an AI Enablement Architect and Team Lead for the XO AI Labs team in the EA Experiences organization, you will be an innovator and creator of cutting-edge AI solutions, solutions that keep our fans at the center of everything we do. You will collaborate closely with teams across EA Experiences, developing an understanding of their business, their challenges, and their opportunities. You will rapidly create innovative AI solutions using cutting-edge AI technologies that drive growth, increase efficiency, and help our teams focus on what matters most, our fans.

This is a hands-on leadership role focused on building and deploying production AI solutions for EA Experiences organization. You will report to the Senior Director, AI Enablement. This is a hybrid role based at our office in Galway, Ireland.

Responsibilities:

• Inspire and lead AI-driven transformation by creating and deploying innovative solutions that drive efficiency, expansion, and transformation to scale impact, and reimagine our employee and fan experiences.

• Be an AI evangelist, promoting the use of AI as a force-multiplier, driver of growth, and method for improving our fans experiences

• 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

• Be a hands-on leader, collaborating with teams across EA Experiences to understand their business, their challenges, their opportunities, and create innovative solutions.

• Innovate, transform, and imagine what can be with the novel application of AI into business and related solutions. Drive organizational transformation and change management.

• Demonstrated ability to clearly and succinctly articulate complex concepts at a senior level

Qualifications:

• 8 years + experience architecting, developing, rapidly prototyping and deploying robust production solutions incorporating software engineering, infrastructure, architectural, and security best practices

• Python development skills are required. C#, JavaScript, HTML, and CSS are beneficial

• Extensive experience with spec driven development and using agentic AI software engineering solutions such as Kiro, Cursor, and 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

• Content creation (advertising and marketing), data analytics, and business transformation experience is highly beneficial

• A comprehensive understanding of AWS's services, architecture, and design principles, including:

• 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

• Experience integrating AI solutions specifically with AAA Console/PC games and businesses is beneficial

About Electronic Arts:

We’re proud to have an extensive portfolio of games and experiences, locations around the world, and opportunities across EA. We value adaptability, resilience, creativity, and curiosity. From leadership that brings out your potential, to creating space for learning and experimenting, we empower you to do great work and pursue opportunities for growth.

We adopt a holistic approach to our benefits programs, emphasizing physical, emotional, financial, career, and community wellness to support a balanced life. Our packages are tailored to meet local needs and may include healthcare coverage, mental well-being support, retirement savings, paid time off, family leaves, complimentary games, and more. We nurture environments where our teams can always bring their best to what they do.

XML job scraping automation by YubHub

The future of entertainment is interactive, and you can help lead that future by growing and enriching how hundreds of millions of people find joy and belonging, forge friendships, and celebrate their lived experiences through the work we do every single day, together.

As the hands-on AI architect and engineer, you will design, create, and deploy AI-powered solutions that transform content creation, data and analytics, and transform our business processes and systems. You'll architect and ship innovative AI native solutions that leverage agents, multi-agent workflows, guardrails, knowledge bases, knowledge graphs, LLM's, vision models, diffusion models, and related technologies.

Responsibilities:

• Architect end-to-end AI solutions: Design and implement AI-native solutions and multi-agent workflows that transform content creation, data and analytics, and core business processes, from idea through production deployment.

• Drive AI-enabled content creation: Build and evolve AI pipelines, assistants, and tools that help creative, marketing, and franchise teams ideate, generate, localize, and optimize content (image, video, copy, interactive experiences) at scale while staying on-brand and fan-first.

• Lead data, knowledge, and RAG architectures: Define and implement data lakes, vector stores, knowledge bases, and knowledge graphs to power intelligent assistants and solutions

• Productionize and integrate AI systems: Own the path from prototype to production,integrating AI solutions with existing platforms, services, and workflows, and defining observability, reliability, and performance baselines.

• Embed guardrails, safety, and governance: Implement guardrails, policies, and evaluation frameworks that address IP, privacy, security, safety, and bias; define acceptance criteria, red/amber/green thresholds, and incident handling for AI-powered systems.

• Standardize architectures, patterns, and tooling: Create and maintain reusable reference architectures, design patterns, SDKs, and templates for agents, multi-agent workflows, RAG, and LLM/vision/diffusion integrations to accelerate adoption across teams.

• Partner and co-create with cross-functional teams: Collaborate with creative, product, marketing, data, engineering, operations, and legal partners to identify high-value AI opportunities, shape requirements, and ensure solutions are usable, scalable, and aligned to business goals.

• Lead experimentation and continuous improvement: Run “prove, pilot, production” cycles, define success metrics, analyze performance and fan impact, and continuously refine models, prompts, workflows, and UX based on data and stakeholder feedback. Develop and lead AI first engineering processes to accelerate product, engineering, and delivery

• Evangelize and upskill the organization: Provide thought leadership on AI, share best practices, host demos and workshops, and coach teams on how to leverage agents, LLMs, vision/diffusion models, and AI workflows as force-multipliers in their day-to-day work.

Your Qualifications:

• 8+ years of experience architecting, prototyping, and deploying robust production solutions that incorporate software engineering, infrastructure, architectural, and security best practices.

• Strong Python development skills; experience with C#, JavaScript, HTML, and CSS is beneficial.

• Hands-on experience using agentic AI software engineering solutions (e.g., Kiro, Cursor, or similar) to accelerate development and experimentation.

• 3+ years implementing AI solutions driving measurable business impact.

• 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, evaluation, benchmarking, and guardrails.

• Experience tuning language and diffusion models, labeling and tagging data, and architecting data pipelines

• Deep understanding of architecture and design principles, and cloud services, specifically AWS, Azure and GCP are beneficial

• Enterprise architecture and microservices design; observability and telemetry; infrastructure as code and CI/CD pipelines

• Experience navigating the legal, ethical, and security implications for AI, including data privacy, IP, safety, and responsible use of Generative AI.

• 4+ years experience leading cross-functional teams and collaborate effectively with global teams

• Experience approaching a problem from different angles, analyzing pros and cons of different solutions, and analytical problem-solving skills, with the ability to reframe problems, explore multiple options, and land pragmatic, high-impact solutions.

• Excellent ability to clearly communicate complex concepts simply and clearly. Weave an engaging narrative that clearly communicates complex concepts and demonstrates business impact.

• Customer- and player-centric mindset, with a passion for building solutions that empower teams and delight fans.

• Enjoy and prioritize continual learning – staying up to date with the newest AI technologies, models, tools, and solution patterns, and understanding how they can benefit EA Experiences.

• Thrive working both collaboratively and independently in a fast-paced, dynamic environment; comfortable operating in a startup-like setting within a large organization.

XML job scraping automation by YubHub

As a Director & Architect, AI Innovations, you will report to and collaborate closely with the Head of AI Enablement for EA Experiences and collaborate closely with a global team across Europe, North America, and Southeast Asia.

Responsibilities:

• Deeply understand the future of Generative AI technologies, with a strong grasp of traditional AI and ML
• Rapidly prototype and creative innovative solutions that drive significant increases in player engagement, expansion of EA's core business, and efficiency opportunities
• Design and implement GenAI-native solutions and multi-agent workflows that transform content creation, data and analytics, and core business processes, from idea through production deployment.
• Drive AI-enabled content creation: Build and evolve AI pipelines, assistants, and tools that help creative, marketing, and franchise teams ideate, generate, localize, and optimize content (image, video, copy, interactive experiences) at scale while staying on-brand and fan-first.
• Productionize and integrate AI systems: Own the path from prototype to production,integrating AI solutions with existing platforms, services, and workflows, and defining observability, reliability, and performance baselines.
• Drive adoption of new GenAI based architectures, patterns, and tooling: Create and maintain reusable reference architectures, design patterns, SDKs, and templates for agents, multi-agent workflows, RAG, and LLM/vision/diffusion integrations to accelerate adoption across teams.
• Partner and co-create with cross-functional teams: Collaborate with creative, product, marketing, data, engineering, operations, and legal partners to identify high-value AI opportunities, shape requirements, and ensure solutions are usable, scalable, and aligned to business goals.
• Lead experimentation and continuous improvement: Run “prove, pilot, production” cycles, define success metrics, analyze performance and fan impact, and continuously refine models, prompts, workflows, and UX based on data and stakeholder feedback. Develop and lead AI first engineering processes to accelerate product, engineering, and delivery
• Evangelize and upskill the organisation: Provide thought leadership on AI, share best practices, host demos and workshops, and coach teams on how to leverage agents, LLMs, vision/diffusion models, and AI workflows as force-multipliers in their day-to-day work.

Qualifications:

• 8+ years of experience architecting, prototyping, and deploying robust production solutions that incorporate software engineering, infrastructure, architectural, and security best practices.
• Strong Python development skills; experience with C#, JavaScript, HTML, and CSS is beneficial.
• Hands-on experience using agentic AI software engineering solutions (e.g., Kiro, Cursor, or similar) to accelerate development and experimentation.
• 3+ years implementing AI solutions driving measurable business impact.
• 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, evaluation, benchmarking, and guardrails.
• Experience tuning language and diffusion models, labeling and tagging data, and architecting data pipelines
• Deep understanding of architecture and design principles, and cloud services, specifically AWS, Azure and GCP are beneficial
• Enterprise architecture and microservices design; observability and telemetry; infrastructure as code and CI/CD pipelines

XML job scraping automation by YubHub

Your focus will span end-to-end semiconductor workflows, from chip design and verification through tooling and manufacturing-adjacent systems. You will help expand OpenAI's footprint across the stack, shaping how frontier models are applied throughout the semiconductor lifecycle.

You will measure success through production adoption, cycle-time reduction, engineer productivity gains, and evaluation-driven feedback loops that inform product, model, and platform strategy. You'll work closely with Product, Research, GTM, and Partnerships to turn early wins into a durable semiconductor vertical offering.

This role operates in environments where correctness, scale, and trust matter , regressions cost weeks, failures block tape-out, and credibility is earned through technical rigor.

This role is based in San Francisco. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required.

In this role you will:

• Design and ship production AI systems around models, owning integrations with RTL repositories, verification environments, simulators, and internal tooling.

• Lead discovery and scoping from pre-engagement through production rollout, translating ambiguous engineering pain points into hypothesis-driven use cases with measurable outcomes.

• Deliver AI-powered verification workflows such as change-aware test selection, directed test generation, and intelligent regression triage, taking them from prototype to daily production use.

• Build systems that operate over large, evolving codebases and artifacts (RTL, tests, logs, waveforms, traces), where performance, latency, and failure handling shape architecture.

• Define and run evaluation loops that measure model and system quality against workflow-specific benchmarks (e.g., coverage, false positives, debug time, iteration speed).

• Own delivery state across multiple workstreams, making trade-offs between scope, speed, and robustness to protect production impact.

• Distill deployment learnings into hardened primitives, reference implementations, playbooks, and tooling that can be reused across customers.

• Surface field insights that inform model behavior, tooling gaps, and future product direction across the semiconductor stack.

You might thrive in this role if you:

• Bring 5+ years of engineering experience in chip design, verification, EDA, or FPGA development (including RTL design, timing closure, and hardware/software co-design), or closely adjacent systems domains such as firmware, distributed systems, compilers, or performance-critical infrastructure.

• Have worked directly with RTL, verification environments, simulators, or large-scale performance/debug tooling , or have partnered closely with teams who do.

• Have delivered complex systems end-to-end in environments where scale, correctness, and long feedback loops shaped how you build and ship.

• Write and review production-grade code in Python and/or systems-adjacent languages, and are comfortable integrating across heterogeneous toolchains.

• Have experience deploying or experimenting with LLM-powered systems and understand how model behavior, evaluation, and guardrails affect trust and adoption.

• Communicate clearly with hardware engineers, software engineers, product teams, and executives, translating technical trade-offs into delivery decisions.

• Apply systems thinking with high execution standards, turning failures, regressions, and unexpected model behavior into improved operating patterns.

• Stay calm and decisive in technically deep, high-stakes environments where progress depends on credibility and follow-through.

Success in this role means shipping AI systems that semiconductor engineers trust in their daily workflows, establishing repeatable deployment patterns across chip design and verification, and helping OpenAI become a long-term partner across the semiconductor ecosystem.

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About the Role Copilot is entering a new era of agentic AI, where intelligent agents take actions on behalf of users. We’re looking for a Software Engineer II with solid fundamentals and high growth potential,someone who can quickly deepen their expertise in AI-driven security and expand their ownership over time. You’ll contribute to secure orchestration frameworks, AI-powered defenses, and the core systems that ensure Copilot’s actions remain trustworthy.

Responsibilities Build and ship security features that protect Copilot from threats such as prompt injection, adversarial manipulation, and unsafe agentic workflows. Implement secure orchestration components that allow Copilot to safely delegate and execute actions across devices, services, and platforms. Contribute to developing intelligent agents that apply information-flow reasoning, guardrails, and common-sense constraints for security and privacy. Collaborate with partner teams across engineering, product, security, privacy, and AI to adopt secure agentic patterns and best practices. Instrument and monitor key metrics for agentic AI security, using data to improve reliability, safety, and user trust. Write clear documentation for secure agentic patterns, including safe-delegation guidelines and emerging risk considerations. Demonstrate high growth potential by progressively expanding technical scope, autonomy, and ownership as you gain experience with agentic AI and security systems.

Qualifications Required Qualifications: Bachelor’s Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Preferred Qualifications: Master’s Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor’s Degree in Computer Science or related technical field AND 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Experience building production-quality software systems. 1–2+ years building or operating large-scale distributed systems or services. Experience working on security-critical, privacy-sensitive, or AI-powered systems. Familiarity with agentic AI concepts such as tool calling, orchestration, or multi-agent workflows. Experience with modern cloud development, containerization (Docker, Kubernetes), or distributed compute frameworks. Exposure to evaluation or observability tooling for LLM-based applications (e.g., LangFuse, MLFlow, Phoenix) or interest in learning these systems. Ability to communicate technical concepts clearly and collaborate effectively across teams. Demonstrated high growth potential, with solid learning velocity and the ability to quickly take on broader areas of ownership. Growth mindset with interest in developing deeper expertise in AI security, orchestration, and emerging threat models.

#MicrosoftAI #MAI DPS

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About the Role Copilot is entering a new era of agentic AI, where intelligent agents take actions on behalf of users. We’re looking for a Software Engineer II with solid fundamentals and high growth potential,someone who can quickly deepen their expertise in AI-driven security and expand their ownership over time. You’ll contribute to secure orchestration frameworks, AI-powered defenses, and the core systems that ensure Copilot’s actions remain trustworthy. This role is ideal for engineers who enjoy solving complex technical problems, learning new AI-driven patterns, and building secure, scalable systems that balance innovation with user trust.

Why This Role Matters Your work will directly shape how hundreds of millions of users experience safe, trustworthy, and innovative AI. You’ll be at the forefront of defining how agentic AI can proactively defend users, mitigate emerging threats, and unlock new secure scenarios, making a global impact on Microsoft’s most transformative products.

Responsibilities Build and ship security features that protect Copilot from threats such as prompt injection, adversarial manipulation, and unsafe agentic workflows. Implement secure orchestration components that allow Copilot to safely delegate and execute actions across devices, services, and platforms. Contribute to developing intelligent agents that apply information-flow reasoning, guardrails, and common-sense constraints for security and privacy. Collaborate with partner teams across engineering, product, security, privacy, and AI to adopt secure agentic patterns and best practices. Instrument and monitor key metrics for agentic AI security, using data to improve reliability, safety, and user trust. Write clear documentation for secure agentic patterns, including safe-delegation guidelines and emerging risk considerations. Demonstrate high growth potential by progressively expanding technical scope, autonomy, and ownership as you gain experience with agentic AI and security systems.

Qualifications Required Qualifications: Bachelor’s Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Preferred Qualifications: Master’s Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor’s Degree in Computer Science or related technical field AND 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

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Copilot Security and Privacy is responsible for building these defenses directly into Copilot. Our work focuses on new security primitives for agentic AI, including runtime misuse detection, adaptive guardrails, containment and isolation mechanisms, and feedback-driven control systems informed by offensive security research.

We are hiring a Principal Technical Program Manager (TPM) to own the end-to-end delivery of these capabilities. This is a deeply technical execution role for someone who can operate at the boundary of security engineering, AI research, and platform systems,turning ambiguous threat models into shippable, operable defenses deployed in a globally scaled AI product.

This role is not about process, governance, or coordination. The TPM is accountable for making complex systems land in production, under real-world adversarial pressure. Most security roles protect systems after they exist. This role helps define how agentic AI systems defend themselves while they operate.

You will shape how Copilot detects misuse, enforces boundaries, and recovers safely in real time,working directly on the mechanisms that make autonomy deployable at global scale. The impact is immediate, technical, and measurable in production behavior.

If you want to operate where AI systems, security engineering, and execution reality intersect, this role offers that surface area,without turning you into a policy owner or process layer.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

Own Delivery of In-Product AI Threat Defenses

Lead execution of runtime threat defense capabilities embedded directly into Copilot execution paths, not layered on externally.

Drive delivery of detection, prevention, and containment mechanisms that operate synchronously and adaptively as agents reason and act.

Ensure defenses are designed as control systems with clear signals, enforcement points, and feedback loops.

Translate Threat Models into Executable Systems

Take emerging and ambiguous agentic AI threat models,including misuse, escalation, and information-flow risks,and convert them into concrete engineering plans.

Partner with security engineers and researchers to translate offensive security insights and red-team findings into production features.

Make judgment calls about enforcement boundaries, degradation strategies, and isolation guarantees.

Drive Cross-Cutting Technical Execution

Coordinate delivery across security engineering, AI research, platform/runtime teams, and Copilot product surfaces.

Own dependency management, sequencing, and delivery risk for systems that are tightly coupled and cannot be built independently.

Resolve technical and organizational tradeoffs where ownership boundaries are unclear and failure modes are novel.

Ensure Operability at Runtime

Define what “working” means for threat defenses: detection quality, false-positive tolerance, performance impact, and blast-radius containment.

Ensure defenses are measurable, testable, and observable in production.

Lead learning loops from live incidents, near-misses, and adversarial testing back into system design.

Qualifications:

Required Qualifications:

Bachelor’s Degree AND 6+ years experience in engineering, product/technical program management, data analysis, or product development OR equivalent experience.

3+ years of experience managing cross-functional and/or cross-team projects.

Preferred Qualifications:

Bachelor’s Degree AND 12+ years experience engineering, product/technical program management, data analysis, or product development OR equivalent experience.

Proven ability to lead execution in high-ambiguity environments where requirements, threats, and system behavior evolve rapidly.

Solid systems thinking: ability to reason about execution paths, failure modes, and adversarial behavior.

Track record of making sound technical tradeoffs and shipping durable solutions without relying on heavy process.

Background in security engineering, distributed systems, applied research, or ML systems prior to or alongside TPM work.

Experience delivering runtime detection, abuse prevention, or adaptive enforcement systems.

Familiarity with agentic AI systems, LLM-based products, or non-deterministic execution environments.

Experience partnering closely with offensive security or red-team functions.

Demonstrated ability to translate research, prototypes, or threat models into production-grade systems.

Solid analytical skills, including working with telemetry, signals, and feedback loops.

#MicrosoftAI #MAIDPS

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Copilot Security and Privacy is responsible for building these defenses directly into Copilot. Our work focuses on new security primitives for agentic AI, including runtime misuse detection, adaptive guardrails, containment and isolation mechanisms, and feedback-driven control systems informed by offensive security research.

We are hiring a Principal Technical Program Manager (TPM) to own the end-to-end delivery of these capabilities. This is a deeply technical execution role for someone who can operate at the boundary of security engineering, AI research, and platform systems,turning ambiguous threat models into shippable, operable defenses deployed in a globally scaled AI product.

This role is not about process, governance, or coordination. The TPM is accountable for making complex systems land in production, under real-world adversarial pressure. Most security roles protect systems after they exist. This role helps define how agentic AI systems defend themselves while they operate.

You will shape how Copilot detects misuse, enforces boundaries, and recovers safely in real time,working directly on the mechanisms that make autonomy deployable at global scale. The impact is immediate, technical, and measurable in production behavior.

If you want to operate where AI systems, security engineering, and execution reality intersect, this role offers that surface area,without turning you into a policy owner or process layer.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Responsibilities:

Own Delivery of In-Product AI Threat Defenses

Lead execution of runtime threat defense capabilities embedded directly into Copilot execution paths, not layered on externally.

Drive delivery of detection, prevention, and containment mechanisms that operate synchronously and adaptively as agents reason and act.

Ensure defenses are designed as control systems with clear signals, enforcement points, and feedback loops.

Translate Threat Models into Executable Systems

Take emerging and ambiguous agentic AI threat models,including misuse, escalation, and information-flow risks,and convert them into concrete engineering plans.

Partner with security engineers and researchers to translate offensive security insights and red-team findings into production features.

Make judgment calls about enforcement boundaries, degradation strategies, and isolation guarantees.

Drive Cross-Cutting Technical Execution

Coordinate delivery across security engineering, AI research, platform/runtime teams, and Copilot product surfaces.

Own dependency management, sequencing, and delivery risk for systems that are tightly coupled and cannot be built independently.

Resolve technical and organizational tradeoffs where ownership boundaries are unclear and failure modes are novel.

Ensure Operability at Runtime

Define what “working” means for threat defenses: detection quality, false-positive tolerance, performance impact, and blast-radius containment.

Ensure defenses are measurable, testable, and observable in production.

Lead learning loops from live incidents, near-misses, and adversarial testing back into system design.

Qualifications:

Required Qualifications:

Bachelor’s Degree AND 6+ years experience in engineering, product/technical program management, data analysis, or product development OR equivalent experience.

3+ years of experience managing cross-functional and/or cross-team projects.

Preferred Qualifications:

Bachelor’s Degree AND 12+ years experience engineering, product/technical program management, data analysis, or product development OR equivalent experience.

Proven ability to lead execution in high-ambiguity environments where requirements, threats, and system behavior evolve rapidly.

Solid systems thinking: ability to reason about execution paths, failure modes, and adversarial behavior.

Track record of making sound technical tradeoffs and shipping durable solutions without relying on heavy process.

Background in security engineering, distributed systems, applied research, or ML systems prior to or alongside TPM work.

Experience delivering runtime detection, abuse prevention, or adaptive enforcement systems.

Familiarity with agentic AI systems, LLM-based products, or non-deterministic execution environments.

Experience partnering closely with offensive security or red-team functions.

Demonstrated ability to translate research, prototypes, or threat models into production-grade systems.

Solid analytical skills, including working with telemetry, signals, and feedback loops.

#MicrosoftAI #MAIDPS

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We are looking for world-class Software Engineers to join our core Backend Engineering teams. You will design, build, and scale the distributed systems that power one of the largest data and AI infrastructures in the world. You will tackle high-scale infrastructure challenges where 'zero-waste' compute efficiency and 100% system resilience are mandatory. From optimising massive data storage layers to architecting low-latency microservices that must function in disconnected regions, you will own the end-to-end lifecycle of mission-critical code.

Key responsibilities include:

• Architecting scalable systems using Scala, Java, Go, C++/C or similar
• Designing and implementing highly available backend services in Java, Scala, or Go that power our Data Intelligence Platform across sovereign and air-gapped clouds
• Solving complex architectural trade-offs around concurrency, storage, and networking to ensure the platform remains resilient in high-consequence public sector environments
• Driving 'zero-waste' compute initiatives, optimising our engine and services to perform in resource-constrained or specialised regions (e.g., GovCloud)
• Partnering with Production Engineering to ensure all backend services meet the highest security standards and compliance guardrails while maintaining high availability

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This role requires deep fluency in how large language models work, hands-on experience with AI agent architectures, and the ability to partner closely with Engineering on production systems.

As an AI Agent Architect, you will:

Own Agent retrieval accuracy and relevance. Architect the knowledge systems that enable AI agents to surface the right answer on the first try. Measure and improve retrieval precision, contextual relevance, and hallucination rates.

Drive automated resolution rates. Build the decision frameworks that allow agents to take confident actions. What APIs do agents need to access? When can they make account modifications? You're accountable for encoding business logic into auditable, predictable systems that resolve issues without human intervention.

Manage AI safety and trust. Establish the guardrails that keep resolution rates high while failure rates stay low. You're responsible for what the agent doesn't do wrong: edge cases caught, prompt injection blocked, unintended behaviours prevented.

Own the feedback loop. Monitor the observability layer that turns agent behaviour into actionable insights. Instrument retrieval accuracy, action success rates, and failure patterns. Use this data to drive measurable week-over-week improvements in agent performance.

Continuously improve agent quality. Develop and maintain the prompt architecture that governs how agents reason and respond. Build systematic approaches to versioning, A/B testing, and performance evaluation, measuring consistency, accuracy, and adaptability across scenarios.

Drive integration strategy. Architect how agents connect to external systems,billing platforms, CRMs, internal tools, Airtable APIs. Define authentication patterns, error handling, and data transformation. Uptime, error rates, and data accuracy are your metrics.

You are:

Familiar with concepts like RAG architectures, prompt engineering patterns, chain-of-thought reasoning, and agent frameworks. You've built or significantly contributed to AI-powered systems in production.

Able to think in terms of data flows, state management, error handling, and edge cases. You can design complex systems that are both powerful and reliable. You've likely worked in roles like solutions architecture, platform engineering, or technical program management.

Able to write scripts, work with APIs, query databases, and prototype solutions. You're not a full-time software engineer, but you're dangerous enough to build, test, and validate technical approaches independently.

Able to instrument systems, analyse logs, and use data to diagnose issues and validate improvements. You build dashboards, define metrics, and can tie technical changes to business outcomes like resolution rates and customer satisfaction.

Able to explain complex AI system behaviour to non-technical stakeholders, write clear technical documentation, and translate business requirements into system specifications. You're effective working across engineering, product, and operations teams.

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The team fosters a collaborative environment and is building a best-in-class program to partner with the business to protect the Firm’s information and computer systems. Millennium is a complex and robust technical environment and securing the Firm from external and internal threats is a top priority.

Responsibilities

• Define and implement security guardrails for Generative AI, LLMs, and Agentic frameworks, ensuring safe enterprise adoption.
• Conduct specialized threat modeling, red teaming, and risk assessments for AI/ML models (e.g., testing for prompt injection, model theft, and data poisoning).
• Lead risk management activities, including application risk assessments, design reviews, and mitigation strategies for IT projects.
• Engage throughout the SDLC to identify vulnerabilities, conduct code reviews/penetration testing, and enforce secure coding standards.
• Evangelize AppSec and AI security best practices through developer education, training materials, and outreach.
• Design robust security architectures and integrate automated security testing (SAST/DAST/SCA) into CI/CD pipelines.
• Partner with Technology, Trading, Legal, and Compliance to create policies and communicate technical risks to non-technical stakeholders.

Qualifications

• Bachelor's degree or higher in Computer Science, Computer Engineering, IT Security or related field.
• 5+ years’ experience working as an Application Security Engineer, Software Engineer, or similar role.
• Deep understanding of AI-specific risks (OWASP Top 10 for LLMs) and experience securing applications utilizing LLMs.
• Experience working with AI models, Agentic frameworks and security risks associated with AI.
• Experience in working with global teams, collaborating on code and presentations.
• Demonstrated work experience in hybrid on-premise and Public Cloud environments (AWS/GCP/Azure)
• Strong understanding of security architectures, secure configuration principles/coding practices, cryptography fundamentals and encryption protocols.
• Experience with common SCM & CI/CD technologies like GitHub, Jenkins, Artifactory, etc. and integrating Security Scanning and Vulnerability Management into the CI/CD Pipelines
• Familiarity with static and dynamic security analysis tools, and SCA/SBOM solutions.
• Hands on experience with Secrets Management & Password Vault technologies such as Delinea Secret Server and/or Hashicorp Vault, etc.
• Strong experience in secure programming in languages such as Python, Java, C++, C#, or similar.
• Familiarity with Infrastructure as Code tools (CloudFormation, Terraform, Ansible, etc.)
• Familiarity with web application security testing tools and methodologies.
• Knowledge of various security frameworks and standards such as ISO 27001, NIST, OWASP, etc.
• Knowledge of Linux, OS internals and containers is a plus.
• Certifications like CISSP, CISM, CompTIA Security+, or CEH are advantageous.

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The successful candidate will have a proven understanding in enterprise security and AI security and will focus on defining and implementing security guardrails for Generative AI, LLMs, and Agentic frameworks, ensuring safe enterprise adoption.

Key responsibilities include:

• Defining and implementing security guardrails for Generative AI, LLMs, and Agentic frameworks
• Conducting specialized threat modeling, red teaming, and risk assessments for AI/ML models
• Leading risk management activities, including application risk assessments, design reviews, and mitigation strategies for IT projects
• Engaging throughout the SDLC to identify vulnerabilities, conduct code reviews/penetration testing, and enforce secure coding standards
• Evangelizing AppSec and AI security best practices through developer education, training materials, and outreach

Qualifications include:

• Bachelor's degree or higher in Computer Science, Computer Engineering, IT Security or related field
• 5+ years' experience working as an Application Security Engineer, Software Engineer, or similar role
• Deep understanding of AI-specific risks (OWASP Top 10 for LLMs) and experience securing applications utilizing LLMs
• Experience working with AI models, Agentic frameworks and security risks associated with AI
• Experience in working with global teams, collaborating on code and presentations

Preferred qualifications include:

• Demonstrated work experience in hybrid on-premise and Public Cloud environments (AWS/GCP/Azure)
• Strong understanding of security architectures, secure configuration principles/coding practices, cryptography fundamentals and encryption protocols
• Experience with common SCM & CI/CD technologies like GitHub, Jenkins, Artifactory, etc. and integrating Security Scanning and Vulnerability Management into the CI/CD Pipelines
• Familiarity with static and dynamic security analysis tools, and SCA/SBOM solutions
• Hands on experience with Secrets Management & Password Vault technologies such as Delinea Secret Server and/or Hashicorp Vault, etc.
• Strong experience in secure programming in languages such as Python, Java, C++, C#, or similar
• Familiarity with Infrastructure as Code tools (CloudFormation, Terraform, Ansible, etc.)
• Familiarity with web application security testing tools and methodologies
• Knowledge of various security frameworks and standards such as ISO 27001, NIST, OWASP, etc.
• Knowledge of Linux, OS internals and containers is a plus
• Certifications like CISSP, CISM, CompTIA Security+, or CEH are advantageous

We offer a competitive salary and benefits package, as well as opportunities for professional growth and development.

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You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform.

You will continue to strengthen your technical skills through applying cutting-edge technologies in GenAI, MLOps, and ML more broadly, expanding your impact through mentorship, and establishing yourself as an AI thought leader.

The impact you will have:

• Architect production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimisation, integration with cloud-native services, MLOps, etc.

• Serve as trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems

• Build, scale, and optimise customer AI workloads and apply best-in-class MLOps to productionise these workloads across a variety of domains

• Provide advanced technical support to Solution Architects during the technical sale ranging from feature engineering, training, tracking, serving to model monitoring all within a single platform, as well as participating in the larger ML SME community in Databricks

• Collaborate cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities and influence the product roadmap, helping with the adoption of Databricks' AI offerings

What we look for:

• 5+ years of hands-on industry ML experience in at least one of the following:

• ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.

• AI Engineer: Experience with the latest techniques in LLMs & agentic systems including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI

• Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience

• Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike

• Passion for collaboration, life-long learning, and driving business value through ML & AI

• [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role

• Can meet expectations for technical training and role-specific outcomes within 3 months of hire

• This role can be remote, but we prefer that you be located in the job listing area and can travel up to 30% when needed

Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilising the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here. Local Pay Range $219,100-$301,300 USD

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At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff , payroll, health insurance, 401(k)s, and HR , so owners can focus on their craft and their customers.

With teams in Denver, San Francisco, and New York, we support more than 400,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) , because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.

AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About the Role:

Gusto is scaling our AI-powered risk function to support a complex, multi-entity business operating in highly regulated environments. As the Enterprise Risk Management Lead, you will own and operate Gusto's Enterprise Risk and Third Party Risk Management programs , built AI-first, designed to scale, and built to enable the business to move fast without breaking things.

This is a People Empowerer (manager) role. You balance hands-on program leadership with managing and developing a team of compliance professionals. You navigate the tension between "doing the work" and "leading the work" , contributing directly to complex, high-impact programs while ensuring your team delivers with excellence.

You are a change agent who influences how automated risk management gets done at Gusto, models AI-enabled ways of working, and helps others grow their own capabilities in the process.

You will champion the adoption of AI, machine learning, and process automation across risk monitoring, control testing, incident management, and reporting , and you will partner with Product, Data Science, and Engineering to make it explainable, adopted, compliant, and scalable.

Here’s what you’ll do day-to-day:

You manage initiatives that are complex in both scope and impact, influencing the strategic direction of Gusto's compliance risk management framework.

You apply a deep understanding of the regulatory landscape and how it intersects with Gusto's business model to proactively design and lead cross-functional risk programs.

You translate complex risk topics into clear, actionable guidance that senior leaders can immediately understand and operationalize.

You lead cross-functional working groups, align divergent perspectives, and drive cohesive progress toward shared goals , with minimal oversight.

As a PE, you balance individual risk and compliance contribution with team leadership.

You manage operations, professional development, resource allocation, and performance , while staying close enough to the work to be a credible, hands-on partner to your team and stakeholders.

You model responsible AI use, and act as a source of knowledge and mentorship , supporting your team's AI journey and helping others apply it responsibly and effectively.

AI-Enabled Risk Operations, Innovation & Transformation

This is how you and your team operate , not a side project.

• Champion the adoption of AI, machine learning, process automation, and advanced analytics to improve risk monitoring, control testing, and reporting across ERM, TPRM, and broader compliance functions

• Lead the integration of AI and automation into every phase of the risk lifecycle: vendor assessments, document ingestion and analysis, continuous monitoring and alerting, risk scoring, prioritization, and trend analysis

• Build intelligent risk monitoring and evaluation systems , including auto-tagging for risk issues, audit requests, and regulatory changes , that improve real-time visibility and eliminate manual effort across the enterprise risk portfolio

• Drive the digitalization of risk tools including RCSAs, KRIs, incident reporting, and audit tracking , transforming periodic, reactive processes into continuous intelligence systems with live leading and lagging indicators that enable real-time decision-making

• Partner with Product, Data Science, and Engineering to define requirements for AI-driven workflows, decisioning engines, and dashboards , ensuring explainability, auditability, and regulatory defensibility of all AI-enabled risk decisions

• Design and build intelligent dashboards and reporting tools that deliver real-time risk visibility and decision-quality insights to senior leadership and cross-functional stakeholders

• Design AI workflows with appropriate validation loops, human-in-the-loop checkpoints, and guardrails , ensuring outputs are reliable, governable, and meet regulatory standards before being used to frame risks, recommendations, or decisions

• Stay current on AI advancements and emerging technologies and proactively integrate new capabilities into team operations to increase velocity and scale

• Model responsible AI use , supporting ICs in their AI journeys and fostering a culture of intentional experimentation, accountability, and continuous improvement

Enterprise Risk Management

• Design, implement, and continuously improve Gusto's ERM framework, ensuring alignment with best practices and Gusto's stage of growth and strategic priorities across all entities

• Define and maintain Gusto's enterprise risk taxonomy, risk appetite statement, and key risk indicators spanning operational, regulatory, technology, financial, and reputational risk domains

• Lead Gusto's Enterprise Risk Management process , driving integration of risk practices across business functions, promoting a proactive risk culture, and ensuring incident management, root cause analysis, and lessons learned are systematically captured in an automated, AI forward way.

• Apply AI-assisted insights to enterprise risk datasets to surface systemic patterns, validate assumptions, prioritize risks, and deliver proactive, data-driven advisory to senior leadership

• Monitor the regulatory landscape (OCC, FDIC, CFPB, SEC, FINRA, GDPR, NIST, ISO, SOC) and leverage AI to proactively incorporate changes before they become compliance gaps

• Act as a key advisor to senior compliance leadership , translating complex risk findings into clear, actionable recommendations with minimal oversight

Third Party Risk Management (TPRM)

• Design, implement, and independently manage a high-impact, AI-first TPRM program with clear milestones, progress tracking, and measurable outcomes across all Gusto entities

• Manage the full third-party risk lifecycle , onboarding and risk profiling, periodic assessments, issue management, corrective action tracking, and offboarding , across suppliers, product partners, contractors, service providers, and cloud service providers , and do so in an AI and automated way.

• Maintain a centralized, authoritative vendor risk inventory and risk register, ensuring real-time visibility into Gusto's third-party risk posture

• Conduct periodic AI-driven audits and reviews of third-party compliance with contractual obligations and regulatory standards, identifying patterns that inform continuous program improvement

• Serve as the central orchestrator across Compliance, Security, Legal, Procurement, IT, and GRC for proactive and reactive third-party incident management

• Own Gusto's TPRM policy and maintain comprehensive documentation , risk assessments, audit findings, corrective actions , ensuring full accountability and traceability

People Leadership & Team Development

• Balance individual compliance contribution with team leadership , managing operations, professional development, resource allocation, and performance while staying close to the work

• Coach and develop ICs toward next

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The Support Operations team at Anthropic is dedicated to optimising and scaling the Product Support function to deliver exceptional user experiences. As a Support Operations Specialist focusing on AI Agent Management, you will sit within our AI Support function and be instrumental in building and improving the capabilities of Fin, our AI support agent.

This role sits at the intersection of conversation design and support automation. You'll shape how our AI agent communicates,its tone, structure, handoff logic, and interaction flow,while also building the backend scaffolding that enables it to take real action on behalf of users.

The goal isn't just an AI that answers questions; it's an AI that resolves issues end-to-end: processing refunds, managing subscriptions, verifying account details, and routing complex cases to the right humans at the right time.

Working as part of our AI Support function, you'll execute on the Fin optimisation roadmap,configuring AI behaviours, building integrations that expand what Fin can do, designing conversation flows that create a seamless customer experience, and maintaining the automated workflows that power our support operation.

Responsibilities

• Design and optimise conversation flows, triage logic, and handoff behaviours that create seamless transitions between AI and human support across different customer segments
• Configure and maintain Fin's Guidance rules and Attributes to improve AI response quality, tone, and inbound categorisation
• Build and maintain integrations between Fin and external systems (Stripe, status page, etc.) that enable the AI agent to take action on behalf of users,processing refunds, fetching subscription details, surfacing incident status, and more
• Design, implement, and troubleshoot automated workflows for refunds, subscription management, and other transactional support actions, including guardrails, validation logic, and exception handling to ensure safe and reliable execution
• Own Fin over email setup and maintenance, ensuring consistent AI support quality across channels
• Contribute to AI support effectiveness monitoring by tracking resolution rates, automation rates, CSAT, and escalation patterns to identify improvement opportunities
• Provide input on the Fin optimisation roadmap based on hands-on experience with configuration, capability gaps, and conversation analysis
• Partner with the AI Support Systems team on technical dependencies where Fin capabilities intersect with help desk infrastructure

You might be a good fit if you:

• Have 3+ years of experience in support operations, support engineering, chatbot/AI agent configuration, or similar roles in a technology company
• Have hands-on experience configuring AI agents, chatbots, or automation tools,ideally within Intercom, Zendesk, or similar platforms
• Think in terms of conversation design: you naturally consider how tone, structure, and flow shape the customer experience, not just whether the right answer gets delivered
• Are technically comfortable working with integrations, APIs, and workflow builders without needing engineering support for every change
• Understand that enabling an AI agent to take action requires careful attention to guardrails, edge cases, and safe execution paths,not just connecting systems
• Demonstrate a data-driven approach, using metrics like automation rates, resolution rates, and CSAT to evaluate whether capabilities are working and where to iterate
• Can manage multiple in-flight projects simultaneously, balancing new capability builds with maintenance of existing workflows
• Are detail-oriented and quality-focused,you test thoroughly before shipping and monitor closely after
• Communicate clearly with both technical and non-technical stakeholders
• Thrive in a fast-paced environment where the product and support landscape evolve rapidly
• Are genuinely excited about AI and see the potential for AI agents to transform customer support from reactive ticket resolution into proactive, end-to-end problem solving

Strong candidates may also have:

• Experience specifically with Intercom's Fin AI agent or similar AI support tools
• A background in conversation design, UX writing, or content design for conversational interfaces
• Experience building or managing automated workflows that handle transactional actions (refunds, subscription changes, account modifications)
• Familiarity with support metrics frameworks (automation rates, deflection, CSAT) and how operational decisions connect to business outcomes
• Experience working with Stripe integrations or payment/subscription systems
• Background in support engineering or technical support operations

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position 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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. 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 believe that the highest-impact AI research will be big science. At Anthropic, we're building a team of talented researchers, engineers, and policymakers who share our vision for a future where AI is developed and used responsibly.

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You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform.

Your responsibilities will include:

• Architecting production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimization, integration with cloud-native services, MLOps, etc.

• Serving as a trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems

• Building, scaling, and optimizing customer AI workloads and applying best-in-class MLOps to productionize these workloads across a variety of domains

• Providing advanced technical support to Solution Architects during the technical sale ranging from feature engineering, training, tracking, serving to model monitoring all within a single platform, as well as participating in the larger ML SME community in Databricks

• Collaborating cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities and influence the product roadmap, helping with the adoption of Databricks' AI offerings

We are looking for someone with 5+ years of hands-on industry ML experience in at least one of the following areas:

• ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring.

• AI Engineer: Experience with the latest techniques in LLMs & agentic systems including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI

A graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience is also required.

Additionally, experience communicating and/or teaching technical concepts to non-technical and technical audiences alike is highly valued.

The salary range for this position is $180,000-$247,500 USD, depending on location.

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The team develops and enhances various AI models, ML services and tools including LLM fine-tuning and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb.

You will build and leverage cutting edge AI technologies to transform Airbnb’s customer service by delivering personalized, easy-to-use and proactive customer service experience.

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

Your expertise should include a PhD/Master’s degree, preferably in CS, or equivalent experience, and 5-12 years of ML engineering experience, with ownership responsibility over large-scale software systems.

Experience with LLM driven chatbot and Agentic AI products would be a big plus.

Excellent communication skills and the ability to work well within a team and with teams across the engineering, product & design organizations are also required.

Fluency in both English and Mandarin is essential.

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As the portfolio grows, this role is expected to evolve into team leadership. You'll play a key role in shaping how risk decisions translate into customer experience, product growth, and long-term portfolio performance.

Key responsibilities include:

• Building and operationalizing the credit strategy

• Translating underwriting vision into formal credit policy and decision frameworks

• Defining approval logic, segmentation strategy, and limit-setting methodology

• Establishing portfolio guardrails aligned to loss targets and unit economics

• Designing account management strategies across the customer lifecycle

• Developing early portfolio management approaches including exposure adjustments, servicing strategies, and input into collections processes as the portfolio matures

• Defining portfolio monitoring frameworks and escalation triggers for emerging credit risk trends

• Building the data-driven risk engine

• Implementing credit policy in our underwriting platform

• Evaluating and integrating key data sources (bureau, income, debt signals)

• Ensuring decision logic is structured, testable, and scalable

• Partnering with Engineering and Data to build monitoring and feedback loops

• Owning portfolio performance post-launch

• Defining and tracking core KPIs (approval rate, early delinquency, loss rate, exposure, utilization, etc.)

• Monitoring vintage performance and segment behavior

• Recommending and implementing strategy adjustments based on observed risk trends

• Presenting risk performance, insights, and recommendations to senior leadership

• Driving data-informed risk and growth decisions

• Using SQL to independently evaluate underwriting decisions and trade-offs

• Analyzing drivers of credit performance and portfolio outcomes

• Partnering with Finance on forecasting and risk-adjusted economics

• Driving cross-functional execution

• Partnering with Compliance to ensure the underwriting program is well-documented and built to scale

• Working with Partnerships, Procurement, and Legal on evaluating and onboarding credit data providers

• Supporting broader risk initiatives across our business charge card portfolio during the build phase

Requirements include:

• 6+ years of experience in consumer credit risk
• Experience launching or materially redesigning a consumer lending product
• Experience implementing credit policy within a decisioning or underwriting platform is strongly preferred
• Demonstrated experience owning risk strategy and monitoring portfolio performance
• Deep familiarity with bureau data and core credit risk metrics (approval rate, loss rate, vintage curves, etc.)
• Experience presenting risk insights and strategy recommendations to senior stakeholders
• Experience translating policy into production decision logic
• Strong SQL skills and comfort working directly with data
• Comfortable building in ambiguity and operating in a 0→1 environment

Total rewards package includes base salary, equity, and benefits. Salary and equity ranges are highly competitive within the SaaS and fintech industry.

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Start with an opening paragraph (no heading): what the role is, who the company is, why it matters. If the ad mentions salary, include it here. One short paragraph about the company is enough — do not reproduce lengthy "About Us" text.

For the role details, reuse the same section headings from the original ad (e.g. if the ad says "Responsibilities", use that heading, not "What you'll do"). Match the tone of the original: if formal, stay formal. If casual, stay casual.

Rephrase bullet points in your own words while keeping the factual content. Combine related points where it makes sense.

Content that is not directly about the role (long company history, mission statements, investor lists, press quotes) should be paraphrased into a sentence or two at most — the job seeker needs to understand the company, not read its pitch deck.

For benefits/perks: gather them from anywhere in the ad into one section. If the ad mentions nothing about benefits, omit a benefits section entirely.

Do not invent information that is not in the original ad.

The Role

We're looking for a Founding Product Marketer who will serve as editor-in-chief for all external developer messaging while building the systems and orchestrating the execution that transforms how OpenRouter communicates with developers and customers. This is an IC PMM role with significant Founder and leadership exposure, at the intersection of product marketing and content creation. You ensure that every piece of content that leaves OpenRouter is technically accurate, strategically aligned, and resonates with technical audiences.

You will own the strategy and execution for product launches, model releases, feature announcements, and thought leadership. You'll create the templates, positioning frameworks, and processes that enable founders, engineers, and other builders to communicate authentically and consistently across social media, blog posts, and community channels. This role is about systematizing what works and building the messaging infrastructure that amplifies OpenRouter's technical authority through education, and accessible developer on-ramps.

You will use AI extensively to draft, edit, and prototype content and must have demonstrated experience using AI tools and agents as a part of a high-velocity communications calendar.

What You'll Do

• Serve as Editor-in-Chief: Own editorial quality for all external content—ensuring clarity, consistency, technical accuracy, and strategic alignment across everything OpenRouter publishes.

• Orchestrate and Systematize Content Strategy: Coordinate and enable internal builders and executives to communicate effectively, ensuring coverage of key product launches and a cohesive point of view across all channels.

• Own Product Launch Narratives: Develop positioning and messaging for new features, model integrations, and product releases that establish technical authority and differentiation.

• Build AI-Native Content Systems: Design and manage the "system prompts" for AI-assisted content creation—establishing guardrails, quality standards, and brand guidelines that enable scalable, high-quality output.

• Enable Founder-Led Marketing: Support executives with ghostwriting, content strategy, and messaging guidance for personal accounts on Twitter, LinkedIn, and other platforms.

What We're Looking For

• 5-7 years in product marketing or developer marketing, ideally at a technical platform, API-first company, or developer tool.

• Strong technical fluency with API products, developer workflows, and the ability to understand and communicate complex technical concepts clearly.

• Exceptional writing and editing skills with demonstrated ability to create compelling content across formats (blog posts, social media, web copy, positioning documents).

• Systems thinker with strong process design skills—you build frameworks and templates that enable others to execute consistently.

• Deep familiarity with developer communities on Reddit, Discord, and Twitter/X.

• Experience orchestrating content across multiple internal stakeholders and channels without direct management authority.

• Comfort with AI-native workflows—you embrace automation and AI-assisted content creation while maintaining editorial quality and strategic judgment. You have the technical fluency to serve as first-line reviewer of AI-generated content, validating technical accuracy before engineering review.

• Self-directed and comfortable with ambiguity—you can define the work, build the systems, and execute without a playbook.

Nice to Have

• Prior experience in AI/ML, LLM ecosystems, or developer infrastructure products.

• Track record launching technical products or features with strong content-led strategies.

• Experience building and managing content systems, style guides, and brand voice frameworks.

• Strong social media presence or demonstrated ability to create viral, engaging technical content.

• Familiarity with developer communities, technical Twitter, and how developers discover and evaluate infrastructure tools.

Why OpenRouter

• Work at the center of the AI infrastructure stack as enterprises define how they adopt LLMs.

• Build the content and positioning strategy for a product with clear, tangible ROI: cost efficiency, scalability, flexibility, and innovation velocity.

• High ownership and visibility with direct impact on how OpenRouter is perceived in the market.

• Competitive compensation, including base salary and equity.

• Fully remote team with a strong culture of autonomy and trust.

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We work on the bleeding edge and leverage the most powerful pretrained models and algorithms, making it critical that we ensure our AI systems behave safely and align with organisational values.

You will be responsible for designing novel safety evaluation frameworks, curating high-quality data for robust evaluations and training, prototyping new safety capabilities, and developing safety-focused fine-tuning algorithms.

We're looking for outstanding individuals with deep expertise in multimodal AI safety who can translate research insights into practical solutions while being a strong communicator and collaborative teammate.

The ideal candidate takes the initiative in exploring new safety methodologies and enjoys building world-class, trustworthy AI experiences in a fast-paced applied research environment.

Microsoft's mission is to empower every person and every organisation on the planet to achieve more.

As employees we come together with a growth mindset, innovate to empower others, and collaborate to realise our shared goals.

Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities:

Leverage expertise in multimodal safety to uncover potential risks and develop novel mitigation strategies, including alignment techniques and robustness improvements for multimodal large language models.

Create and implement comprehensive evaluation frameworks and red-teaming methodologies to assess model safety across diverse scenarios, edge cases, and potential failure modes.

Build automated safety testing systems, generalise safety solutions into repeatable frameworks, and write efficient code for safety pipelines and intervention systems.

Maintain a user-oriented perspective by understanding safety needs from user perspectives, validating safety approaches through user research, and serving as a trusted advisor on multimodal safety matters.

Track advances in multimodal safety research, identify relevant state-of-the-art techniques, and adapt safety algorithms to drive innovation in production systems serving millions of users.

Embody our culture and values.

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Location

San Francisco

Employment Type

Full time

Department

Consumer Products

Compensation

• $293K – $325K • 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 Systems Integration team is responsible for building the infrastructure, tooling, and validation systems that ensure our device software is reliable, testable, and ready to ship. We design and maintain automated test frameworks, hardware-in-the-loop labs, and release pipelines that keep quality signals trustworthy and enable rapid, safe product launches. Our work spans developer tools, automation, systems integration, and cross-team collaboration to ensure every release meets the highest standards.

About the Role

As a Software Engineer, Quality and Developer Tools, you will build and own the systems that validate our device software—from test frameworks and regression infrastructure to hardware-in-the-loop labs and release gates. You’ll design the tooling and automation that keep quality signals trustworthy, integrate them into CI/CD, and make it easy for engineers and QA vendor technicians to execute reliable, repeatable workflows.

Responsibilities

• Test infrastructure & frameworks: Design, implement, and maintain a unified test framework for device software across unit, integration, system, and end-to-end testing, with reproducible runs and integrations with GitHub, Linear, and Slack.

• CI/CD integration & releases: Integrate test suites with Buildkite, enforce promotion criteria for staging and production, auto-file regressions, and publish traceable artifacts and release notes.

• Hardware-in-the-loop lab design & orchestration: Plan and bring up racks, power and networking systems, and orchestration for device testing; support automated flashing, provisioning, and telemetry capture.

• Automation and developer tooling: Develop tools for API and firmware validation, result triage, log capture, replayable bug reports, and workflows that improve engineering velocity and debugging efficiency.

• Quality signals, metrics, and flake control: Build dashboards and alerts for pass rates, stability, and release readiness; detect and quarantine flaky tests; drive root-cause analysis with owners; and track delivery metrics that protect release health.

• Vendor enablement: Create clear procedures and tooling that allow QA vendor technicians to execute repeatable processes, review their reports, and maintain a queue of rig maintenance and repairs.

• Cross-team collaboration: Partner with embedded and systems software teams on testability, and with release infrastructure engineers on pipelines, signing, staged rollouts, and rollback/forward strategies.

Requirements

• Have deep experience building software quality, test automation, or developer tooling systems for hardware products shipped at scale.

• Are proficient in Python, C, C++, or Rust, and have strong Linux fundamentals, including processes, networking, storage, and udev/systemd.

• Have experience building CI/CD pipelines, artifact management systems, and reproducible or isolated test environments.

• Have demonstrated success designing and operating hardware-in-the-loop labs and device orchestration systems at scale.

• Are fluent with test reliability techniques such as failure triage, flake detection and quarantine, and signal-quality guardrails.

• Have strong debugging skills across software, firmware, devices, and release infrastructure.

• Work well across teams and enjoy improving the systems that make engineering and release processes more seamless.

What We Offer

• Competitive salary and equity package

• Comprehensive benefits package, including medical, dental, and vision insurance, 401(k) retirement plan, and paid parental leave

• Opportunities for professional growth and development, including training and mentorship programs

• Collaborative and dynamic work environment with a team of experienced professionals

• Flexible work arrangements, including remote work options

• Access to cutting-edge technology and tools

• Recognition and rewards for outstanding performance

How to Apply

If you are a motivated and experienced software engineer looking for a new challenge, please submit your resume and a cover letter explaining why you are a good fit for this role. We look forward to hearing from you!

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Location

San Francisco

Employment Type

Full time

Location Type

Hybrid

Department

Applied AI

Compensation

• $255K – $405K • 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

ChatGPT is a rapidly evolving system: new capabilities ship continuously, product surfaces change quickly, and usage patterns shift week-to-week. Supporting that pace requires infrastructure that can handle real production constraints—high concurrency, unpredictable traffic patterns, complex dependency graphs, and frequent change.

The job of ChatGPT Infrastructure is to build and operate the platforms that make fast iteration possible without breaking performance or reliability. We design the shared systems, data paths, rollout mechanisms, and reliability guardrails that teams rely on when shipping changes to ChatGPT at scale.

We focus on high-leverage infrastructure: primitives and “golden paths” that turn hard-won operational lessons into defaults—so engineers don’t need to rediscover the same failure modes, latency pitfalls, or integration issues each time they build something new.

About the Role

We’re hiring Senior and Staff engineers to design and build infrastructure systems that sit underneath ChatGPT and multiply the effectiveness of the teams building user experiences.

This is not a “keep the lights on” role. It’s a platform-building role: you’ll define interfaces, develop core abstractions, and create tooling that makes safe, fast iteration the norm. Your work will show up as less friction, fewer regressions, better performance, and systems that scale gracefully as the product expands.

Where You Can Have Impact

You might work on one or more of these areas (without being locked into any single lane):

• Platform foundations & frameworks: core libraries, service frameworks, and shared components that standardize how systems are built, integrated, and evolved.

• Scalability & performance primitives: patterns and infrastructure that reduce tail latency, improve throughput, and keep costs predictable as demand grows.

• Reliability guardrails: mechanisms that prevent outages by design—rate limiting, load shedding, dependency isolation, backpressure, safe fallbacks, and “make it hard to regress” controls.

• Developer productivity via golden paths: paved roads for common workflows (data access patterns, service integration patterns, request lifecycle patterns) that are fast, safe, and easy to use.

• Observability & debugging systems: instrumentation, metrics models, and investigative tooling that turn “it’s slow” into a precise, actionable diagnosis.

• Safe change management: deployment and rollout systems that support rapid iteration with confidence—progressive delivery, automated verification, and fast rollback strategies.

• Interface and contract design across boundaries: clean APIs and stable contracts that reduce coupling and allow independent evolution across a complex ecosystem.

What You’ll Do

• Build and evolve infrastructure platforms that many engineers and services depend on.

• Translate messy real-world constraints into clean abstractions: simple APIs, enforceable contracts, safe defaults.

• Drive improvements in reliability and performance through principled design, measurement, and iterative hardening.

• Partner across engineering and product to identify systemic pain points and turn them into reusable solutions.

• Own outcomes end-to-end: design → implementation → rollout → operational maturity.

Qualifications

**Minimum Qualifications

• Experience building and operating large-scale distributed systems in production (high throughput, concurrency, and failure handling).

• Strong fundamentals in systems design, including caching, consistency, queueing/backpressure, and resilient dependency management.

• Ability to reason about performance (latency distributions, tail behavior, bottlenecks) and translate that into concrete engineering work.

• Track record of building platforms

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San Francisco; New York City; Seattle

Employment Type

Full time

Department

Applied AI

Compensation

• $185K – $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.

• 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

We’re building up the core Swift platform, from architecture to CI, that enables hundreds of engineers (and their agents) to develop new capabilities in ChatGPT, Atlas, Sora, and other rapidly evolving apps. Our focus is helping teams move quickly while improving on performance or reliability.

About the Role

We’re hiring Staff+ engineers with a variety of iOS platform backgrounds, including people who may specialize in performance, build/CI, or AI developer productivity. Whether you focus on UI frameworks or automated testing, your work will show up as less friction, less complexity, fewer regressions, and better performance.

If you're rethinking how to scale Swift development in the context of increasingly capable AI tools, this is the role for you.

We are hiring in San Francisco, New York and Seattle.

In this role, you may:

• Build and evolve foundational Swift frameworks used across multiple apps and platforms (caching, state management, observability, navigation, component systems).

• Improve app performance (startup, responsiveness, memory, battery) through profiling, visibility, regression prevention, or architecture patterns.

• Strengthen reliability: systemically reduce crashes, improve error handling, or drive release process improvements.

• Embed with product engineering teams in order to understand pain points or emerging infrastructure needs.

• Develop internal tooling and automation (Bazel, CI, testing, guardrails, Codex agent skills and systems) that increase engineering velocity.

• Translate messy real-world constraints into clean abstractions: simple APIs, enforceable contracts, safe defaults.

You might thrive in this role if you:

• Have strong iOS engineering fundamentals (Swift, concurrency, networking, Xcode ecosystem, some UIKit/SwiftUI) and are familiar with the latest features and practices.

• Gravitate towards platform problems: frameworks, architecture, performance, or tools that empower and accelerate others.

• Take ownership: continually identify the biggest risks/opportunities, drive projects to completion, and update quickly.

• Care about measurement: instrument, define metrics, run experiments, and iterate based on data.

• Have strong communication and collaboration skills—aligning on interfaces, navigating tradeoffs, and driving cross-team execution.

• Tend to think in systems, fight entropy, and enjoy an ambiguous challenge.

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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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 are looking for a talented Member of Technical Staff - Multimodal Safety - MAI Super Intelligence Team 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 Member of Technical Staff, Multimodal Safety, you will work to develop and implement cutting-edge safety methodologies for post-training multimodal large language models to be served to millions of users through Copilot every day. We work on the bleeding edge and leverage the most powerful pretrained models and algorithms, making it critical that we ensure our AI systems behave safely and align with organizational values.

Accountabilities

• Leverage expertise in multimodal safety to uncover potential risks and develop novel mitigation strategies, including alignment techniques and robustness improvements for multimodal large language models.
• Create and implement comprehensive evaluation frameworks and red-teaming methodologies to assess model safety across diverse scenarios, edge cases, and potential failure modes.

The Candidate we're looking for

Experience:

• Bachelor’s Degree in Computer Science, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python.

Technical skills:

• Proven expertise in multimodal LLM safety with experience in diffusion models and generative image/video/audio.

Personal attributes:

• Track record building evaluation frameworks, automated red-teaming, and reusable guardrail systems for safety at scale.

Benefits

• Software Engineering IC4 – The typical base pay range for this role across the U.S. is USD $119,800 – $234,700 per year.
• Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location.

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