Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. Because our technology is purpose-built for restaurants, by restaurant people - and increasingly shaped by AI in both what we build and how we build - restaurants can trust that we'll deliver on their needs for today while investing in the experiences that will power their restaurant of the future.

An AI pod is Toast's AI-native engineering team, a focused pod of specialists who tackle complex, high-impact problems across Toast's product and platform landscape using AI-first approaches.

As Senior Principal Software Engineer and Senior Architect, you will be one of the most senior technologists at Toast, setting the technical vision and architectural direction for the pod while remaining hands-on in its most ambitious work. You'll define how AI-native engineering is done at Toast and spread those patterns across the organization.

A day in the life (Responsibilities)

• Develop and drive the architectural vision for AI Pod, establishing patterns for AI-native development that scale across Toast engineering
• Be an active participant and authoritative technical voice in cross-organizational strategy and architecture discussions
• Lead the pod's most complex, high-impact initiatives from design through production — staying hands-on throughout
• Evolve agentic development practices within the pod and champion their adoption across Toast
• Work closely with other Senior Principal Engineers and technical leaders across Toast to advance the overall architecture
• Mentor and develop technical leadership within the pod and across teams
• Partner with engineering leadership and product to translate strategic goals into executable technical plans

What you'll need to thrive (Requirements)

• 12+ years of experience designing and building highly scalable, mission-critical distributed software systems
• 10+ years of experience building and operating SaaS products, ideally AWS-hosted
• Demonstrated ability to drive architectural vision for a company, line of business, or highly scalable platform
• Deep experience building, deploying, and operating LLM-powered agents and AI-native systems
• 1+ years of hands-on experience using the latest AI coding agents with a strong desire to go all-in on agentic development practices
• Experience with MCP (Model Context Protocol), tool use patterns, or similar agentic integration frameworks
• Ability to move fluidly between whiteboard architecture and understanding the ebb and flow of code under load
• Ability and desire to communicate and influence at the executive level

What will make you stand out

• Experience managing or leading a small engineering team (2-4 engineers)
• Familiarity with A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns
• Experience with machine learning concepts, model evaluation, or ML infrastructure
• Familiarity with observability tooling for AI/LLM systems (e.g., Langfuse, DataDog)

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. Because our technology is purpose-built for restaurants, by restaurant people - and increasingly shaped by AI in both what we build and how we build - restaurants can trust that we'll deliver on their needs for today while investing in the experiences that will power their restaurant of the future.

AI Foundations is Toast's internal platform team for AI. We build the infrastructure, governance, and developer tooling that makes AI a reliable, scalable force multiplier across the entire product development lifecycle. Our products include the LLM Proxy, AI Key Management, Observability pipelines, the Claude Plugin Marketplace, and autonomous agents that participate directly in the software development lifecycle.

As a Senior Software Engineer on this team, you'll be hands-on building full-stack across the systems other Toast engineers depend on every day: the LLM proxy, agentic workflows, and the plugin marketplace that brings AI-native development practices to teams across the company.

A day in the life (Responsibilities)

• Design, build, and ship features across AI platform infrastructure, including the LLM proxy, AI key management, and observability pipelines
• Develop and extend autonomous agents that participate in the SDLC, such as the AI Review GitHub App and Developer Platform MCP integrations
• Build on and contribute to the internal plugin marketplace, developing plugins that encode Toast's architectural standards and quality practices into AI assistant behavior
• Implement MCP (Model Context Protocol) services and service templates that make it faster and easier for teams across Toast to build AI-powered workflows
• Contribute full-stack across backend services and frontend or developer-facing interfaces as needed
• Write high-quality, well-tested code and participate in design and code reviews
• Partner closely with product management to shape feature requirements, contribute to roadmap discussions, and translate product vision into reliable technical solutions

What you'll need to thrive (Requirements)

• 5+ years of experience designing and implementing scalable software systems, with a full-stack background spanning backend services and frontend or developer-facing interfaces
• Demonstrated experience building, deploying, or operating LLM-powered agents or AI-assisted developer tooling
• Familiarity with AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) and an interest in extending them through custom plugins, skills, or hooks
• Experience with MCP (Model Context Protocol), tool use patterns, or agentic frameworks
• Working knowledge of prompt engineering and how LLM behavior responds to context and instructions
• Strong foundation in modern backend (Java, Kotlin, or similar) and frontend (TypeScript, React, or similar) technologies
• Experience with distributed systems, REST APIs, and cloud-native infrastructure
• Strong customer empathy and a drive to make developers' lives better through reliable, well-designed platform solutions

What will make you stand out

• Familiarity with A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns
• Experience with machine learning concepts, model evaluation, or ML infrastructure
• Experience building developer experience platforms, internal tooling, or API gateways
• Familiarity with observability tooling for AI/LLM systems (e.g., Langfuse, DataDog)

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. Because our technology is purpose-built for restaurants, by restaurant people - and increasingly shaped by AI in both what we build and how we build - restaurants can trust that we'll deliver on their needs for today while investing in the experiences that will power their restaurant of the future.

AI Foundations is Toast's internal platform team for AI. We build the infrastructure, governance, and developer tooling that makes AI a reliable, scalable force multiplier across the entire product development lifecycle. Our products include the LLM Proxy, AI Key Management, Observability pipelines, the Claude Plugin Marketplace, and autonomous agents that participate directly in the software development lifecycle.

As a Principal Software Engineer, you'll set the technical direction for how we design, build, and evolve these systems, working full-stack across the agentic workflows that employees across Toast depend on every day. This role is for someone who can drive architecture, raise the engineering bar across the team, and stay hands-on building.

A day in the life (Responsibilities)

• Define and drive the technical architecture for AI Foundations platform, spanning backend services, frontend tooling, and third party integrations
• Lead full-stack design and delivery across the LLM proxy, AI key management, observability pipelines, and internal plugin marketplace
• Architect and deliver autonomous agents and MCP services that participate in the software development lifecycle
• Set and uphold engineering standards across the team: code quality, system design, testing, and operational practices
• Mentor and technically lead engineers across all levels through design reviews, code reviews, and pairing
• Partner with product and platform leadership to shape the AI Foundations roadmap and translate strategy into executable technical plans
• Collaborate closely with designers and product teams to translate UX concepts into polished, production-ready frontend experiences
• Serve as a key technical voice in cross-team discussions, representing AI Foundations in architecture decisions across Toast engineering

What you'll need to thrive (Requirements)

• 10+ years of experience designing and delivering scalable software systems, with a strong full-stack background spanning backend services and frontend or developer-facing interfaces
• Demonstrated experience leading technical architecture for a platform, product, or engineering team
• Proven ability to mentor engineers and raise the technical bar of a team through standards, reviews, and hands-on guidance
• Experience building, deploying, or operating LLM-powered agents or AI platform infrastructure
• Familiarity with AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) and experience extending them through custom plugins, skills, or hooks
• Experience with MCP, tool use patterns, or agentic frameworks
• Strong foundation in modern backend (Java, Kotlin, or similar) and frontend (TypeScript, React, or similar) technologies, with demonstrated experience building user-facing product interfaces
• Comfort with a rapidly changing technical landscape and a bias towards action over analysis paralysis

What will make you stand out

• Familiarity with A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns
• Experience with machine learning concepts, model evaluation, or ML infrastructure
• Experience building developer experience platforms or internal tooling
• Familiarity with observability tooling for AI/LLM systems (e.g., Langfuse, DataDog)

**This role requires a minimum of two days per week in our Dublin office**

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

About the Opportunity

Become part of the AI revolution at Contentful where we build real world AI solutions for thousands of active customers.
At Contentful you will help imagine, drive, and build ML solutions which produce immediate customer impact by helping them get more done than they ever imagined. We are looking for an excellent machine learning engineer who understands and has experience in this field and current Generative AI trends. If you're passionate about pushing the boundaries of what's achievable with ML products on a large scale content management system, we want you on our team.

What to expect? 

• Measure and ensure the high-quality output of ML workloads for our enterprise customers.
• Design, build, and measure production software in the Contentful Platform.
• Conduct focused research and testing using tools like Jupyter Notebook.
• Optimize generative AI products for accuracy, speed, and scalability.
• Integrate ML cloud solutions and adapt them to our large-scale use cases.
• Integrate prompt engineering and fine-tuning for customer use cases.
• Develop software that scales to meet the demands of high-load customer workloads.
• Lead design reviews with peers and stakeholders to decide amongst available technologies.
• Drive the ML education in the team and establish a high standard of quality & ownership

What you need to be successful?

• Proven experience as a technical leader in a product software development environment
• Proven Track Record in Model Development, including successfully developing and deploying LLM-based models for various NLP tasks in real-world applications with production workloads
• Proven ability to work backwards from customer needs to deliver ML-based features that meet those needs
• Ability to organize and prioritize competing workloads
• Familiarity with distributed systems and cloud services (e.g., AWS, Azure, GCP)
• Solid understanding of machine learning principles
• Experience with container frameworks such as Docker or Kubernetes
• Constructive Problem-Solving: As a natural problem-solver, you bring forward ideas that lead to practical solutions and contribute to product growth.
• Up-To-Date on The Latest Updates on LLM Development & Research: From RAGs to Open-Source models over Agent Architectures – you have an inherent drive to stay up-to-date on what is the latest and greatest

Preferred:

• Background includes machine learning and AI implementation
• You can translate a research paper into a PoC if the code does not exist

What’s in it for you?

• Join an ambitious tech company reshaping the way people build digital experiences
• Full-time employees receive Stock Options for the opportunity to share in the success of our company
• Fertility and family building benefits, including a lifetime reimbursable wallet to support your growing family.
• We value Work-Life balance and You Time! A generous amount of paid time off, including vacation days, sick days,  education days, compassion days for loss, and volunteer days
• Time off to care for and focus on your growing family 
• Use your personal annual education budget to improve your skills and grow in your career
• Enjoy a full range of virtual and in-person events, including workshops, guest speakers, and fun team activities, supporting learning and networking exchange beyond the usual work duties 
• An annual wellbeing stipend to care for your physical, financial, or emotional health
• A monthly communication phone/internet stipend and phone hardware upgrade reimbursement.
• New hire office equipment stipend for hybrid or distributed employees. Get the gear you need to work at your best.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

Because our technology is purpose-built for restaurants, by restaurant people - and increasingly shaped by AI in both what we build and how we build - restaurants can trust that we'll deliver on their needs for today while investing in the experiences that will power their restaurant of the future.

An AI pod is Toast's AI-native engineering team, a focused pod of specialists who tackle complex, high-impact problems across Toast's product and platform landscape using AI-first approaches.

As a Principal Software Engineer, you'll work full-stack across ambitious, cross-cutting initiatives, bringing deep technical expertise and architectural judgment to some of the hardest problems at Toast.

A day in the life (Responsibilities)

• Lead full-stack design and delivery of AI-native solutions that address complex, cross-functional problems across Toast
• Drive technical architecture for pod initiatives, from backend services and APIs to frontend and developer-facing interfaces
• Build and integrate LLM-powered agents, tools, and workflows that bring AI capabilities directly into product and platform contexts
• Set and uphold engineering standards within the pod: code quality, system design, testing, and operational practices
• Mentor engineers through design reviews, code reviews, and pairing
• Partner with stakeholders across Toast engineering to define problem scope, align on approach, and deliver outcomes
• Serve as a technical voice in cross-team architecture discussions, connecting pod work to broader Toast engineering patterns

What you'll need to thrive (Requirements)

• 10+ years of experience designing and delivering scalable software systems, with a strong full-stack background spanning backend services and frontend or developer-facing interfaces
• Demonstrated experience leading technical architecture for a platform, product, or engineering team
• Experience building, deploying, or operating LLM-powered agents or AI platform infrastructure
• Familiarity with AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) and experience extending them through custom plugins, skills, or hooks
• Experience with MCP (Model Context Protocol), tool use patterns, or similar agentic integration frameworks
• Strong prompt engineering skills and intuition for how LLM behavior responds to context and instructions
• Strong foundation in modern backend (Java, Kotlin, or similar) and frontend (TypeScript, React, or similar) technologies
• A bias towards action and comfort operating in ambiguous, rapidly evolving problem spaces

What will help you stand out

• Familiarity with A2A (Agent-to-Agent) protocols and multi-agent orchestration patterns
• Experience with machine learning concepts, model evaluation, or ML infrastructure
• Experience building developer experience platforms or internal tooling
• Familiarity with observability tooling for AI/LLM systems (e.g., Langfuse, DataDog)

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

#LI-Hybrid
#BI-Hybrid

What's the opportunity?

Fin is the #1 AI agent for customer service. It resolves over 1M customer conversations every week, and we're just getting started. We're on the cutting edge of defining how AI agents should work — inventing paradigms that didn't exist six months ago and discovering entirely new problem spaces every time we push the technology further.

This role sits at the heart of the Service Agent pillar — 12 teams and growing, building every capability that makes Fin work. You'll partner at the strategic pillar level: broad context across work streams, deep technical understanding of how Fin operates end-to-end, and the autonomy to decide where you need to go next. When we need a new work stream spun up from scratch — a brand new team, an ambiguous problem space, aggressive timelines — you're the person who makes that happen.

The competitive landscape is intense. Fast-moving AI-native startups are coming hard for this space, shipping aggressively and going after the same deals we are. We need someone who thrives under that pressure, who can maintain technical excellence while moving at startup speed within a company that has the resources and customer base to make that speed count.

What will you do?

• Partner at the strategic pillar level. You'll have broad context across work streams and use that to inform technical strategy and investment priorities. What should we care about? Where should we invest? Where do you need to step in? These are your decisions.
• Spin up 0-to-1 work streams. Bring together engineers who've never worked as a team, disambiguate the problem space, build momentum under aggressive timelines, set high expectations, and drive execution.
• Execute on the most ambiguous, highest-stakes problems. You're hands-on. You write code, you ship features, you're deep in the weeds. We've seen too many Principal Engineers who are brilliant but too far from execution to be effective here.
• Lead experimental work at the AI frontier. Run your own A/B tests, do prompt engineering, build evals, and calibrate accuracy, cost, and latency for LLM-powered features. Much of what we're building are new paradigms for customer-facing software.
• Shape long-term technical strategy through execution. Your hands-on work informs the architecture. You're simultaneously building and thinking about what needs to change about how we build products — data models, system design, the shift from GUI-first to agent-first interfaces.
• Work across the full stack in an AI-first development environment. Every engineer has Claude Code with unlimited tokens and a sophisticated ecosystem of MCP servers, skills, and shared context. You'll push the boundaries of what's possible with AI-assisted development and help shape how the entire engineering org works.
• Raise the bar for the people around you. Give direct, actionable feedback that changes outcomes. We don't need someone who mentors juniors on the basics — we need someone who can accelerate and grow the senior and staff engineers around them.

What are we looking for?

The core requirement: engineering depth and product thinking. We need someone who combines deep engineering ability with strong product and design instincts — not one or the other. We don't want engineers who are technically brilliant but disconnected from the product. And we don't want product-minded engineers who have great instincts but can't execute at the level this work demands. You need to be able to both build complex systems AND identify the right problems to solve. This is the single most important dimension we evaluate on.

Beyond that:

• Operated at real scale and has builder energy. You've likely spent time at companies operating at significant scale — the kind of environment where you've seen what large-scale distributed systems actually look like in production. But you also have startup DNA: urgency, resourcefulness, a bias toward building over discussing.
• AI fluency. You're actively experimenting with AI-assisted development, pushing the boundaries of what's possible, and forming opinions about how it changes the way teams build software. This matters more than any specific stack experience.
• Deep technical depth with breadth. You can go deep into the weeds on at least one technical domain, but you also navigate complex multi-team systems with ease. In conversation, you move fluidly from business context to technical strategy to implementation details.
• Communication as a superpower. You can explain to leadership why a technical investment matters, align multiple teams around a complex project, and then walk an engineer through the gnarly implementation details. This role lives and dies on your ability to communicate across every level.
• Extreme autonomy. You partner with the Engineering Director on where you think the pillar needs to go next. You identify problems, form convictions, and act on them. If you're the kind of engineer who waits to be told what to work on, this isn't the right role.
• Critical thinking about the business. You understand what Intercom is optimizing for and translate that into technical decisions. Which work streams need investment? Where are we falling behind? What should we defund to go harder somewhere else? You think at this level naturally.
• At least ~10+ years of experience, with significant time as a technical leader driving complex projects across multiple teams and stakeholders.
• Stack agnostic. We use Ruby on Rails, React (migrating from Ember), and AWS. But AI tools increasingly abstract the stack — what matters is your ability to learn, build, and operate effectively with tools like Claude Code. If you're great with a high-level programming language and fluent with AI-assisted development, you'll be productive fast.

Why Intercom, why now?

Fin is #1 — and the race is heating up. We built the leading AI agent for customer service, but fast-moving AI-native startups are coming for this space. This is one of the most consequential competitive moments in tech history, and the engineering decisions made in the next 12 months will determine who wins.

A rare pivot that actually worked. Intercom has been building for 15 years — deep product, massive customer base, strong revenue. But unlike most legacy SaaS companies, we made a successful pivot to AI. While most of SaaS trends down, Intercom trends up. We recently raised $250M to go even further even faster..

AI-first engineering culture. Every engineer gets Claude Code with unlimited Opus 4.6 tokens and 1M context. We're investing heavily in the platform capabilities — MCP servers, skills, shared tooling — that make AI-assisted development genuinely transformative. You'll be at the sharp edge of figuring out what this means for how teams work.

The sweet spot. Big enough to have real resources, customers, and revenue. Small enough that you have genuine agency. Bold enough to make decisions that most companies of this size wouldn't. You come to Intercom if you want the impact of a startup with the backing of a company that's been winning for 15 years.

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work!
• Pension scheme & match up to 4%
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Flexible paid time off policy
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too 
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

#LI-Hybrid

Toast is driven by building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love.

SEAR at Toast focuses on two things: actionable intelligence collection, analysis, and delivery to engineers, and hunting the adversaries who would damage and defraud Toast, its customers, and its partners. We bake security into every layer of our products, from the first sprinkle of an idea to the final serving of a fully-baked solution. Our team is tasked with searching out and degrading the operations of persistent, motivated attackers.

Like master chefs, we blend cutting-edge technology with strategic thinking, implementing bespoke and industry-standard solutions for intelligence processing at scale. By joining SEAR, you'll be part of the kitchen crew that keeps our customers' trust from going stale. You'll tackle complex challenges that have real-world impact, helping to serve up a safer, more secure digital experience for businesses that count on Toast every day. It's not just about pattern-matching  – it's about crafting a recipe for digital trust that keeps our customers coming back for more.

A day in the life (Responsibilities)Select, implement, design, and build services and tools to manage and deliver security intelligence across Toast platforms.

• Identify, triage, and provide remediation guidance for application vulnerabilities, with a specific focus on anti-abuse activities.
• Improve developer tooling and adoption to build a more robust SSDLC which integrates security and anti-abuse features.
• Practice a #OneTeam attitude to help other Toast teams make informed, security-conscious decisions when building new public-facing software.
• Assist incident response teams with application security expertise and tools, especially related to abuse and fraud.
• Build threat models on Toast applications and use cases.
• Guide in the design and maintenance of robust and resilient network and application architecture.
• Collaborate to improve information gathering and sharing across all Toast products.
• Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.

What you'll need to thrive (Requirements)

• 5+ years experience working in software development
• Minimum 5+ years of experience in application security and security engineering.
• Experience building and maintaining scaled Java web services in production.
• Experience developing script applications in Python for scheduling and backend data handling.
• Experience leveraging LLM AI features for software development and/or security operations.
• Strong understanding of cloud application architecture.
• Successful history of being a subject matter expert to guide colleagues toward better security outcomes, especially related to abuse, fraud and legal concerns.
• Previous security experience working with fintech applications and associated requirements.
• Strong understanding of privacy, security, and cryptography patterns and when to apply them, especially when handling customer information (such as PKIs, access management, data tokenization, and anonymization).

**This role requires two days per week in our Dublin office**

Nonessential Skills/Nice to Haves

• Offensive security training and certifications (e.g. OSCP, OSWE, OSEP)
• Edge Security solution like WAF, API Security
• Adversary Emulation proficiency (red/purple teaming)
• Cloud and container security technologies
• SSDLC tooling (e.g., SAST/DAST/SCA)
• Scaled data handling in RDBMS, streaming, and columnar stores
• Metrics and charting software proficiency
• Mobile apps/threats (iOS, Android), and their particular abuse vectors
• Knowledge in security of operating systems, networking and protocols
• Securing financial technologies and associated requirements

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

POS-31662

Role Summary

HubSpot is building the next generation of AI experiences across our go-to-market products. We’re hiring a Senior Machine Learning Engineer II to join the Flywheel Context (Contacts) team, where you’ll build the context platform layer that powers accurate, high-performing AI assistants and agents.

This is a backend-leaning ML engineering role focused on shipping production software—designing systems that help other engineering teams retrieve relevant facts, manage long/complex context, and evaluate quality at scale. If you love building durable platforms (not just prototypes), this role is for you.

What You’ll Do

• Design, build, and operate backend services that power context retrieval and enrichment for AI assistants and agents.

• Build platform capabilities for storing, searching, and retrieving “insights” and relevant facts across HubSpot’s GTM data.

• Develop systems to manage and compress context when it gets large (e.g., long contact histories, high-volume CRM data).

• Create tooling that allows other engineering teams to ship assistants/agents faster, with consistent APIs and reusable primitives.

• Build and maintain evaluation and measurement approaches (offline evals, golden datasets, automated metrics, human review loops) to ensure context quality and answer accuracy.

• Collaborate closely with sister platform teams and downstream product engineering teams (your “customers”) to integrate platform capabilities into real experiences.

• Own end-to-end delivery: architecture, implementation, observability, performance, reliability, and iteration.

Required Qualifications

• Strong track record shipping production backend systems as a senior engineer (ownership from design to delivery).

• Professional Java experience building maintainable, testable services in production (this is core to the role).

• Experience implementing ML workflows in production (e.g., retrieval/ranking pipelines, feature/data pipelines, model/embedding services, evaluation frameworks).

• Comfort working with data tooling and data-intensive systems (large datasets, pipelines, and service integrations).

• Experience operating software at meaningful scale (e.g., high throughput, significant data volume, performance and reliability constraints).

• Strong engineering fundamentals: system design, code quality, debugging, observability, and operational excellence.

Nice-to-Have Qualifications

• Experience with search/retrieval/relevance/ranking systems (highly aligned to context work).

• Experience with RAG-style systems, embeddings, vector search, or hybrid retrieval strategies.

• Familiarity with LLM evaluation patterns (golden sets, automated metrics, human review), hallucination mitigation, and quality measurement.

• Experience with distributed systems, event-driven architectures, or stream processing.

• Cloud/platform experience (e.g., Kubernetes, AWS/GCP) and running services in production.

• Some Python experience (useful, but not the primary language).

POS-31662

Role Summary

HubSpot is building the next generation of AI experiences across our go-to-market products. We’re hiring a Senior Machine Learning Engineer II to join the Flywheel Context (Contacts) team, where you’ll build the context platform layer that powers accurate, high-performing AI assistants and agents.

This is a backend-leaning ML engineering role focused on shipping production software—designing systems that help other engineering teams retrieve relevant facts, manage long/complex context, and evaluate quality at scale. If you love building durable platforms (not just prototypes), this role is for you.

What You’ll Do

• Design, build, and operate backend services that power context retrieval and enrichment for AI assistants and agents.

• Build platform capabilities for storing, searching, and retrieving “insights” and relevant facts across HubSpot’s GTM data.

• Develop systems to manage and compress context when it gets large (e.g., long contact histories, high-volume CRM data).

• Create tooling that allows other engineering teams to ship assistants/agents faster, with consistent APIs and reusable primitives.

• Build and maintain evaluation and measurement approaches (offline evals, golden datasets, automated metrics, human review loops) to ensure context quality and answer accuracy.

• Collaborate closely with sister platform teams and downstream product engineering teams (your “customers”) to integrate platform capabilities into real experiences.

• Own end-to-end delivery: architecture, implementation, observability, performance, reliability, and iteration.

Required Qualifications

• Strong track record shipping production backend systems as a senior engineer (ownership from design to delivery).

• Professional Java experience building maintainable, testable services in production (this is core to the role).

• Experience implementing ML workflows in production (e.g., retrieval/ranking pipelines, feature/data pipelines, model/embedding services, evaluation frameworks).

• Comfort working with data tooling and data-intensive systems (large datasets, pipelines, and service integrations).

• Experience operating software at meaningful scale (e.g., high throughput, significant data volume, performance and reliability constraints).

• Strong engineering fundamentals: system design, code quality, debugging, observability, and operational excellence.

Nice-to-Have Qualifications

• Experience with search/retrieval/relevance/ranking systems (highly aligned to context work).

• Experience with RAG-style systems, embeddings, vector search, or hybrid retrieval strategies.

• Familiarity with LLM evaluation patterns (golden sets, automated metrics, human review), hallucination mitigation, and quality measurement.

• Experience with distributed systems, event-driven architectures, or stream processing.

• Cloud/platform experience (e.g., Kubernetes, AWS/GCP) and running services in production.

• Some Python experience (useful, but not the primary language).

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-BRAZIL
#LI-INDIA

About the Role

The AI Integrations team builds the tools that bring AI into Telnyx's core products — think voice agents, intelligent messaging, and real-time AI features that improve our customer experience. We need a strong fullstack engineer to help us move faster.

This is a mid-level IC role. You'll own features end-to-end: building React interfaces, writing Node.js services, wiring up databases, and shipping things that work. AI experience isn't required — you'll pick that up here. What matters is that you can build solid fullstack products and want to learn.

What You'll Work On

• Fullstack features across our AI product suite — frontend to database
• Integrating LLMs and AI capabilities into real world applications (with support from the team)
• Building interfaces that make complex AI tools feel simple
• Shipping iteratively — we value working software over perfect plans

Tech Stack

Core (you should know this well):
React 18+, TypeScript, Tailwind CSS, Node.js, PostgreSQL, Prisma, TanStack Query, Docker, Kubernetes

Bonus (nice to have, happy to teach):
Next.js 15+, LLM integration, LangChain/LangGraph RAG/Embeddings, Vector Databases, MCP, WebSockets, WebRTC, Radix UI, Shadcn UI, Framer Motion

What We're Looking For

You're genuinely comfortable across the stack — not a frontend dev who can "do backend" or vice versa. You've built and shipped real features. You take ownership, communicate clearly, and don't need hand-holding.

AI interest is a plus, but we'd rather have a strong fullstack engineer who's curious than an "AI enthusiast" who can't ship.

We move fast on modern web tooling (React/Next, TypeScript, Tailwind). Expect frequent upgrades.

Values

• Ownership — you see things through
• Collaboration — you work well with others and communicate proactively
• Adaptability — comfortable with ambiguity and changing requirements
• Growth mindset — eager to learn, takes feedback well


#LI-KG1 #LI-VIETNAM
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems
  
  
  

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-VIETNAM
#LI-BRAZIL
#LI-INDIA

The Opportunity

Our AI teams build highly available, low-latency microservices using modern, cloud-native technologies. This role sits within our broader AI organization and focuses on developing the platforms, services, and tooling that support AI-powered products — from inference and embeddings to APIs, data flows, and observability.

You’ll work in a fast-paced environment where reliability, performance, and usability matter just as much as innovation. While some squads work closer to inference pipelines and LLM tooling, others focus on platform foundations, troubleshooting and reliability, data systems, or observability — all critical to delivering AI in production at scale.

What You’ll Do

• Build, test, and deploy scalable microservices for AI-powered systems, including text and voice processing
  
  
• Design developer-friendly APIs, agents and internal tools that make AI easy to use across the Telnyx product ecosystem
  
  
• Contribute to AI inference, embeddings, and platform services that support real-time and low-latency workloads
  
  
• Optimize performance, reliability, and observability in production environments
  
  
• Collaborate closely with Product, Infrastructure, and ML teams to bring features from ideation to production
  
  
• Troubleshoot, debug, and support critical systems running in production
  
  
• Participate in code reviews and uphold high engineering and operational standards
  
  

Depending on the squad, your focus may lean more toward inference pipelines, platform foundations, observability, data systems, or production troubleshooting.

Who We’re Looking For

• Someone who has built something with cutting-edge AI tooling (something that has only become possible in the last 6 months, rolling from application date). It doesn't really matter what it is; we just want to know you are building and staying up to date.
  
  
• Strong understanding of async programming, concurrency, and parallel execution
  
  
• Experience with microservice-based systems
  
  
• Excellent communication skills and the ability to work effectively across teams
  
  

Nice to Haves

• Familiarity with LLM frameworks and tools (e.g. PyTorch, Transformers, DeepSpeed, vLLM)
  
  
• Experience with distributed computing frameworks such as Ray (Ray Core, Serve, Train)
  
  
• Exposure to cloud-native stacks (Docker, Kubernetes, Prometheus, Jaeger)
  
  
• Knowledge of LLM optimization techniques (e.g. quantization, speculative decoding)
  
  
• Background in Retrieval Augmented Generation (RAG) systems

How We Hire Across Our AI Teams

Our AI Chapter is made up of multiple squads, including:

• AI application & software engineering
  
  
• Platform & infrastructure foundations
  
  
• Troubleshooting, reliability, and production support
  
  
• Data, storage, and embeddings
  
  
• Observability and performance monitoring
  
  

We hire into a shared AI engineering pipeline. During interviews, we focus on core engineering skills, system design, and problem-solving ability. Final squad alignment happens later, based on:

• Your strengths and interests
  
  
• Team needs at the time
  
  
• Where you’ll have the biggest long-term impact
  
  

This means the exact focus of the role may vary by squad, but the core expectation remains the same: building reliable, scalable AI systems in production.

Why You’ll Love It Here

At Telnyx, we’re not just building products — we’re building the future of intelligent communication. Our TelnyxAI platform empowers developers and businesses to:

• Store and serve vector embeddings at scale
  
  
• Use open-source models with our hosted inference infrastructure
  
  
• Deploy multimodal AI features across text, voice, and storage seamlessly
  
  

You’ll have the autonomy to take ownership of meaningful projects, contribute ideas that shape the product roadmap, and collaborate with talented people who care deeply about impact, quality, and long-term thinking.

#LI-KG1
#LI-BRAZIL

Your Role:

Tenable's cloud-based exposure management platform helps organizations see, understand, and reduce cyber risk across their entire attack surface. Our SRE teams keep that platform reliable, scalable, and secure — and we're building the next generation of tooling to do it smarter.
 

This role sits within our SRE Infrastructure Management organization on a focused team dedicated to reducing operational toil through AI-powered automation. You'll build intelligent systems that replace manual workflows — from incident diagnostics to infrastructure provisioning to upgrade automation — using LLMs, agentic architectures, and deep SRE domain knowledge.
 

This isn't an operations role with some AI on the side. You'll spend most of your time writing production code: designing and building agentic workflows, integrating across observability and infrastructure platforms, and measuring the impact of what you ship against real toil data.

Your Opportunity:

• Design and build AI-powered agentic workflows that automate complex SRE operations — incident investigation, infrastructure provisioning, deployment reliability, and more.
• Improve the accuracy, reliability, and observability of agent pipelines through evaluation frameworks, prompt engineering, retrieval strategies, and structured output validation.
• Build developer tools and internal platforms — CLI tools, IDE plugins, and workflow automation — that engineers across the organization use daily.
• Build tooling that connects across the SRE tech stack — Kubernetes, Terraform, Helm, CI/CD pipelines, observability platforms, and cloud infrastructure APIs.
• Work on a focused team where everyone writes code, owns what they ship, and drives prioritization from measured toil data.
• Participate in SRE on-call rotation — we use on-call as a direct input into what we build, not just a firefighting duty.
• Collaborate with SRE teams across the organization to identify automation opportunities and deliver tooling that gives engineers hours back.

What Success Looks Like:

• Within your first few months, you've shipped an agentic workflow that automates a real SRE toil category — and engineers are using it.
• Within 6 months, you're independently designing and building AI-powered pipelines, contributing to the team's evaluation and accuracy practices, and your work is driving measurable toil reduction.
• Within a year, you've become a go-to contributor on the team — shaping the roadmap, mentoring others on AI + SRE patterns, and building systems that scale the team's impact across engineering.

What You'll Need:

And Ideally:

We’re a new team building AI-assisted tools to make Datadog developers more effective, by autonomously generating tests, fixing bugs, and improving performance.

We’re looking for a product-minded generalist to help us quickly define and ship products that make all Datadog customers 10x developers.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Work closely with our users, designers, PMs, and data scientists on cutting edge and delightful LLM-based agents.
• Work on prompts, evals, and agent tools for working with code, and on the engineering to productionize these systems and integrate them with Datadog products.
• Prototype and see what works well in the real world, iterate closely with our users, and then ship.
• Follow the latest research on LLM prompting, agents, and code editing.
• Work with other Datadog teams to expand capabilities, for example integrating with the Datadog IDE extensions to deliver a CoPilot-like assistant, or Dynamic Instrumentation to collect more runtime data from production and pre-production environments.

Who You Are:

• You’re a backend engineer with at least 5 years of professional experience (Go, Java, Python) who values code simplicity and performance. You’ve shipped software that uses LLMs to production.
• You are passionate about Generative AI and its ability to help software engineers be more productive.
• You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields.
• You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution.
• You want to work in a fast, high-growth startup environment that respects its engineers and customers.
• You have experience writing developer tools (such as IDEs, static code analysis tools, or compilers).
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.
• Bonus: you’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

We’re building a new team building to bring the power of Datadog to developers within AI-focused Developer workflows such as AI agents like Cursor, Claude code, GitHub Copilot and Datadog’s native AI agents.

We’re looking for product-minded engineers who are excited about developer tools, SDLC improvements and AI in general to help us quickly define and ship features and tools that make all Datadog customers 10x developers.

At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them.

What You’ll Do: 

• Work on agents, prompts, evals, MCPs and direct agent tools from ideation to productization, for allowing Datadog to help companies and developers to maximize the benefit of their integration with Datadog
• Help conceptualize and build innovative User flows and experiences within Datadog’s user space and different extensions 
• Prototype and see what works well in the real world, iterate closely with our users, and then ship
• Follow the latest research on LLM prompt optimization, agents, and code editing.
• Support other Datadog product teams to help them build and productionize their developer-focused AI Workflows. For example, you’ll work with the Live Debugger team to create an agentic production debugging experience, delivering on the vision of  “one click to reproduce” for production errors.

Who You Are: 

• You’re a backend engineer with at least 5 years of professional experience who values code simplicity and performance. You’ve shipped software that uses LLMs to production
• You are passionate about Generative AI and its ability to help software engineers be more productive
• You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields
• You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution
• You want to work in a fast, high-growth startup environment that respects its engineers and customers
• You have experience writing classic or AI oriented developer tools(such as IDEs, static code analysis, SDLC tools etc).
• You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

At Datadog, we leverage AI across our observability platform to improve monitoring, speed up incident resolution, and ensure data reliability for cloud applications.

Datadog’s Deployment Gates team builds customer-facing systems that decide whether software should ship to production. Deployment Gates sit directly in customers’ CI/CD pipelines and use observability data to answer one of the hardest questions in software delivery:
Given everything we know right now, is this deployment safe to proceed?

In this role, you will:

• Work on an analysis service that prevents incidents by detecting faulty software changes in production before they reach clients

• Lead the design of progressive deployment automation, starting with zero-setup, conservative AI rules and evolving toward adaptive gates that learn from incidents and organizational patterns
• Design the foundation for autonomous remediation, connecting what changed in code to what broke in production, from blocking and rolling back unsafe deployments to proposing fixes and enforcing policies

This is a highly product‑minded engineering role: you’ll work from problem discovery and UX all the way to reliable, scalable production systems.

At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them.

What you’ll do:

• Build AI-driven deployment gates: Design and ship decision systems that evaluate customer deployments using CI/CD context and Datadog telemetry, producing safe, explainable allow/block outcomes
• Own evals and rollout: Define precision, recall, and trust metrics; build offline and online evals; validate changes in shadow mode; and safely promote improvements to enforcement
• Design for robustness and safety: Implement conservative defaults, guardrails, fallbacks, and human-in-the-loop paths so gates behave predictably under noisy or incomplete data
• Partner closely with Product: Work hand-in-hand with the Product Manager to translate customer problems, adoption signals, and roadmap goals into concrete technical decisions and iterations
• Integrate across the Datadog platform: Partner with internal AI teams building the Faulty Deployment Detection pipeline, as well as teams working on LLMs and AI agents
• Own production systems: Build and operate reliable backend services that run in the critical path of customer deployments, and be on-call for those services

Who you are:

• A Product‑minded engineer who ships AI to production
• You have 5+ years experience with backend systems and microservices performance: tracing, latency breakdowns, concurrency, and resiliency patterns
• You are proficient in a modern programming language; strong API/service design; production ops (monitoring, alerting, on‑call rotation)
• You have proven experience delivering software based on LLM/agent features to production
• You are comfortable owning user journeys, iterating from prototype → alpha → GA, and measuring impact with clear product metrics
• An End-to-end AI implementation owner: You understands the end-to-end LLM product lifecycle
• Fluent with offline/online evals for AI systems
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output

Bonus Point:

• Experience with Continuous Delivery tools (i.e. ArgoCD / Argo Rollouts, Spinnaker, Octopus Deploy)
• Exposure to planning/agent frameworks, tool‑use orchestration, RAG, and retrieval/indexing for observability data
• You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. 

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

We’re a new team building AI-assisted tools to make Datadog developers more effective, by autonomously generating tests, fixing bugs, and improving performance.

We’re looking for a product-minded generalist to help us quickly define and ship products that make all Datadog customers 10x developers.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Work closely with our users, designers, PMs, and data scientists on cutting edge and delightful LLM-based agents.
• Work on prompts, evals, and agent tools for working with code, and on the engineering to productionize these systems and integrate them with Datadog products.
• Prototype and see what works well in the real world, iterate closely with our users, and then ship.
• Follow the latest research on LLM prompting, agents, and code editing.
• Work with other Datadog teams to expand capabilities, for example integrating with the Datadog IDE extensions to deliver a CoPilot-like assistant, or Dynamic Instrumentation to collect more runtime data from production and pre-production environments.

Who You Are:

• You’re a backend engineer with at least 5 years of professional experience (Go, Java, Python) who values code simplicity and performance. You’ve shipped software that uses LLMs to production.
• You are passionate about Generative AI and its ability to help software engineers be more productive.
• You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields.
• You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution.
• You want to work in a fast, high-growth startup environment that respects its engineers and customers.
• You have experience writing developer tools (such as IDEs, static code analysis tools, or compilers).
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.
• Bonus: you’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

We’re a new team building AI-assisted tools to make Datadog developers more effective, by autonomously generating tests, fixing bugs, and improving performance.

We’re looking for a product-minded generalist to help us quickly define and ship products that make all Datadog customers 10x developers.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Work closely with our users, designers, PMs, and data scientists on cutting edge and delightful LLM-based agents.
• Work on prompts, evals, and agent tools for working with code, and on the engineering to productionize these systems and integrate them with Datadog products.
• Prototype and see what works well in the real world, iterate closely with our users, and then ship.
• Follow the latest research on LLM prompting, agents, and code editing.
• Work with other Datadog teams to expand capabilities, for example integrating with the Datadog IDE extensions to deliver a CoPilot-like assistant, or Dynamic Instrumentation to collect more runtime data from production and pre-production environments.

Who You Are:

• You’re a backend engineer with at least 5 years of professional experience (Go, Java, Python) who values code simplicity and performance. You’ve shipped software that uses LLMs to production.
• You are passionate about Generative AI and its ability to help software engineers be more productive.
• You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields.
• You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution.
• You want to work in a fast, high-growth startup environment that respects its engineers and customers.
• You have experience writing developer tools (such as IDEs, static code analysis tools, or compilers).
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.
• Bonus: you’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

We’re a new team building AI-assisted tools to make Datadog developers more effective, by autonomously generating tests, fixing bugs, and improving performance.

We’re looking for a product-minded generalist to help us quickly define and ship products that make all Datadog customers 10x developers.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Work closely with our users, designers, PMs, and data scientists on cutting edge and delightful LLM-based agents.
• Work on prompts, evals, and agent tools for working with code, and on the engineering to productionize these systems and integrate them with Datadog products.
• Prototype and see what works well in the real world, iterate closely with our users, and then ship.
• Follow the latest research on LLM prompting, agents, and code editing.
• Work with other Datadog teams to expand capabilities, for example integrating with the Datadog IDE extensions to deliver a CoPilot-like assistant, or Dynamic Instrumentation to collect more runtime data from production and pre-production environments.

Who You Are:

• You’re a backend engineer with at least 5 years of professional experience (Go, Java, Python) who values code simplicity and performance. You’ve shipped software that uses LLMs to production.
• You are passionate about Generative AI and its ability to help software engineers be more productive.
• You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields.
• You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution.
• You want to work in a fast, high-growth startup environment that respects its engineers and customers.
• You have experience writing developer tools (such as IDEs, static code analysis tools, or compilers).
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output.
• Bonus: you’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products.

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global mental health benefits for employees and dependents age 6+
• Competitive global benefits

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#LI-Hybrid

About the role:

Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry's largest compute-agnostic inference deployments.  We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.

The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems
• Implementing and deploying machine learning systems at scale
• Load balancing, request routing, or traffic management systems
• LLM inference optimization, batching, and caching strategies
• Kubernetes and cloud infrastructure (AWS, GCP)
• Python or Rust

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Want to learn more about machine learning systems and infrastructure
• Thrive in environments where technical excellence directly drives both business results and research breakthroughs
• Care about the societal impacts of your work

Representative projects across the org:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators
• Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads
• Building production-grade deployment pipelines for releasing new models to millions of users
• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage
• Contributing to new inference features (e.g., structured sampling, prompt caching)
• Supporting inference for new model architectures
• Analyzing observability data to tune performance based on real-world production workloads
• Managing multi-region deployments and geographic routing for global customers

Deadline to apply: None. Applications will be reviewed on a rolling basis.

Team Overview:

The Amplify Engineering team builds internal tools, platforms, and AI-powered automation that help Klaviyos work more efficiently and at scale as the company grows. We partner across the organisation, including Sales, Marketing, Engineering & Product, Customer Service & Success, and Finance, to solve real operational problems with practical, well-designed software.

Our work spans internal applications, 3rd-party integrations, interactive dashboards, CMS integrations, and robust AI systems and agents that automate repetitive tasks, surface the right information at the right time, and help teams make better decisions. We operate in tight feedback loops with our internal customers, shipping quickly, learning fast, and seeing the impact of our work immediately in how deals move faster, tickets resolve sooner, and launches go more smoothly.

This role is for software engineers who enjoy turning real-world problems into reliable, scalable solutions. The work is highly visible and has a direct impact on how thousands of Klaviyos get their work done every day.

How You’ll Make an Impact:

The Amplify team builds the internal platforms, automation, and AI-driven systems that power how Klaviyos work every day. As an AI Software Engineer II on Amplify, you will:

• Design, build, and maintain internal products and platforms, including internal web applications, APIs, data and event pipelines, and AI-powered workflows used daily by Sales, Marketing, Customer Service & Success, R&D, and Finance teams. You will own features and small-to-medium projects end to end, from design through deployment and iteration.
• Deliver project work with increasing autonomy and scope. You will break down problems into actionable pieces, refine requirements with your team, and collaborate to ship high-quality solutions that meet reliability and performance expectations.
• Work closely with internal customers to understand their workflows and constraints, and translate operational challenges into pragmatic, maintainable software and AI-enabled tools that improve productivity and reliability.
• Contribute to the technical and architectural evolution of Amplify’s systems by participating in design discussions, proposing improvements, and helping ensure our platforms remain scalable, observable, and reliable as usage grows.
• Take responsibility for the quality and operational health of the code you ship, including testing, deployment, monitoring, debugging issues in development and production, and participating in on-call rotations for the systems your team owns.
• Use modern AI tools as part of your day-to-day engineering workflow, including for coding, testing, documentation, and experimentation, and share learnings to help the team continuously improve how it works with AI.
• Help improve the developer experience and engineering practices on Amplify by contributing to tooling, CI/CD, automation, tests, and shared utilities that support consistent, high-quality delivery.
• Collaborate closely with Product, Design, Data, and operational partners, contributing to discovery, evaluating trade-offs, and ensuring the work you deliver is tied to clear success criteria and business outcomes.

Who You Are:

• You care about building software for the long term, balancing technical quality, delivery pace, and business impact.
• You have strong hands-on experience building and operating full-stack or backend-focused software systems, including SaaS products, platforms, or internal tools.
• You can take a well-defined feature or small-to-medium project from design through implementation, testing, rollout, and ongoing maintenance, seeking input when needed and moving work forward independently.
• You are comfortable questioning and refining requirements, working with product managers and stakeholders to clarify scope, identify edge cases, and propose pragmatic solutions.
• You have experience in at least one area of modern web engineering such as backend services, data pipelines, internal tools, integrations, or AI-enabled workflows, and understand how to build secure, scalable, and maintainable systems.
• You are comfortable using AI-powered tools to support your development workflow and are interested in continuing to grow your AI skills over time.
• You care about operational excellence and are comfortable debugging issues, improving tests, adding logging and metrics, and working within reliability goals.
• You are a clear communicator and collaborative teammate, contributing thoughtfully to technical discussions, writing clear tickets or lightweight design docs, and keeping others informed on progress and risks.
• You enjoy working in small, autonomous, agile teams, shipping iteratively, and partnering closely with product managers and other engineers to deliver meaningful outcomes.
• You are motivated to continuously improve how things work, whether that is tooling, workflows, architecture, or team practices.

Nice to Have:

• Experience building internal tools, platforms, or automations for developers and/or non-technical stakeholders, with a track record of improving reliability, productivity, or operational efficiency.
• Hands-on experience in full-stack environments, including diagnosing and improving application performance, database queries, and complex, multi-service workflows.
• Practical experience building or integrating AI-powered systems and agents; e.g., workflows using LLM APIs/SDKs, agentic or automated workflows, RAG pipelines, or evaluation/monitoring frameworks.
• Familiarity with cloud infrastructure (AWS preferred), infrastructure-as-code (Terraform), and containerized environments (Kubernetes), including how to design services that run reliably at scale in those environments.

Technologies We Use:

Klaviyo’s platform is primarily built with Python and React and runs on AWS. Engineers join us from a wide range of technical backgrounds and are supported in learning our stack. Core technologies include:

• Python / Django / FastAPI
• Typescript / React
• MySQL / Redis / Memcached
• RabbitMQ / Celery / Apache Kafka / Apache Pulsar
• AWS / Terraform / Kubernetes

Location & Work Model:

This role is based in Dublin, Ireland and follows a hybrid working model. Klaviyo supports work authorization and relocation for this position.

At Klaviyo, we enjoy tackling meaningful engineering challenges and value people who take ownership, learn continuously, and collaborate openly. We are committed to building inclusive teams and encourage applications from candidates of all backgrounds.

Klaviyo is growing fast and we have openings for all skill levels across all of our teams. Learn more about our engineering culture at https://klaviyo.tech

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Team Overview:

The Amplify Engineering team builds internal tools, platforms, and AI-powered automation that help Klaviyos work more efficiently and at scale as the company grows. We partner across the organisation, including Sales, Marketing, Engineering & Product, Customer Service & Success, and Finance, to solve real operational problems with practical, well-designed software.

Our work spans internal applications, 3rd-party integrations, interactive dashboards, CMS integrations, and robust AI systems and agents that automate repetitive tasks, surface the right information at the right time, and help teams make better decisions. We operate in tight feedback loops with our internal customers, shipping quickly, learning fast, and seeing the impact of our work immediately in how deals move faster, tickets resolve sooner, and launches go more smoothly.

This role is for lead engineers who enjoy turning real-world problems into reliable, scalable solutions. The work is highly visible and has a direct impact on how thousands of Klaviyos get their work done every day.

How You’ll Make an Impact:

The Amplify team builds the internal platforms, automation, and AI-driven systems that power how Klaviyos work every day. As a Lead AI Software Engineer on Amplify, you will:

• Provide technical oversight and direction for a portfolio of Amplify systems, including internal web applications, APIs, data and event pipelines, and AI-powered workflows used daily by Sales, Marketing, Customer Service & Success, R&D, and Finance teams. You will help ensure they are reliable, scalable, secure, and straightforward to evolve.
• Formulate and drive a forward-looking technical vision for key Amplify domains in partnership with your Engineering Manager, Product, Data, and cross-functional stakeholders, aligning that vision with our AI-first strategy and the business outcomes we are accountable for.
• Lead the end to end lifecycle of complex, multi component projects and platforms that span multiple internal customers and engineering teams, from discovery and technical design through implementation, rollout, and long term ownership.
• Act as the primary technical interface between your areas of ownership in Amplify and the rest of engineering, helping clarify requirements, reduce technical ambiguity and risk, and ensure the team delivers roadmap and OKR commitments on time with the right quality bar.
• Take responsibility for the technical and architectural evolution of multiple significant domains within Amplify, anticipating scalability, reliability, security, and observability needs as usage grows and new use cases emerge.
• Define, refine, and uphold operational standards for the systems you and your team own, including SLOs, monitoring and alerting, incident response practices, and post incident learning that continually improves our operational excellence.
• Create, teach, and improve shared engineering processes, patterns, and internal platforms that make it easier for Amplify and other teams across Klaviyo to build high quality internal tools and AI-powered systems consistently.
• Use modern AI tools as part of your day-to-day engineering workflow and help the team adopt AI effectively, from design and coding through testing, documentation, and evaluation. You will help establish sensible guardrails, evaluation practices, and patterns for AI-first development.
• Work through influence and delegation to get work done, setting clear technical direction, breaking down projects into actionable workstreams, and enabling other engineers to deliver successfully.
• Mentor and coach other engineers across levels through design reviews, pairing, and thoughtful feedback. You will help grow future technical leaders, raise the technical bar for the team, and ensure knowledge is shared rather than concentrated.
• Collaborate closely with Product, Design, Data, and operational partners to define problems, shape trade offs, and agree on success metrics, ensuring Amplify’s work focuses on the highest value opportunities and that outcomes are clearly measured.
• Represent Amplify’s systems and strategy in broader engineering forums, contributing to architectural discussions, technical RFCs, and cross team initiatives, and helping ensure our internal platforms fit well into the wider Klaviyo ecosystem.

Who You Are:

• You care deeply about building software and platforms for the long term, balancing technical quality, delivery pace, and measurable business impact, and you are comfortable making and explaining trade offs at that intersection.
• You have strong hands on experience building and operating highly available, full stack or backend focused SaaS systems, and have been responsible for the health and evolution of multiple services or product areas, not just a single application.
• You have led delivery of business critical projects that span multiple engineers and months, including discovery, technical design, implementation, rollout, and ongoing maintenance. You are comfortable scrutinising requirements, reducing ambiguity, and setting technical direction for others.
• You have deep expertise in several areas of modern web engineering such as backend services, data pipelines, internal platforms, or AI enabled systems, and a strong understanding of scalable, multi-tenant architectures and the operational realities of running them in production.
• You regularly use AI powered tools in your development workflow and are interested in shaping how a team or organisation uses AI thoughtfully to improve engineering productivity, system capabilities, and internal customer experiences.
• You take ownership of operational excellence for the systems you and your team build, including performance, reliability, observability, and on-call participation where required. You are comfortable defining and maintaining SLOs and holding systems to them.
• You are effective at working through influence and delegation. You know when to keep a task for yourself and when to hand it to others, and you enjoy creating the clarity, patterns, and guardrails that let other engineers move quickly and safely.
• You enjoy questioning convention and continuously improving how things work, whether that is architecture, tooling, workflows, or team processes, and you are comfortable introducing change in a way that brings people along with you.
• You are a clear communicator and collaborative teammate, comfortable leading technical discussions, writing design docs or RFCs, and keeping stakeholders aligned from early discovery through delivery and follow up.
• You enjoy working in small, autonomous, agile teams, shipping incrementally, and partnering closely with product managers, data partners, and other engineers to deliver better outcomes for internal customers.

Nice to Have:

• Experience building internal tools, platforms, or automations for developers and or non technical stakeholders, with a track record of improving reliability, productivity, or operational efficiency across multiple teams.
• Experience providing technical leadership across several related systems or domains, including defining technical vision, setting design patterns, and aligning multiple teams on shared approaches.
• Practical experience building or integrating AI powered systems and agents such as workflows using LLM APIs or SDKs, agentic or automated workflows, retrieval augmented generation pipelines, or evaluation and monitoring frameworks for AI systems.
• Experience working with cloud infrastructure (AWS preferred), infrastructure as code (Terraform), and containerised environments (Kubernetes), and designing services that run reliably at scale in those environments.
• Experience partnering closely with customer service & support, go to market, or operations teams to understand complex workflows and build internal tools that drive measurable efficiency gains.

Technologies We Use:

Klaviyo’s platform is primarily built with Python and React and runs on AWS. Engineers join us from a wide range of technical backgrounds and are supported in learning our stack. Core technologies include:

• Python / Django / FastAPI
• Typescript / React
• MySQL / Redis / Memcached
• RabbitMQ / Celery / Apache Kafka / Apache Pulsar
• AWS / Terraform / Kubernetes

Location & Work Model

This role is based in Dublin, Ireland and follows a hybrid working model. Klaviyo supports work authorization and relocation for this position.

At Klaviyo, we enjoy tackling meaningful engineering challenges and value people who take ownership, learn continuously, and collaborate openly. We are committed to building inclusive teams and encourage applications from candidates of all backgrounds.

Klaviyo is growing fast and we have openings for all skill levels across all of our teams. Learn more about our engineering culture at https://klaviyo.tech

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

#indeedDUB

Team Overview:

The Amplify Engineering team builds internal tools, platforms, and AI-powered automation that help Klaviyos work more efficiently and at scale as the company grows. We partner across the organisation, including Sales, Marketing, Engineering & Product, Customer Service & Success, and Finance, to solve real operational problems with practical, well-designed software.

Our work spans internal applications, 3rd-party integrations, interactive dashboards, CMS integrations, and robust AI systems and agents that automate repetitive tasks, surface the right information at the right time, and help teams make better decisions. We operate in tight feedback loops with our internal customers, shipping quickly, learning fast, and seeing the impact of our work immediately in how deals move faster, tickets resolve sooner, and launches go more smoothly.

This role is for senior engineers who enjoy turning real-world problems into reliable, scalable solutions. The work is highly visible and has a direct impact on how thousands of Klaviyos get their work done every day.

How You’ll Make an Impact:

The Amplify team builds the internal platforms, automation, and AI-driven systems that power how Klaviyos work every day. As a Senior AI Software Engineer on Amplify, you will:

• Design, build, and own complex internal products and platforms, including internal web applications, APIs, data and event pipelines, and AI-powered workflows used daily by Sales, Marketing, Customer Service & Success, R&D, and Finance teams.
• Lead the end-to-end lifecycle of projects and features – from discovery and technical design through implementation, rollout, and long-term ownership.
• Partner closely with internal customers to understand their workflows and constraints, and translate real operational challenges into scalable, maintainable solutions that measurably improve productivity, quality, and reliability.
• Take ownership of the technical and architectural evolution of parts of the Amplify platform, anticipating scalability, reliability, and observability needs as usage grows.
• Define and maintain operational standards for the systems you own, including SLOs, monitoring, incident response, and follow-up on root-cause analysis.
• Use modern AI tools as part of your day-to-day engineering workflow (e.g. LLM-based tooling for design, coding, testing, and documentation), and help shape best practices for how the team applies AI responsibly and effectively.
• Contribute to shared patterns, tooling, and processes that make it easier for teams across Klaviyo to build high-quality internal tools and AI-powered systems consistently.
• Mentor and support other engineers through design reviews, pairing, and constructive feedback, leaving the codebase and systems better than you found them.
• Collaborate closely with Product, Design, Data, and operational partners to define requirements, trade-offs, and success metrics, ensuring Amplify’s work focuses on the highest-value problems.

Who You Are:

• You care deeply about building software for the long term, balancing technical quality, delivery pace, and business impact.
• You have strong hands-on experience building and operating highly available, full-stack SaaS systems, and are comfortable owning complex problems end to end.
• You’ve led the delivery of significant projects or features independently, including discovery, technical design, implementation, rollout, and ongoing maintenance, and you’re comfortable providing technical direction to others.
• You have deep expertise in one or more areas of modern web engineering (e.g. backend services, data pipelines, internal platforms, or AI-enabled systems) and a solid understanding of scalable, multi-tenant architectures.
• You regularly use AI-powered tools in your development workflow and are curious about applying AI thoughtfully to improve engineering productivity and system capabilities.
• You take ownership of operational excellence for the systems you build, including performance, reliability, observability, and on-call participation where required.
• You enjoy questioning convention and continuously improving how things work, whether that’s architecture, tooling, workflows, or team practices.
• You are a clear communicator and collaborative teammate, comfortable leading technical discussions, writing design docs or RFCs, and keeping stakeholders aligned.
• You enjoy working in small, autonomous, agile teams, shipping incrementally, and partnering closely with product managers and other engineers to deliver better outcomes.

Nice to have:

• Experience building internal tools, platforms, or automations for developers and/or non-technical stakeholders, with a track record of improving reliability, productivity, or operational efficiency.
• Hands-on experience in full-stack environments, including diagnosing and improving application performance, database queries, and complex, multi-service workflows.
• Practical experience building or integrating AI-powered systems and agents; e.g., workflows using LLM APIs/SDKs, agentic or automated workflows, RAG pipelines, or evaluation/monitoring frameworks.
• Experience working with cloud infrastructure (AWS preferred), infrastructure-as-code (Terraform), and containerised environments (Kubernetes), and designing services that run reliably at scale.

Technologies We Use:

Klaviyo’s platform is primarily built with Python and React and runs on AWS. Engineers join us from a wide range of technical backgrounds and are supported in learning our stack. Core technologies include:

• Python / Django / FastAPI
• Typescript / React
• MySQL / Redis / Memcached
• RabbitMQ / Celery / Apache Kafka / Apache Pulsar
• AWS / Terraform / Kubernetes

Location & Work Model:

This role is based in Dublin, Ireland and follows a hybrid working model. Klaviyo supports work authorization and relocation for this position.

At Klaviyo, we enjoy tackling meaningful engineering challenges and value people who take ownership, learn continuously, and collaborate openly. We are committed to building inclusive teams and encourage applications from candidates of all backgrounds.

Klaviyo is growing fast and we have openings for all skill levels across all of our teams. Learn more about our engineering culture at https://klaviyo.tech

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

About the role

As a Solutions Architect on the Startups Applied AI team at Anthropic, you will win the trust of founders and engineers by being an exceptional technical partner, helping startups successfully build on the Claude Developer Platform as they grow from early product to scale. You'll combine deep technical expertise with a builder-first mindset to help startups architect innovative LLM solutions, win technical evaluations, and get the most out of Claude.

Working closely with Account Executives and the broader Sales, Product, and Engineering teams, you'll guide startups from initial technical discovery through successful deployment and beyond. You'll leverage your expertise to help founders understand Claude's capabilities, develop evals, and design architectures that maximize the value of our AI systems.

Responsibilities:

• Partner with Account Executives to deeply understand startup requirements, translate them into technical solutions, and serve as a trusted technical advisor throughout the Claude adoption journey from initial evaluation through deployment and expansion
• Win technical evaluations that demonstrate why Claude is the best foundation for what startups are building, helping them develop evaluation frameworks to measure Claude's performance for their specific use cases
• Build technical credibility with founders, founding engineers, and startup engineering teams by speaking their language, understanding their build patterns, and guiding technical architecture decisions
• Gather insights on how startups are building with Claude, identify emerging use cases and deployment patterns, and deliver feedback to Product and Engineering teams
• Travel to customer sites, startup-focused events, and industry conferences for workshops, technical deep dives, and relationship building

You may be a good fit if you have:

• 5+ years of experience in technical customer-facing roles such as Solutions Architect, Sales Engineer, Forward Deployed Engineer, or technical founder who has done founder-led sales
• Experience working with startups or high-growth technology companies; you understand the velocity and constraints of early-stage companies
• Strong builder credibility through experience as a software engineer or technical founder; you speak the language of builders and have the presence to earn the trust of technical founders and startup engineering teams
• Deep technical proficiency with building and scaling AI products, a pulse on AI engineering best practices, and the ability to demonstrate why Claude is the best foundation
• Hands-on experience building and deploying LLM-powered applications in production, with expertise in context engineering, evaluation frameworks, and modern AI architectures
• Strong technical communication skills with the ability to translate complex AI concepts into actionable insights for founders and engineering teams
• Track record of selling technical products in competitive markets
• Comfortable with Python and familiarity with common LLM frameworks, tools, and integration patterns
• Passion for making powerful technology safe and societally beneficial

The Impact You'll Drive

As a Senior ML Engineer (Speech Synthesis), you’ll be a founding member of the team building Telnyx’s next-generation speech synthesis systems. This is a greenfield opportunity — you’ll define the stack, architecture, and best practices for training and deploying state-of-the-art multilingual text-to-speech (TTS) models that power our voice AI agents.

You’ll build everything from distributed training pipelines to inference services that generate ultra-low-latency, lifelike voices across dozens of languages. Your work will bridge research and production — shaping how millions of people experience real-time conversational AI.

What You’ll Work On

• Own the stack from day one: Design and implement the ML training and inference pipelines for multilingual speech synthesis.
• Low-latency TTS: Engineer systems optimized for real-time, streaming speech generation with sub-100ms response targets.
• Train cutting-edge models: Build and fine-tune multilingual TTS systems using modern architectures — including LLM-based, diffusion, and flow-matching approaches.
• Massive-scale data processing: Develop pipelines for ingesting, aligning, and normalizing text, audio, and phonetic data across dozens of languages.
• Experimentation at scale: Run distributed training across multi-node GPU clusters, tracking results and iterating quickly.
• Cross-functional collaboration: Work with infrastructure and voice platform teams to deploy models that scale globally.
• Research meets production: Evaluate emerging techniques (LLM-guided synthesis, zero/few-shot voice cloning, full-duplex modeling) and bring them to life in production-grade systems.

What You’ll Work With

• Infrastructure: Docker, Kubernetes, Ray, Kubeflow, MLflow, Weights & Biases
• Data Systems: Kafka, Redis, PostgreSQL, Parquet
• You’ll define it: You’ll help select and implement the stack that supports distributed training, data processing, and inference for global deployment.

What We’re Looking For

• 6+ years of experience in machine learning or speech systems engineering
• Hands-on expertise with neural TTS, speech synthesis, or adjacent areas (ASR, voice cloning, multilingual modeling)
• You’ve obsessed over one or two hard problems, whether it’s building multilingual TTS from noisy data, teaching LLMs to speak, designing self-supervised audio encoders, or making diffusion models run in real time.
• Experience with LLM-based approaches to speech synthesis or prosody control
• Strong proficiency in Python and PyTorch
• Ability to deploy models efficiently (ONNX, TensorRT)
• Experience leading small teams and defining technical direction or team executables
• Production mindset: You build systems that run fast, stay stable, and are easy to maintain

Why Telnyx

You’ll be joining a company where voice, infrastructure, and AI converge. Telnyx is building the foundation for real-time, intelligent global communications — and your work on multilingual TTS will be at the core of that vision.



#LI-KG1
#LI-REMOTE

About the Role

We’re looking for a Junior Security Operations Engineer who is AI-Forward to help scale and modernize our SecOps program. This is a hands-on, builder role for someone who will design and ship the security tooling that powers our triage, investigations, and response workflows.

You’ll report to the Technical Operations Director and work alongside our GRC lead to improve our vulnerability intake, threat response, darkweb posture, and internal security tooling. A core part of this role is building AI-assisted security tooling: triage agents that pre-classify bug bounty reports, investigation copilots that pull context from logs and SIEM data, response workflows that draft remediation steps and track them to closure. You’ll spend as much time wiring up that tooling as you will reproducing vulnerabilities and working incidents.

This role suits someone who thrives in a lean, high-impact environment, has strong opinions on where humans add value versus where tooling should take over, and wants to shape how a modern security team operates.

What You’ll Do

Triage & Vulnerability Management

• Review incoming vulnerability reports from our bug bounty intake; reproduce and document valid issues for engineering teams.
• Build the tooling that improves signal-to-noise: automate duplicate detection, spam filtering, and abuse flagging so humans only touch what’s worth touching.
• Act on DAST findings: coordinate remediation, verify fixes, and re-test.
• Track remediation timelines for critical vulnerabilities and keep stakeholders honest on SLAs.

Threat Response & Monitoring

• Monitor and respond to EDR and cloud security alerts; investigate, contain, and document.
• Analyze darkweb findings and credential exposures; shape our darkweb monitoring practices and tooling over time.
• Improve detection coverage: tune noisy rules, close gaps, and enrich alerts with context so responders spend time on decisions, not data gathering.
• Help configure and tune DLP, SIEM, and AI security tooling.

Security Tooling (core to this role)

• Build AI-assisted triage tooling that pre-classifies bug bounty reports, flags duplicates, and routes real issues to the right engineering team with reproduction context attached.
• Build investigation tooling: LLM-backed copilots and Slack bots that pull alert context, correlate signals across sources, and surface what actually matters.
• Build response tooling: workflows that draft remediation steps, generate evidence artifacts, and track issues to closure across the teams that own them.
• Evaluate emerging AI security tooling and bring what’s genuinely useful into the stack; retire what isn’t.
• Apply a security-minded lens to our own AI usage: prompt injection, data exfiltration, model misuse, and the guardrails that go around them.

Compliance & Cross-Functional

• Support audit evidence collection for SOC 2, ISO 27001, and PCI DSS.
• Partner with ITOps to verify patches, endpoint coverage, and access hygiene.

You May Be a Good Fit If You Have

• Previous experience in a SecOps, Security Analyst, or Threat Response role.
• Proven ability to understand and reproduce technical vulnerabilities. You can read a bug report and tell whether it’s real.
• Experience with bug bounty triage (HackerOne, Bugcrowd, or similar).
• Hands-on exposure to SIEM, EDR, and DLP tools in production environments.
• A genuine, demonstrated interest in applying AI/LLMs to security work: side projects, internal tooling, prompt engineering, agent frameworks, anything that shows you’ve actually built with this stuff.
• Scripting and automation skills in Python, Bash, or similar. You reach for code before you reach for a checklist.
• Comfort working autonomously across time zones and asynchronously.
• Strong written communication. You can turn a messy investigation into a clear, defensible writeup.

Bonus Points For

• Experience building LLM agents, RAG pipelines, or Slack bots backed by OpenAI / Anthropic / open-source models.
• Detection engineering experience (Sigma rules, custom Falcon queries, Grafana/Loki dashboards).
• Cloud security depth in AWS or GCP (IAM, workload posture, cloud-native logging).
• Prior work in regulated environments (SOC 2, ISO 27001, PCI DSS) from the practitioner side.
• Exposure to AI/ML security concerns such as prompt injection, model evals, and data leakage through LLMs.
• Public contributions: blog posts, CVEs, open-source tools, CTF writeups.

Engagement & Logistics

• Full-time contract.
• Remote-first and async-friendly. We have hubs in San Francisco, Denver, Dublin, and Amsterdam. If you’re near one, we’d love to see you in the office regularly; if you’re elsewhere in the world, that works too.
• Potential to extend or convert based on fit.
• Reports to the Technical Operations Director; works closely with the GRC lead and IT Operations.

How We Work

We’re a lean, high-trust team. We value people who ship, who can operate independently, and who treat security as an engineering problem rather than a checklist. If you’re someone who sees a repetitive task and immediately thinks “this should be a script, or better yet, an agent,” you’ll fit in here.

To Apply

Tell us about a time you used AI, automation, or custom tooling to meaningfully change how a security workflow ran. What was manual before, what it looked like after, and what you learned. Links to code, writeups, or demos are welcome.

Ready to be the catalyst for transformation within a global organization?

At Supermetrics, we're looking for an entrepreneurial and innovative BizOps Engineer to join our team. This isn't your typical technical or engineering role. You will be a force multiplier, tasked with fundamentally rethinking and rebuilding how our organization operates.

Reporting directly to the CIO you will have a unique mandate: to analyze our processes, design and implement groundbreaking AI-native solutions and human-in-the-loop automations that delight. If you are a creative problem-solver who loves to build, experiment, and drive change, this role offers a unique chance to make a massive impact.

In a nutshell, we’re looking for a lateral thinker at heart, the type of person who thrives on the creative problem-solving and 'out-of-the-box' strategy found in complex board games or tabletop roleplaying.

What You'll Do:

• Analyze and Identify: Dive deep into the processes and workflows of our teams to pinpoint inefficiencies and opportunities for radical improvement.
• Design and Architect: Act as the chief architect for new operational models. You will design innovative, tech-enabled solutions and streamlined processes that dramatically increase productivity and effectiveness.
• Build and Implement: Engage in hands-on development to bring your vision to life. This includes building internal tools, connecting systems via APIs, and deploying automation solutions using languages like Python or Go.
• Deploy and Monitor: Work with platform teams to deploy code, models, and more including working with Terraform, cloud providers, and logging/monitoring systems.
• Leverage AI: Be a go-to expert for applying AI and LLM technologies to solve real-world problems for our teams, moving beyond theory to practical implementation.
• Be an agent of change by collaborating with various internal partners to implement improvements. You will champion your solutions and lead enablement efforts, ensuring teammates and their managers are on board and proficient with new tools and processes.
• Partner and Collaborate: Work cross-functionally, especially with our Data team, Chief of Staff, and other CIO teams, to leverage the right resources and ensure your solutions are robust, scalable, and effective.

What We're Looking For:

• Proven impact and a bias to act: You have a demonstrated history of solving complex problems with clever, effective solutions. We're more interested in your portfolio of impactful projects than your years of experience.
• Entrepreneurial Mindset: You are a self-starter who thinks like an owner. You possess the wits and "lateral thinking" to see challenges from new angles and the drive to move ideas from concept to completion.
• Hands-On Builder: While this isn't a pure software engineering role, you have to have practical experience building and deploying solutions in Python, Go, or a similar language. You know how to help yourself and create simple yet powerful applications and integrations.
• AI and LLM Proficiency: You are fluent in the AI and LLM tech space with hands-on experience in applying these technologies to business processes. You love the idea of sparring with Claude Code to create out of the box solutions.
• Business Acumen: You have experience working with business systems and an understanding of their inner workings. Familiarity with tools in our stack (Hubspot, Salesforce, Zendesk, BigQuery) is a major plus.
• Effective Communicator and Influencer: You can clearly articulate complex ideas and inspire others. You are skilled at teaching new concepts and driving adoption across teams.

Nice-to-Haves:

• Experience in a fast-growing B2B SaaS company.
• Familiarity with Google Cloud Platform (GCP).
• A solid portfolio of personal or professional projects you can share and discuss.

Benefits we offer…

• A comprehensive benefits package that includes equity options
• Great work equipment, and home office allowance for those working in our fully remote locations
• Health care benefits and leisure time insurance
• Sports and well-being allowance

Does this sound like your next career adventure? Apply now! We’ll be filling the role as soon as the right candidate is identified.

Hear why our team likes it here at supermetrics.com/careers/life-at-supermetrics.

 #LI-Hybrid #LI-FullTime #LI-MiddleToSeniorLevel

Supermetrics is committed to providing a welcoming and inclusive workplace for all. We believe that a diverse workforce is a strong workforce, and we are dedicated to creating an environment where everyone feels valued and respected.

If you require any reasonable accommodations during the application or interview process, please do not hesitate to let us know. All requests for accommodation will be kept confidential.

Position: 

We are seeking a Technical Success Manager to own long-term client relationships with FastSpring's most technically sophisticated, high potential clients. These are businesses who have advanced technical, product or operational complexity and play a key strategic role in FastSpring's present and future success. This role will serve as the primary owner of these accounts, with ownership over both the technical and business elements of the relationship. FastSpring is looking for a candidate who can act as a hands-on technical partner, able to invest in fully understanding our customer's business and integration, and maintain highly engaged customer relationships. 

This is a key role in a new team, complementing an existing Customer Success department. This position will also work closely with FastSpring's Customer Success Engineering team, and collaborate with them in their work with a broader range of managed clients.

Independent and proactive problem-solving is expected, with a high degree of organization, creativity and end-to-end responsibility. The candidate will work cross-departmentally, representing our customers in conversations with key internal departments such as Product, Engineering, Marketing, Sales, etc. The customers the Technical Success team work with have close relationships with FastSpring's leadership, and consequently the TSM role will work closely with executive level leadership. 

This position reports to the Senior Manager of Technical Success and is based in Dublin, Ireland. The FastSpring Sales department has an office in Dublin, and while some time spent with that team is encouraged, remote working is allowed and there is no obligation to work on location. The candidate is expected to be able to occasionally travel onsite to client location, or to internal events internationally.

Responsibilities:

• Serve as the primary relationship manager and contact for FastSpring's more technically sophisticated and engaged clients. These are accounts that require a high degree of ownership, collaboration and a deep understanding of their business and solution end-to-end. The focus of this role is fully owning the relationship with these clients on a day-to-day basis.
• Provide actionable guidance, solution recommendations, integration and domain expertise, and proactive technical leadership.
• Connect technical needs to business outcomes, ensuring that technical engagement and solutions directly support long-term customer revenue goals and growth.
• Translate client technical requirements or challenges into short-term Product & Engineering feature requests or bug fixes as well as long-term input into FastSpring's strategic roadmap.
• Collaborate with the broader Customer Success Engineering and Customer Success teams in their client work, leveraging each other's experience and expertise to support clients. Share best practises and technical insights to strengthen the Customer Success team as a whole.
• Proactively identify, forecast and progress commercial opportunities with FastSpring's managed clients.
• Lead executive business reviews on a regular cadence with clients.
• Operate cross-departmentally, working closely with FastSpring's Success, Support, Sales, Marketing, Finance, Product and Engineering departments.

Qualifications & Experience:

• 3–5 years in a customer-facing technical role (TAM, TSM, or Sales Engineering), ideally in a B2B SaaS environment.
• Communication: proven ability to translate complex technical concepts to non-technical stakeholders (and vice versa). Strong internal communication skills are a must, especially using online collaboration tools such as Slack and email.
• Problem Solving: ability and mindset to creatively and quickly investigate issues, problems or to test out suggestions before presenting to a customer or before escalating to FastSpring's engineering team. Candidates must be comfortable using logs, writing quick test code, investigating API responses and troubleshooting independently.
• Project Management: experience managing multiple projects or accounts simultaneously with a high degree of organization.
• Data literacy: experience building and working with data queries, pulling and analysing reports and data, deriving insights and compelling narratives from that data to share with clients (or internal stakeholders).

Tools & Technology:

Required:

• Front-end: HTML, CSS, JavaScript required; SPA experience (ReactJS, Vue, etc).
• Back-end / APIs: should be comfortable with server-side development: webhook listeners, API calls, experience with at least one back-end framework such as Node.js, PHP, .NET, etc.
• Productivity & Collaboration: Slack, Google Workspace, Zoom (or equivalent tools.
• Analytics/Data: Looker, SQL or other equivalent business intelligence/data analysis tool or language.

Nice to have:

• Payment domain knowledge: familiarity with payments landscape and payment methods, card processing, transaction lifecycle, fraud, 3DS, tokenisation, subscription management, compliance, etc.
• Business & Engineering Tooling: Salesforce, Gong, Jira, Confluence
• eCommerce & Analytics: payment gateways, Google Analytics/Google Tag Manager

AI: confident but considered usage of LLM AI systems to enhance work, research, investigate issues, expedite development, etc.

Compensation: This role offers a base salary range of €59,000 to €70,000, with an additional 15% performance-based bonus opportunity, bringing total on-target earnings (OTE) to €67,850–€80,500 depending on base salary placement.

What's the opportunity?

We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Intercom’s next generation of AI products.

Intercom is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month. 

You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks, and is the foundation of the AI Group's full stack approach to AI.

We’re particularly interested in engineers who have:

• A track record of working on model training or model inference at scale, or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better.

What will I be doing?

As a Senior AI Infrastructure Engineer focused on model training and inference, you will:

• Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation.
• Build and optimize inference services that deliver low‑latency, high‑reliability experiences for our customers, including autoscaling, routing, and fallbacks.
• Work on GPU‑level performance: tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack.
• Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production.
• Play an active role in hiring, mentoring, and developing other engineers on the team.
• Raise the bar for technical standards, reliability, and operational excellence across Intercom’s AI platform.

Profile we’re looking for:

These are indicative, not hard requirements

We’re looking to hire Senior+ AI Infrastructure Engineers. You’re likely a great fit if:

• You have 5+ years of experience in software engineering, with a strong track record of shipping high‑quality products or platforms.
• You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals).
• You have hands‑on experience with one or more of the following:
• Model training (especially transformers and LLMs).
• Model inference at scale (again, especially transformers and LLMs).
• Low‑level GPU work, such as writing CUDA or Triton kernels.
• Comfortable working in production environments at meaningful scale (traffic, data, or organizational).
• You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers.
• You take pride in strong technical fundamentals, love learning, and are willing to invest in your own development.
• Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly.

Bonus skills & attributes

None of these are required, but they’re nice to have:

• Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies).
• Experience running training or inference workloads on Kubernetes.
• Experience with AWS or other major cloud providers.
• Production experience with Python in ML or infrastructure contexts.
• Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work!
• Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated.
• Pension scheme & match up to 4%
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Flexible paid time off policy
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too 
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

#LI-Hybrid

What's the opportunity?

We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Intercom’s next generation of AI products.

Intercom is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month. 

You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks, and is the foundation of the AI Group's full stack approach to AI.

We’re particularly interested in engineers who have:

• A track record of working on model training or model inference at scale, or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better.

What will I be doing?

As a Senior AI Infrastructure Engineer focused on model training and inference, you will:

• Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation.
• Build and optimize inference services that deliver low‑latency, high‑reliability experiences for our customers, including autoscaling, routing, and fallbacks.
• Work on GPU‑level performance: tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack.
• Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production.
• Play an active role in hiring, mentoring, and developing other engineers on the team.
• Raise the bar for technical standards, reliability, and operational excellence across Intercom’s AI platform.

Profile we’re looking for:

These are indicative, not hard requirements

We’re looking to hire Senior+ AI Infrastructure Engineers. You’re likely a great fit if:

• You have 5+ years of experience in software engineering, with a strong track record of shipping high‑quality products or platforms.
• You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals).
• You have hands‑on experience with one or more of the following:
• Model training (especially transformers and LLMs).
• Model inference at scale (again, especially transformers and LLMs).
• Low‑level GPU work, such as writing CUDA or Triton kernels.
• Comfortable working in production environments at meaningful scale (traffic, data, or organizational).
• You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers.
• You take pride in strong technical fundamentals, love learning, and are willing to invest in your own development.
• Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly.

Bonus skills & attributes

None of these are required, but they’re nice to have:

• Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies).
• Experience running training or inference workloads on Kubernetes.
• Experience with AWS or other major cloud providers.
• Production experience with Python in ML or infrastructure contexts.
• Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary, annual bonus and equity
• Regular compensation reviews - we reward great work!
• Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated.
• Generous paid time off above statutory minimum
• Hybrid working
• MacBooks are our standard, but we also offer Windows for certain roles when needed.
• Fun events for employees, friends, and family!

#LI-Hybrid

What's the opportunity?

We’re looking for Senior+ AI Infrastructure Engineers to build the systems that train and serve Intercom’s next generation of AI products.

Intercom is an AI company that builds from the GPU all the way up to a user agent that resolves millions of customer service queries a month. 

You’ll join a small, highly technical team working at the cutting edge of modern AI infrastructure. The AI Infra team built the training pipelines and runs the inference for custom models like Fin Apex, which outperforms frontier models in customer service tasks, and is the foundation of the AI Group's full stack approach to AI.

We’re particularly interested in engineers who have:

• A track record of working on model training or model inference at scale, or on low‑level GPU coding (e.g. CUDA, Triton). Experience with one is great, multiple is even better.

What will I be doing?

As a Senior AI Infrastructure Engineer focused on model training and inference, you will:

• Implement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation.
• Build and optimize inference services that deliver low‑latency, high‑reliability experiences for our customers, including autoscaling, routing, and fallbacks.
• Work on GPU‑level performance: tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack.
• Collaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production.
• Play an active role in hiring, mentoring, and developing other engineers on the team.
• Raise the bar for technical standards, reliability, and operational excellence across Intercom’s AI platform.

Profile we’re looking for:

These are indicative, not hard requirements

We’re looking to hire Senior+ AI Infrastructure Engineers. You’re likely a great fit if:

• You have 5+ years of experience in software engineering, with a strong track record of shipping high‑quality products or platforms.
• You hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals).
• You have hands‑on experience with one or more of the following:
• Model training (especially transformers and LLMs).
• Model inference at scale (again, especially transformers and LLMs).
• Low‑level GPU work, such as writing CUDA or Triton kernels.
• Comfortable working in production environments at meaningful scale (traffic, data, or organizational).
• You communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non‑engineers.
• You take pride in strong technical fundamentals, love learning, and are willing to invest in your own development.
• Have deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly.

Bonus skills & attributes

None of these are required, but they’re nice to have:

• Experience at AI native companies that train and/or run inference for their own models (e.g. modern AI labs or AI‑native product companies).
• Experience running training or inference workloads on Kubernetes.
• Experience with AWS or other major cloud providers.
• Production experience with Python in ML or infrastructure contexts.
• Demonstrated passion for technology through personal projects, open source, meetups, or publishing content about your work and learnings

Benefits

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work!
• Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated.
• Pension scheme & match up to 4%
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Flexible paid time off policy
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too 
• MacBooks are our standard, but we also offer Windows for certain roles when needed.

#LI-Hybrid

What's the opportunity? 

The Research, Analytics & Data Science (RAD) team turns insight into action. We uncover customer, product, and business insights and translate them into tools and decision systems embedded directly into GTM workflows.

AI has unlocked an entirely new generation of internal tools for our GTM teams. We’re evolving from static dashboards to LLM and agent-powered workflows that do the work: auto-researching accounts, summarizing prior interactions, drafting personalized outreach, flagging renewal risk, and assembling decks and docs - enabling Sales and Success to focus on high-value conversations.

The RAD team partners closely with GTM Systems to define problems, solutions, and measure impact - from prototype to production.

This is a high-ownership role for someone who thrives in ambiguity, is energized by solving complex real-world problems, and is motivated by seeing their work translate into tangible business impact.

What will I be doing? 

• Design, evaluate, and ship AI-powered internal tools for GTM use cases - including account research & summaries, next-best-action recommendations, renewal propensity, pipeline risk detection, QBR/autobrief generation, and post-call summarization & follow-ups.
• Work end-to-end: Own the full lifecycle, from problem definition and data modeling to building production-ready tools, including writing Python backends and React frontends.
• Prototype fast, ship to learn: Rapidly build with users, then productionize quickly to iterate and deliver impact.
• Instrument for adoption and outcomes: Define success through real usage and measurable business impact (e.g., improved win rate, conversion, expansion).
• Evangelize and enable: Document playbooks, run enablement sessions, and help leaders operationalize new tooling across teams.

What skills do I need? 

• Proven track record of applied data science with measurable GTM impact - you’ve shipped models or tools that moved metrics like conversion, cycle time, or retention.
• LLM/ML application experience - familiarity with RAG, prompt and tool design, vector search, evals and have leveraged AI for development.
• Excellent SQL skills and fluency in Python or R, with experience applying analytical and statistical methods to business problems.
• Experience with orchestration tools (e.g., DBT, Airflow) for deploying reliable data workflows.
• Strong communication and empathy - ability to translate complex data concepts for non-technical stakeholders.
• Collaborative product mindset - comfort working closely with Sales and Success teams to turn ambiguity into clear deliverables.

Benefits 

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Open vacation policy and flexible holidays so you can take time off when you need it
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• MacBooks are our standard, but we’re happy to get you whatever equipment helps you get your job done

#LI-Hybrid

What's the opportunity? 

The Research, Analytics & Data Science (RAD) team turns insight into action. We uncover customer, product, and business insights and translate them into tools and decision systems embedded directly into GTM workflows.

AI has unlocked an entirely new generation of internal tools for our GTM teams. We’re evolving from static dashboards to LLM and agent-powered workflows that do the work: auto-researching accounts, summarizing prior interactions, drafting personalized outreach, flagging renewal risk, and assembling decks and docs - enabling Sales and Success to focus on high-value conversations.

The RAD team partners closely with GTM Systems to define problems, solutions, and measure impact - from prototype to production.

This is a high-ownership role for someone who thrives in ambiguity, is energized by solving complex real-world problems, and is motivated by seeing their work translate into tangible business impact.

What will I be doing? 

• Design, evaluate, and ship AI-powered internal tools for GTM use cases - including account research & summaries, next-best-action recommendations, renewal propensity, pipeline risk detection, QBR/autobrief generation, and post-call summarization & follow-ups.
• Work end-to-end: Own the full lifecycle, from problem definition and data modeling to building production-ready tools, including writing Python backends and React frontends.
• Prototype fast, ship to learn: Rapidly build with users, then productionize quickly to iterate and deliver impact.
• Instrument for adoption and outcomes: Define success through real usage and measurable business impact (e.g., improved win rate, conversion, expansion).
• Evangelize and enable: Document playbooks, run enablement sessions, and help leaders operationalize new tooling across teams.

What skills do I need? 

• Proven track record of applied data science with measurable GTM impact - you’ve shipped models or tools that moved metrics like conversion, cycle time, or retention.
• LLM/ML application experience - familiarity with RAG, prompt and tool design, vector search, evals and have leveraged AI for development.
• Excellent SQL skills and fluency in Python or R, with experience applying analytical and statistical methods to business problems.
• Experience with orchestration tools (e.g., DBT, Airflow) for deploying reliable data workflows.
• Strong communication and empathy - ability to translate complex data concepts for non-technical stakeholders.
• Collaborative product mindset - comfort working closely with Sales and Success teams to turn ambiguity into clear deliverables.

Benefits 

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated. 
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Open vacation policy and flexible holidays so you can take time off when you need it
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• MacBooks are our standard, but we’re happy to get you whatever equipment helps you get your job done

#LI-Hybrid

What's the opportunity? 

The Research, Analytics & Data Science (RAD) team turns insight into action. We uncover customer, product, and business insights and translate them into tools and decision systems embedded directly into GTM workflows.

AI has unlocked an entirely new generation of internal tools for our GTM teams. We’re evolving from static dashboards to LLM and agent-powered workflows that do the work: auto-researching accounts, summarizing prior interactions, drafting personalized outreach, flagging renewal risk, and assembling decks and docs - enabling Sales and Success to focus on high-value conversations.

The RAD team partners closely with GTM Systems to define problems, solutions, and measure impact - from prototype to production.

This is a high-ownership role for someone who thrives in ambiguity, is energized by solving complex real-world problems, and is motivated by seeing their work translate into tangible business impact.

What will I be doing? 

• Design, evaluate, and ship AI-powered internal tools for GTM use cases - including account research & summaries, next-best-action recommendations, renewal propensity, pipeline risk detection, QBR/autobrief generation, and post-call summarization & follow-ups.
• Work end-to-end: Own the full lifecycle, from problem definition and data modeling to building production-ready tools, including writing Python backends and React frontends.
• Prototype fast, ship to learn: Rapidly build with users, then productionize quickly to iterate and deliver impact.
• Instrument for adoption and outcomes: Define success through real usage and measurable business impact (e.g., improved win rate, conversion, expansion).
• Evangelize and enable: Document playbooks, run enablement sessions, and help leaders operationalize new tooling across teams.

What skills do I need? 

• Proven track record of applied data science with measurable GTM impact - you’ve shipped models or tools that moved metrics like conversion, cycle time, or retention.
• LLM/ML application experience - familiarity with RAG, prompt and tool design, vector search, evals and have leveraged AI for development.
• Excellent SQL skills and fluency in Python or R, with experience applying analytical and statistical methods to business problems.
• Experience with orchestration tools (e.g., DBT, Airflow) for deploying reliable data workflows.
• Strong communication and empathy - ability to translate complex data concepts for non-technical stakeholders.
• Collaborative product mindset - comfort working closely with Sales and Success teams to turn ambiguity into clear deliverables.

Benefits 

We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! 

• Competitive salary and equity in a fast-growing start-up
• We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen
• Regular compensation reviews - we reward great work
• Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents
• Open vacation policy and flexible holidays so you can take time off when you need it
• Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones
• MacBooks are our standard, but we’re happy to get you whatever equipment helps you get your job done

#LI-Hybrid

About the role:

Our Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry's largest compute-agnostic inference deployments.  We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.

The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.

As a Staff Software Engineer on our Inference team, you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.

Strong candidates may also have experience with:

• High-performance, large-scale distributed systems

• Implementing and deploying machine learning systems at scale

• Load balancing, request routing, or traffic management systems

• LLM inference optimization, batching, and caching strategies

• Kubernetes and cloud infrastructure (AWS, GCP)

• Python or Rust

You may be a good fit if you:

• Have significant software engineering experience, particularly with distributed systems

• Are results-oriented, with a bias towards flexibility and impact

• Pick up slack, even if it goes outside your job description

• Want to learn more about machine learning systems and infrastructure

• Thrive in environments where technical excellence directly drives both business results and research breakthroughs

• Care about the societal impacts of your work

Representative projects across the org:

• Designing intelligent routing algorithms that optimize request distribution across thousands of accelerators

• Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads

• Building production-grade deployment pipelines for releasing new models to millions of users

• Integrating new AI accelerator platforms to maintain our hardware-agnostic competitive advantage

• Contributing to new inference features (e.g., structured sampling, prompt caching)

• Supporting inference for new model architectures

• Analyzing observability data to tune performance based on real-world production workloads

• Managing multi-region deployments and geographic routing for global customers

   

Deadline to apply: None. Applications will be reviewed on a rolling basis.