Engineering
Building and shipping software, systems, and technical solutions. Covers software engineering (frontend, backend, full-stack, mobile), ML engineering (production systems), AI agent engineering, applied AI engineering, prompt engineering, DevOps/SRE, platform engineering, QA/testing, and technical architecture. The people who write code and ship product.
Roles
The canonical roles within Engineering.
Infrastructure & Platform Engineer
Engineers in this role design, build, and operate the infrastructure systems that power AI model training, inference, and data pipelines at scale. They work across Kubernetes clusters, cloud platforms (AWS, Azure, GCP), GPU compute, networking, and observability tooling—translating the operational needs of researchers and product teams into reliable, performant platform abstractions. What distinguishes this work is its focus on the full lifecycle of AI infrastructure: from provisioning and scaling compute for large training runs, to optimizing data movement and storage efficiency, to diagnosing performance bottlenecks across distributed systems under real workload pressure. These engineers typically sit within dedicated infrastructure or platform teams that directly enable research velocity and production reliability, partnering closely with ML researchers and other product engineers to remove friction from experiment-to-production workflows.
Backend Engineer
Backend Engineers at AI companies build and operate the server-side systems that AI products and infrastructure run on—distributed services, REST APIs, data pipelines, and the databases behind them. The day-to-day is classical backend work: designing services for reliability and scale, optimizing query performance, instrumenting observability, owning on-call and SLOs, and partnering with product and frontend teams to ship features end-to-end. AI-specific surfaces—high-throughput inference serving paths, telemetry pipelines for GPU-dense infrastructure, agent runtime systems—appear in some of these jobs, particularly at infrastructure and platform companies, but the canonical role is recognizable as backend engineering across any high-growth software business. Backend Engineers typically sit within product, platform, or core services teams, often as the foundational layer that both product engineers and ML engineers build on top of.
Engineering Manager
Engineering Managers at AI companies lead engineering teams across the delivery cycle—hiring and developing engineers, setting technical direction in collaboration with senior ICs, owning roadmap and execution, and partnering with product and design counterparts. The work is the standard engineering management craft: 1:1s and growth conversations, architecture reviews and technical trade-off decisions, on-call and incident response, and translating cross-functional priorities into team plans. Technical scope varies widely—some EMs run platform and infrastructure teams, others run product engineering, others run ML or research-adjacent teams—but the canonical role is recognizable across software companies generally, with AI workloads as the specific domain rather than a different management discipline. These managers typically sit within engineering organizations as first-line or second-line leaders, reporting to directors or VPs depending on team size.
Forward Deployed Engineer
Forward Deployed Engineers embed directly with enterprise customers to architect, build, and deploy production AI systems—from RAG pipelines and multi-agent workflows to fine-tuned models and generative applications. They translate ambiguous business problems into well-scoped technical solutions, own end-to-end delivery across the full product lifecycle, and serve as trusted technical advisors who shape both customer success and internal product direction. These roles sit at the intersection of customer needs and engineering excellence, typically within specialized services or customer-facing AI teams that work cross-functionally with product and research to turn field learnings into scalable patterns and platform improvements.
Fullstack Engineer
Fullstack Engineers at AI companies build product features end-to-end across frontend, backend, and the integration layer between them. The day-to-day is recognizable full-stack work: designing API contracts, implementing UI alongside the services that power it, handling auth and data persistence, and owning features from product specification through production. Companies hire for this generalist profile in different contexts—at smaller companies, fullstack engineers often own most of a product surface; at larger companies, the role tends to bridge feature teams that would otherwise hand off across frontend/backend boundaries. AI-specific surfaces—integrating model APIs, building agent UIs, shipping LLM-backed features—are increasingly common but remain one type of feature work rather than the defining lens. These engineers typically sit within product engineering teams, collaborating with product, design, and ML or backend specialists as the architecture requires.
Machine Learning Engineer
Engineers in this role develop and operate production machine learning systems that power real-world AI products, from recommendation and ranking systems at fintech companies to perception models for autonomous vehicles and multimodal foundation models. They bridge research and production by building scalable training infrastructure, data pipelines, and deployment systems that handle massive datasets and complex model architectures while maintaining reliability and performance at scale. These roles typically sit within specialized teams—whether perception, infrastructure, or applied ML—that collaborate closely with product, research, and systems engineers to translate model innovations into deployed systems serving millions of users or real-world safety-critical applications.
Software Engineer
Software Engineer roles at AI companies cover generalist software engineering work that does not neatly fall into frontend, backend, fullstack, ML, or other specialized tracks—often because the job is generalist by design, or because the title has not yet been segmented into a more specific role. The day-to-day is classical software engineering: designing and building software systems, writing well-tested production code, debugging across the stack, participating in the full development lifecycle, and partnering with cross-functional counterparts on what to build. AI-specific surfaces appear in many of these jobs—integrating models, building ML-adjacent infrastructure, working in AI-aware codebases—but the canonical role is software engineering as practiced across any high-growth technology company. These engineers sit across a wide range of teams depending on the company, with the title often serving as a default for engineers whose scope spans multiple areas.
Inference & Performance Engineer
Engineers in this role optimize how AI models train and run in production, focusing on the full stack from GPU kernels and inference runtimes to distributed training systems and cluster orchestration. They bridge research breakthroughs and production reality, writing CUDA/Triton kernels, tuning serving frameworks like vLLM, profiling end-to-end inference pipelines, and solving the performance and efficiency challenges that emerge when deploying transformer models at scale. They typically sit in infrastructure, platform, or systems teams at AI labs and companies, working closely with researchers and product teams to ensure models meet latency, throughput, and cost targets in real deployments.
Technical Program Manager
This role coordinates the execution of complex technical initiatives across distributed engineering teams building AI infrastructure and products. Day-to-day, Technical Program Managers break down ambitious goals into prioritized workstreams with clear ownership and milestones, manage dependencies and risks across hardware, software, and research teams, and maintain visibility into progress through metrics and status communication. What distinguishes this from project management is the technical depth required—these roles engage substantively with infrastructure decisions, model deployment tradeoffs, and system constraints rather than simply tracking tasks. TPMs typically sit within engineering organizations at AI companies, acting as the connective tissue between research, product, and operations teams to ensure initiatives stay aligned, unblocked, and on track as they scale.
Site Reliability Engineer
Engineers in this role maintain the reliability and performance of AI infrastructure at scale, spending their days on incident response, automation, and observability across distributed systems that power AI workloads. They differ from software engineers by focusing on operational excellence and system resilience rather than feature development, and from DevOps roles by owning broader platform-level reliability goals. These teams typically sit within infrastructure or platform organizations, partnering closely with product engineering teams to ensure AI services remain fast, secure, and always available across multiple regions.
AI Agent Engineer
Engineers in this role design and deploy autonomous AI agents that solve real-world business problems across diverse industries, from finance and healthcare to infrastructure and marketing operations. They move fast across the full development lifecycle—from prototyping with frontier LLMs to shipping production systems that handle complex customer interactions, workflow automation, and operational decision-making at scale. What sets this work apart is the emphasis on reliability and observability: these engineers don't just build agents, they ensure they perform consistently in ambiguous, high-stakes environments while integrating with enterprise systems and human operators. Typically embedded in dedicated agent or agentic AI teams within product-focused AI companies, these roles sit at the intersection of platform engineering and direct impact, partnering closely with product managers, domain experts, and cross-functional stakeholders to turn loosely defined opportunities into robust, measurable business outcomes.
Applied AI Engineer
Applied AI Engineers design and ship AI-powered features within their company's product, applying large language models, retrieval systems, and agentic workflows to solve specific product problems. They work end-to-end across backend services, APIs, and user-facing interfaces, taking responsibility for building reliable systems that combine model selection, prompt engineering, tool use, and evaluation frameworks. These engineers typically sit within product or platform teams at AI-native companies, collaborating closely with product managers and research teams to translate user workflows into AI-driven experiences that measurably improve speed, accuracy, or capability—distinguishing them from infrastructure engineers building foundational AI platforms or customer-facing services.
Frontend Engineer
Frontend Engineers at AI companies build and ship the user-facing interfaces that put AI products in front of users—consumer applications, developer tools, internal tools, and enterprise dashboards. The day-to-day is mainstream modern frontend: building and maintaining web applications in React or similar frameworks, contributing to component libraries, optimizing performance and accessibility, and partnering with designers on translating specs into shipped UI. Specific challenges vary by product surface—some teams need heavy data-visualization work for analytics or monitoring tools, others focus on consumer-facing AI interactions, others on developer-facing IDE-like experiences—but the canonical skill set is universal frontend engineering. These engineers typically sit within product engineering teams alongside designers, product managers, and backend engineers, owning features end-to-end through the frontend layer.
Quality Engineer
Engineers in this role focus on testing and validating complex AI software systems across domains like machine learning frameworks, inference platforms, and autonomous systems. They design automated test frameworks, build CI/CD infrastructure, and collaborate with engineering teams to ensure AI products meet stringent quality and performance standards. What distinguishes them is their emphasis on systems-level thinking—they architect scalable testing solutions that handle the unique challenges of AI workloads, from ML model accuracy validation to hardware-software integration testing. These engineers typically sit within larger quality or systems teams in AI-focused companies, working cross-functionally with ML engineers, infrastructure teams, and product owners to accelerate development velocity while maintaining reliability and safety.
Product Security Engineer
Product Security Engineers at AI companies sit within engineering organizations and own security across the software development lifecycle—threat modeling, secure code review, vulnerability management, and the security-relevant tooling that engineers depend on. In practice at AI companies, the role frequently extends past pure application security into the surrounding infrastructure and identity layers: securing CI/CD pipelines, designing IAM and secrets management for application access, and reviewing the cloud architecture the application runs on. The boundary with the infrastructure-side security role is genuinely blurry across the population, with most engineers in this slug doing both. AI-specific surfaces—LLM input handling, agent and tool-use boundaries, model-pipeline integrity—are emerging as a meaningful part of the work but sit alongside, not in place of, classical product security. These roles typically sit within security or product engineering organizations, partnering directly with developers to embed security into the build.
Mobile Engineer
Mobile Engineers at AI companies build native iOS or Android applications for products with consumer- or workforce-facing mobile surfaces. The day-to-day is mainstream mobile development: building and maintaining production applications, optimizing performance across memory, CPU, and battery, architecting modular and testable codebases, and shipping features through the platform-specific release cycles. AI-specific work—integrating remote model APIs, on-device inference, real-time generative experiences—is increasingly common as a feature-level concern, but the foundational role is recognizable as iOS or Android engineering. These engineers typically sit within product engineering teams, often as the only mobile specialists in fast-moving product organizations, collaborating with backend, design, and ML or research counterparts as the feature requires.
Database & Systems Engineer
Engineers in this role design and operate the database and storage systems that underpin AI infrastructure at massive scale, handling everything from query optimization and transaction management to distributed storage architecture. They work deeply with storage engines, cache layers, and multi-database topologies, making critical tradeoffs between consistency, performance, and resilience as their systems support billions of requests and exabyte-scale workloads. Unlike query optimization or distributed systems specialists, these engineers own the full vertical of how data is stored, retrieved, and scaled—partnering with infrastructure and product teams to ensure databases reliably serve both transactional product workloads and compute-intensive AI training pipelines. They typically sit within platform or infrastructure organizations alongside teams building query engines, replication systems, and cloud infrastructure.
Design Engineer
Design Engineers in this role combine pixel-perfect front-end craftsmanship with strong design sensibility to build user-facing experiences for AI products. Working closely with designers and product teams, they own product surfaces end-to-end—from prototyping in code and validating with users to shipping production-quality interfaces with obsessive attention to performance, accessibility, and detail. These engineers typically work in fast-moving AI companies building consumer or creator-focused products, translating complex AI capabilities into intuitive, delightful interfaces that feel magical to users. They move fluidly between design tools and code, prototype rapidly in React/TypeScript, and champion the small details that elevate craft and experience across their entire product.
Recent Jobs
The latest Engineering openings across the AI industry.