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 architect and operate the systems that power AI research and product development at scale. They design distributed infrastructure for training, serving, and orchestrating AI workloads across GPU clusters, build internal platforms that accelerate developer velocity, and optimize the critical path from code to production. This role bridges deep systems engineering expertise—in areas like Kubernetes, build systems, data pipelines, and performance tuning—with the unique demands of AI workloads, combining hands-on infrastructure work with close collaboration with researchers and product teams to eliminate bottlenecks that slow down innovation.
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.
Machine Learning Engineer
Machine learning engineers in this role build and optimize systems that translate research models into production—spanning model serving infrastructure, inference performance tuning, and distributed training pipelines. They distinguish themselves by combining deep systems expertise with ML knowledge, working on problems like latency optimization, resource efficiency, and scaling models across heterogeneous hardware and platforms. These engineers typically sit within specialized teams focused on either search and retrieval, robotics, foundation models, or inference optimization, collaborating closely with research teams to operationalize cutting-edge architectures at scale.
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.
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.
Forward Deployed Engineer
Forward Deployed Engineers embed with enterprise customers to architect and operationalize production AI systems that solve domain-specific business problems. Unlike traditional software engineers, they own the full lifecycle from discovery and system design through scaling and optimization, working directly alongside customer teams to translate complex requirements into deployed solutions. These roles typically sit within customer success, professional services, or partnerships teams at AI platform companies, bridging the gap between core product capabilities and real-world customer needs while feeding field insights back to drive product evolution.
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.
Technical Program Manager
Technical Program Managers at AI companies orchestrate complex technical initiatives across multiple engineering teams—translating high-level priorities into structured execution plans, managing dependencies across hardware, software, and research domains, and keeping high-stakes programs on track through ambiguity. The day-to-day is classical TPM craft: running operating cadences, maintaining program-level visibility on risks and milestones, facilitating cross-team trade-off decisions, and translating technical detail into status that executives can act on. Specific scope varies—some TPMs run infrastructure and platform programs, others run model or research programs, others run cross-functional product launches—but the canonical role is recognizable across any technical organization at scale. These roles typically sit within program management offices or alongside engineering leadership, partnering with product, infrastructure, and research counterparts.
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.
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.
Applied AI Engineer
Applied AI Engineers build intelligent features into products by integrating LLMs, retrieval systems, and AI APIs to solve real business problems. Day-to-day, they prototype and productionize AI-powered workflows—from designing agent architectures and evaluation frameworks to implementing retrieval pipelines and optimizing inference costs at scale. They sit between product and infrastructure teams, combining hands-on engineering with deep customer collaboration to ship features that work reliably in production. Unlike ML Engineers who train models or Forward Deployed Engineers who embed at customer sites, Applied AI Engineers own the full stack of AI integration within their own organization's products, from architecture decisions to code contributions and technical mentorship.
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.