Top Backend Engineer Jobs in Los Angeles, CA

Build and scale Java backend services, recommendation infrastructure, and data-processing pipelines for Taboola’s performance advertising platform. Design reliable microservices, search and event-driven systems handling billions of events and terabytes of data. Own services end to end, including production monitoring, incident response, root-cause analysis, and continuous improvement. Collaborate with product and architecture teams, manage integration projects, mentor engineers, and use AI-assisted development tools.

Design, develop, test, deploy, and monitor scalable backend systems for a payroll SaaS product. Build APIs and microservices, collaborate with product and engineering teams, participate in code reviews, and own the full development lifecycle in a continuous delivery environment. The role emphasizes Kotlin, Scala, or Java, cloud platforms, automated testing, relational databases, scalable architecture, and responsible use of AI coding tools.

Own backend engineering goals for Consumer Engineering, lead delivery of scalable systems, collaborate with product, design, and analytics, improve reliability and code quality, resolve technical and business issues, support on-call operations, and mentor engineers.

Own quarterly engineering goals, lead backend engineers through ambiguous problems, collaborate with product and analytics partners, improve system reliability and availability, monitor operational metrics, establish quality standards, and mentor team members. The role requires designing and launching highly available distributed backend systems at scale using Python or Kotlin, AWS, MySQL, and Kubernetes, while driving technical planning and safe changes across large codebases.

Design and evolve high-scale backend capabilities for GitLab’s container registry. Drive architecture, performance, reliability, security, APIs, testing, and operational readiness. Mentor engineers, influence technical direction, participate in incident response, and collaborate asynchronously across product, infrastructure, data, frontend, and security teams. Integrate and evaluate agentic AI systems in production engineering workflows while promoting responsible and effective use.

Leads cross-team backend initiatives and technical architecture for GitLab’s Core DevOps platform. Responsibilities include modernizing a monolith, establishing service, API, data, security, testing, and observability standards, guiding Go and Kubernetes practices, supporting Rust adoption, participating in incident response, mentoring engineers, reducing technical debt, and advancing AI-assisted engineering workflows.

Design, implement, test, debug, and operate scalable backend services such as identity, friend graph, and persistence systems. Collaborate across teams to deliver product solutions while maintaining availability, scalability, security, operational excellence, and cost efficiency. Investigate incidents and hotfixes, apply distributed-systems best practices, and use AI-assisted development tools while auditing output for quality and security.

Develop and launch reliable backend systems using Python or Kotlin, AWS, MySQL, and Kubernetes. Break projects into deliverable phases, collaborate with product, design, and analytics teams, monitor system metrics, support availability and on-call operations, debug code, conduct code reviews, and contribute to team growth and interviewing. The role requires 1.5+ years of software engineering experience and strong communication skills.

Design, implement, operate, and scale critical backend services, including identity, social graph, and persistence systems. Collaborate across teams to deliver product solutions, ensure availability and operational excellence, test and debug code, and participate in incident resolution. The role requires technical leadership, distributed-systems expertise, and effective use of AI development tools while maintaining security, performance, and production-quality standards.

Build and deliver backend payment-processing features using Kotlin, scalable APIs, microservices, and containerization. Develop automated tests, monitor feature rollouts, collaborate with UX and product management, and participate in design and code reviews. The role supports Toast’s unified payments platform and requires East Coast working hours.

Lead large-scale initiatives for a high-performance Linux-based network sensor, improving ingestion, scalability, continuous capture, queue reliability, observability, and production diagnostics. Own root cause analysis and postmortems, establish engineering standards, translate business goals into technical plans, and mentor engineers through design and code reviews. The role requires deep Rust expertise, distributed systems experience, concurrent programming, packet capture, message brokers, and cybersecurity best practices.

Architect and operate Affirm’s large-scale Amazon EKS compute platform, including cluster lifecycle, node management, autoscaling, shared components, infrastructure automation, workload safety policies, observability, cost and capacity optimization, and self-service workflows. The role drives technical strategy, mentors engineers, partners cross-functionally, and participates in on-call incident response.

Lead the architecture and technical strategy for Affirm’s high-volume observability platform. Design telemetry pipelines and sampling strategies, improve reliability and cost efficiency, integrate observability into AI-assisted development, and drive platform adoption across engineering teams. Remain hands-on while mentoring six engineers, leading cross-functional initiatives, establishing engineering standards, and overseeing monitoring, alerting, on-call operations, and system availability.

Build and operate high-throughput backend services for AI agents, calendar, scheduling, integrations, real-time data pipelines, and APIs. Design platform infrastructure, event-driven systems, and client-server protocols using Go, PostgreSQL, Kubernetes, and cloud platforms. Collaborate with AI, product, and frontend teams, improve reliability through testing and infrastructure as code, and mentor junior engineers.

Build and operate GitLab’s Rust-based Knowledge Graph backend supporting SDLC and code indexing, graph queries, MCP endpoints, AI agents, and analytics. Integrate with Rails, ClickHouse, NATS JetStream, and Data Insights systems. Improve reliability, observability, scalability, and operational readiness through testing, metrics, runbooks, alerts, and on-call participation. Collaborate asynchronously across product, data, infrastructure, security, and AI teams while contributing to system design and occasional Ruby or frontend work.

Lead backend technical strategy and hands-on development for Growth and Monetization systems, including metering, billing, entitlements, pricing, provisioning, payments, onboarding, notifications, and AI-powered customer journeys. Identify growth opportunities, measure business impact, establish engineering standards, and collaborate with product, design, finance, go-to-market, and business leadership. The role may grow into team leadership.

Build and enforce workload safety standards across Affirm’s Amazon EKS fleet. Responsibilities include Kubernetes resource management, workload isolation, disruption handling, cluster capacity analysis, policy automation, production debugging, incident response, fleet-wide rollouts, technical leadership, mentoring, and measuring infrastructure impacts on availability, latency, risk, and cost.

As a Staff Backend Engineer, you'll design and build scalable platforms for processing sensor data, define engineering standards, and collaborate on backend system development.

Build and ship backend features end-to-end using Ruby on Rails and SQL. Lead complex technical projects, solve architecture challenges, establish standards, conduct meaningful code reviews, resolve high-risk bugs, document systems, collaborate across engineering teams, and mentor junior engineers.

Backend Engineer Interns will help expand Tinder’s service platform by developing and operating scalable distributed services on AWS. Responsibilities include building systems handling billions of daily data events, collaborating with engineering teams, and writing unit and integration tests. Candidates should know at least one programming language, understand data structures and algorithms, be pursuing a relevant degree, and show interest in generative AI. The hybrid internship runs June 1 through August 31, 2027, in Los Angeles or Palo Alto.

Design, build, test, and optimize scalable backend systems, APIs, databases, and production software. Deploy LLM-powered features and AI workflows, evaluate AI outputs, and use coding assistants to improve delivery. Own projects end to end, troubleshoot production issues, contribute to architecture, collaborate with business teams, communicate technical decisions, and mentor peers.

Design, build, test, and optimize scalable backend systems, APIs, databases, and production software. Deploy LLM-powered features and workflows, use AI coding tools, validate AI outputs, and evaluate emerging technologies. Collaborate with business teams, contribute to architecture and Agile delivery, resolve production issues, document decisions, present outcomes, and mentor peers.

Build and ship backend features for Clubhouse’s live audio, reactions, clips, houses, and replays. Evolve scalable backend systems, collaborate with product, design, engineering, and data science teams, run experiments, and make pragmatic tradeoffs that improve engagement and retention for millions of users.

Lead technical vision and strategy for Affirm’s Cards backend engineering organization. Architect scalable and reliable platforms, guide cross-team initiatives, champion AI adoption and engineering excellence, influence technical roadmaps, and mentor engineers through Staff level. The role requires strong architectural expertise, organizational leadership, project management, communication, and cross-functional collaboration.

Own and deliver backend lakehouse infrastructure goals, lead engineers through complex problems, and build highly available distributed data systems. Collaborate with product, design, analytics, and engineering stakeholders; define scalable technical solutions; improve code quality and engineering standards; monitor operational health; support on-call responsibilities; and mentor team members.