Top Remote Senior Level Software Engineer Jobs in Los Angeles, CA

Lead and manage software engineering teams: run agile processes, design and ship secure scalable APIs and services, guide architecture, manage technical debt, recruit and mentor engineers, and align work with company objectives.

Lead the Discovery Platform team, overseeing the development of scalable, high-performance systems for retrieval and ranking to enhance the user experience in Whatnot's live social marketplace.

Lead a compliance and risk engineering pillar to design, deliver, and operate regulatory automation, AI/ML, and risk-control platforms. Own roadmap, reliability, incident and defect management, stakeholder communication with Legal/Cyber/Compliance, and hiring and growth of software and quality engineering teams across global jurisdictions.

Design and implement Android features from design through launch, optimize mobile UX and performance, perform code reviews, evaluate technical tradeoffs, and use AI tools to streamline development while maintaining security and production-quality code.

Lead Apollo’s Rep Experience engineering team building prospecting, account management, task orchestration, dashboards, and AI-driven recommendations. Responsibilities include technical decision-making, architecture, planning, coding mentorship, hiring, people management, on-call sustainability, estimation, process improvement, and cross-functional collaboration with Product, Design, GTM, and engineering leadership.

Lead technical strategy and execution for multi-quarter initiatives, build and maintain software systems using AI, mentor and elevate engineers, collaborate across disciplines, and protect user privacy and security. The role involves working in an AWS and Kubernetes environment with technologies including Python, TypeScript, React, Go, Airflow, dbt, and Claude Code.

Build scalable, API-first platform capabilities through manifest-driven development. Develop Java and Kotlin backend services and Python workers for third-party integrations, collaborate across engineering teams, participate in architecture reviews, and troubleshoot production systems using logs, metrics, and traces. The role requires expertise in highly scalable systems, object-oriented design, REST services, relational databases, responsive messaging, caching, and software quality practices.

Design and implement scalable backup software for endpoints, servers, and cloud applications. Develop Java and Kotlin services, optimize databases and queries, debug production systems, and resolve distributed-system performance issues. Contribute code, reviews, architecture discussions, documentation, and cross-team reliability initiatives. Mentor engineers and help deliver resilient products operating at large scale.

Leads development and architecture of scalable Java software for NinjaOne’s endpoint management platform. Designs high-availability systems, addresses large-scale datastore performance and data-volume challenges, contributes to APIs and platform improvements, drives quality practices, owns technical initiatives, collaborates cross-functionally, and mentors junior engineers.

Design, develop, launch, and maintain scalable backend systems and APIs used by merchant and partner integrations. Collaborate with engineers and stakeholders, balance speed with system quality, debug code, conduct code reviews, and write tested, extensible software. The role requires experience with distributed systems, Python or Kotlin, AWS, MySQL, and Kubernetes, along with strong communication and ownership of professional growth.

Designs, develops, tests, deploys, documents, maintains, and debugs software solutions for PNC’s Asset Management Group. The role addresses complex business needs through technical design, application development, architecture, troubleshooting, testing, and release management. It is remotely based in Pennsylvania, with occasional in-office participation as needed.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.