23 Platform Engineer jobs in Panama available for remote work from home. Apply for positions such as Senior Data & Platform Engineer – Veterinary Technology, Staff ML Software Engineer (L6) — Platform Systems, AIMS Engineering, Software Engineer (L6), Platform Security and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Take end-to-end ownership of the data platform's reliability, including the design and maintenance of robust pipelines and the Snowflake data warehouse. Collaborate with cross-functional teams to model clean data products and implement comprehensive observability and SLA tracking.
Design and operate observability, evaluation, and tooling subsystems to support next-generation AI/ML architectures. Drive cost optimization and reliability improvements across the AI/ML stack to ensure operational excellence.
Define and evolve the security architecture for the edge and gaming compute stack, ranging from hardware interfaces to operating systems. Implement security enhancements and drive platform integrity through measured boot, improved access controls, and automated vulnerability research.
You will build and ship full-stack features for infrastructure cost and usage tooling, including interactive web applications and backend services. You will also partner with data and analytics engineers to design data models and launch products for analyzing AI productivity and cloud efficiency.
You will be responsible for ideating, building, testing, and deploying robust features for the consumer-facing help center. The role involves collaborating with cross-functional teams and external vendors to deliver scalable, end-to-end solutions while participating in an on-call rotation.
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Design and build scalable, highly available distributed infrastructure for Netflix's data platform, focusing on storage and movement. Collaborate across teams to develop shared primitives for fleet management, security, and reliability to optimize data services at scale.
Design and operate the Agent SDK and MCP Gateway to enable Netflix engineers to build and deploy production-grade AI agents. Establish evaluation frameworks, tracing, and quality signals to ensure the reliability and performance of non-deterministic AI systems.
Design and build a scalable platform for large-scale machine learning model training, fine-tuning, and evaluation. Optimize systems for cost-effectiveness and reliability while creating intuitive APIs for ML practitioners.
Design and promote internal Python libraries to improve the development experience and foundational capabilities for Netflix engineers. Optimize the tech stack for scalability and performance while collaborating cross-functionally to provide a comprehensive software development lifecycle.
The engineer will design and build the platform supporting large-scale machine learning model training, fine-tuning, transformation, and evaluation workflows across the entire company. This includes co-designing and optimizing systems for scalability and cost-effectiveness, while creating easy-to-use APIs for ML practitioners and non-experts.
You will design, build, and operate robust, scalable distributed systems to support Netflix's global content production workflows. Additionally, you will mentor other engineers, drive technical strategy, and participate in on-call rotations to ensure operational excellence.
You will own the end-to-end developer experience for ML practitioners by designing and operating large-scale build, test, and CI/CD infrastructure. You will partner with researchers to identify workflow friction and implement tooling that improves engineering velocity and operational efficiency.
Lead the technical direction and architecture of a high-throughput, low-latency ad-serving platform. Focus on optimizing core execution paths, managing tail latencies, and building modular components for ranking, scoring, and auction mechanics.
Define and champion the overall testing strategy and quality roadmap for the Games Platform, focusing on performance, reliability, and certification. Build and maintain CI pipelines and validate platform behavior across multiple game engines and device targets.
Develop and maintain data and feature infrastructure for the AI Platform. Focus on pushing the boundaries of technology to support storytelling and global fandom.
Design and implement the user interface layer for Netflix's AI Platform to accelerate model creation, evaluation, and deployment. Collaborate with researchers and data scientists to build scalable ML infrastructure and gather requirements for various ML use cases.
Design and implement a self-serve user interface layer for Netflix's Machine Learning Platform to accelerate model creation and deployment. Collaborate with researchers and data scientists to evolve Metaflow and improve personalization model development.
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Design and build a next-generation ML data and feature platform to improve productivity for ML practitioners. This includes developing near-real-time feature computation engines and centralized feature and embedding stores for high-throughput training and low-latency inference.
Architect and build AI infrastructure and agentic workflows to transform software development and operational processes. This includes creating a centralized context layer and automating the full development lifecycle from code generation to incident triage.
Engineers will develop and expand compute infrastructure to support growing AI needs, enabling ML application in new business areas and driving innovation across Netflix. This role involves powering business-critical models and taking ML/AI initiatives to the next level through cross-functional partnerships.
The role involves designing and building a next-generation ML data and feature platform to significantly improve ML practitioner productivity, focusing on defining, testing, computing, storing, and serving features for training and inference. Responsibilities include developing near-real-time feature computation engines, managing serving infrastructure, and building centralized feature and embedding stores for sharing across ML domains.
Define and execute the long-term technical strategy for Platform Systems, focusing on reliability, scalability, and AI stack modernization. Lead and scale a high-performing engineering team while setting standards for observability, reliability, and developer productivity across the organization.
You will lead the Agent Platform team to define the technical strategy, roadmap, and execution environment for agentic systems at Netflix. This involves making critical build-versus-partner decisions and fostering a high-performing team to build durable, scalable platform primitives.
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