Top Senior Platform Engineer Jobs in NYC, NY

Hands-on technical lead designing, building, and optimizing high-performance Linux compute platforms for low-latency electronic trading. Responsibilities include kernel- and hardware-level performance tuning, containerization, IaC and CI/CD design, automation and tooling development, vendor interfacing, AI-assisted engineering governance, and mentoring cross-functional engineering teams to deliver reliable, low-latency infrastructure.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Own and evolve GitLab’s authorization systems across its Ruby on Rails monolith and next-generation Rust policy engine. Design fine-grained permissions for users, tokens, roles, and AI agents; secure GraphQL and REST APIs; lead feature-flagged rollouts and migrations; improve performance and reliability; and collaborate across authentication, platform, AI, and modular-service teams in a distributed asynchronous environment.

This role involves leading the development of AI-powered products, optimizing large scale AI systems, and mentoring cross-functional teams. It focuses on designing and deploying AI components, contributing to the technical vision, and applying cutting-edge AI techniques.

Lead cross-functional teams to develop and deploy AI-powered products, improve AI system performance, and create innovative AI solutions at Capital One.

Build and operate a hyper-scale data lake and analytics platform: develop Java microservices and Spark/Scala/Flink pipelines, own a new graph database, design ultra-high-scale data platforms, optimize performance and latency, enable efficient querying for analytics and ML, and collaborate on feature design and stability across teams.

Design, develop, deploy, and support scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for latency, throughput, cost, scalability, and hardware utilization. Contribute to foundational AI strategy, collaborate cross-functionally, and lead or mentor engineering teams.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineers, scientists, technical program managers, and product managers while contributing to technical vision, roadmap development, and responsible AI practices. Lead and mentor engineering teams and apply emerging AI research to production systems.

Lead critical infrastructure platform initiatives enabling engineering teams to build, deploy, and operate products consistently. Responsibilities include developing multi-account and multi-region Kubernetes clusters, MLOps capabilities, standardized deployment systems, and company-wide authentication. The role also involves automating engineering workflows, improving developer experience, prioritizing shared platform initiatives with stakeholders, driving platform adoption, and mentoring team members.

Lead architecture and implementation of reliability improvements across CrowdStrike's cloud-native platform. Build shared libraries and services, drive observability and SLO practices, perform performance and cost optimization, run resilience engineering and chaos experiments, automate infrastructure-as-code, mentor engineers, and embed with product teams to deliver scalable, highly reliable distributed systems at organizational scale.

Lead enterprise deployment, governance, and operations for Microsoft Copilot and Power Platform across M365. Manage tenant controls, licensing/credit governance, AI risk/compliance, Microsoft vendor relationships, SharePoint/OneDrive content readiness, and analytics/reporting. Partner with architects, Security, Legal, Procurement, and business stakeholders to ensure secure, compliant, and adopted AI platform operations.

Design, build, and operate internal developer platforms, CI/CD pipelines, developer environments, deployment workflows, and shared services. Create automation and custom tooling, prototype and productionize AI-assisted engineering workflows, strengthen cloud and container foundations (Kubernetes, IaC), instrument platforms with telemetry, and own platform projects end-to-end including rollout, adoption, and on-call.

Build and operate foundational cloud infrastructure, Kubernetes platforms, CI/CD systems, and internal developer tooling. Deliver platform projects, improve developer experience, optimize cloud costs, and enhance reliability through observability, automation, and incident response. Partner with engineering, security, and infrastructure teams, evolve GitOps workflows, support high-availability systems, participate in on-call rotations, and mentor junior engineers.

Lead design, build, and operate scalable GenAI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Collaborate cross-functionally, invent LLM optimization techniques, and shape long-term technical vision for foundational AI systems.

Lead enterprise-scale migrations to GitHub Enterprise Cloud, including repositories, users, access controls, workflows, Enterprise Managed Users, and data residency. Build governed identity and access environments, validate integrations, and document handoffs. Design and maintain scalable internal developer platforms, standardize tooling, and develop self-service infrastructure and CI/CD capabilities. Partner with client IT teams, mentor engineers, and apply cloud-native architecture, Kubernetes, automation, and Infrastructure as Code practices.

Operate and scale a high-throughput Kafka streaming platform: investigate incidents, optimize performance, integrate open-source components, develop control and data plane tooling, tweak low-level client behavior, drive complex projects, and mentor junior engineers.

The role involves designing, developing, and supporting AI software components, leveraging various AI technologies, and optimizing performance for large-scale AI systems.

Build and operate the multi-tenant platform layer for a GPU cloud service. Responsibilities include Kubernetes-based orchestration, tenant isolation, platform APIs and tools, GPU cluster automation, infrastructure as code, reliability and observability partnerships, customer issue support, environment templates, rollout tooling, architecture reviews, and reducing operational toil.

Lead frontend development for Coinbase identity experiences including login, signup, OAuth, account security, and multi-factor authentication. Own projects from design through deployment and monitoring, guide Native versus Webview architecture, author technical designs, resolve systemic technical debt, lead on-call and incident response, and mentor engineers.

Own and implement platform initiatives to improve scalability, reliability, and developer experience. Automate infrastructure with IaC, operate Kubernetes and CI/CD pipelines, build developer tools, monitor platform resilience, participate in incident response, and influence architecture and processes as you grow.

Own and lead the technical direction of a major codebase section while designing, building, testing, documenting, and maintaining secure, scalable API-based decisioning products. Partner with product, engineering, and security teams on agile delivery, troubleshoot production issues, meet SLA requirements, and participate in an on-call rotation.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Manage and improve enterprise applications and cloud infrastructure on GCP. Design Terraform-based infrastructure, automated CI/CD pipelines, upgrade scripts, and internal developer platforms. Lead incident management, root cause analysis, on-call support, performance optimization, and resource rightsizing while establishing infrastructure-as-code and security standards. Collaborate with engineering teams to improve deployment reliability and productivity.

Build, operate, and optimize large-scale healthcare data pipelines and foundational data products. Transform claims, EHR, and reference datasets into reliable, performant assets for analytics, customer applications, and AI/ML use cases. Improve data quality, observability, scalability, cost efficiency, and pipeline performance while debugging production issues. Collaborate across engineering, product, and data teams, contribute to architecture and technical standards, mentor peers, and provide rotational production support.

Build and maintain the foundational GCP and Kubernetes platform supporting data and agentic AI systems. Develop well-tested applications in Python, TypeScript, and Golang; manage GKE deployments, GitOps workflows, infrastructure as code, databases, migrations, and secure application delivery. Partner with product engineers, optimize scaling and LLM-assisted coding workflows, and validate system correctness and security. The role is based in New York City and requires in-office work four days per week.