Top Senior Platform Engineer Jobs in NYC, NY

Design, build, operate, and scale GitLab Orbit backend services in Rust across distributed cloud-native environments. Improve Kubernetes deployments, infrastructure automation, observability, reliability, incident readiness, multi-tenant isolation, data workflows, graph query capabilities, indexing pipelines, cloud storage integrations, and API/MCP surfaces. Own technical designs through rollout while collaborating asynchronously with infrastructure, security, AI, data, delivery, and SRE teams.

Operate and evolve a Kubernetes/CNCF platform, own multi-cloud (Azure/AWS/GCP) ops, implement IaC (Pulumi), manage regional data-residency architecture, improve reliability and cost via SLOs, observability, autoscaling, and lead on-call and incident response while partnering with SRE/Foundation teams.

Design, implement, optimize, and maintain scalable hybrid multi-cloud Kubernetes platforms operating at massive scale. Integrate open-source technologies, improve platform reliability, provide technical direction, mentor engineers, and participate in on-call support. The role requires expertise managing large Kubernetes clusters, observability tools, Linux environments, public clouds, custom data centers, and AI-enabled workflow improvements.

Own and evolve AWS cloud infrastructure supporting a financial and hospitality technology platform. Design highly available systems, deployment pipelines, observability, security controls, and infrastructure migrations. Lead incident response, reliability planning, capacity management, and root-cause analysis while participating in on-call rotations. Partner with application engineers, establish infrastructure standards, and mentor platform engineers as the organization scales.

Lead design, build, and operate high-performance Linux platforms for low-latency electronic trading. Perform deep performance engineering (kernel, CPU/NUMA, networking), write automation and tooling, architect deployment models (containerization, immutable infra), leverage Agentic AI, interface with vendors, mentor teams, and ensure governance, reliability, and reusable solutions across businesses.

Design, implement, and maintain scalable hybrid multi-cloud Kubernetes platforms at massive scale. Ensure high reliability, integrate open-source observability tools, provide technical direction, operate large Linux environments across cloud and data centers, handle on-call duties, and mentor junior engineers.

Own and improve Doctronic’s platform engineering systems across AWS, including production reliability, infrastructure as code, CI/CD, developer environments, observability, security, cost efficiency, and incident response. Build reusable infrastructure, service templates, automation, and agent-ready workflows that enable engineering teams and coding agents to safely deliver software with greater speed and autonomy.

Build and operate secure, reliable, scalable platform infrastructure across AWS, PostgreSQL, ECS, CI/CD, and observability systems. Lead security and reliability initiatives, incident response, engineering tooling, and non-functional standards. Develop AI platform capabilities, including LLM development tools, guardrails, and infrastructure for AI-powered product features. Collaborate cross-functionally to understand organizational needs and improve developer productivity.

Lead the design, development, deployment, and support of scalable AI platform components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize AI training and inference for latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to technical strategy and mentoring stakeholders.

Lead the design, development, deployment, and support of foundational AI systems, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Drive build-versus-buy decisions, optimize production AI systems for scalability, cost, latency, and throughput, and shape long-term technical roadmaps. Manage, mentor, and grow an engineering team while partnering with scientists, product managers, and technical program managers.

Build and support production AI platform components including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, cost, and hardware utilization. Partner with engineering, research, product, and program teams; contribute to technical vision and roadmap; and lead or mentor engineering teams.

Leads cross-functional technical projects and owns, scales, and improves platform and business operations systems. Builds production features, evaluates technologies, designs scalable architectures, enhances automation and analytics, collaborates with customers and internal stakeholders, and drives adoption and operational outcomes in a fast-paced SaaS environment.

Administer multi-tenant Kubernetes platforms, manage application deployments, troubleshoot incidents, and ensure system reliability while collaborating with development teams.

Build and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, experimentation, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to technical strategy and roadmap, collaborate cross-functionally, and lead or mentor engineering teams while applying emerging AI research in production.

Own the architecture and technical direction of Metropolis Recognition Platform, spanning edge ingestion, identity resolution, real-time decisioning, notifications, and operator tools. Design scalable streaming pipelines, rules systems, privacy and consent models, observability, testing, and progressive delivery. Lead RFCs, cross-functional alignment, architectural reviews, mentorship, and production quality across application, computer vision, data, security, platform, and partner teams.

Own the multi-year technical strategy and architecture for Coinbase’s enterprise IAM platform spanning human, AI agent, service, and workload identities. Drive adoption across 700+ systems, establish authentication and authorization standards, lead migrations and build-versus-buy decisions, automate audit and regulatory controls, reduce standing privilege, and mentor senior engineering talent. Partner across Security, Compliance, IT, Infrastructure, and product teams while influencing technical direction without direct authority.

Build and evolve core platform components, including data modeling, integration testing, release tooling, internal developer tools, and reliable services. Architect systems, lead initiatives, mentor engineers, partner across teams, and improve scalability, performance, fault tolerance, observability, and production reliability. The role also involves developing AI-enabled developer tools and independently driving complex projects in a fast-paced startup environment.

Design, build, and operate high-throughput data pipelines supporting billing and cost attribution. Drive Spark optimization, latency reduction, sharding, cross-datacenter reliability, and new billing capabilities. Investigate billing accuracy issues with Finance and Product teams, participate in on-call rotation, and improve engineering standards and operational excellence.

Own the taxonomy and attribute platform end to end, including LLM extraction, dbt transformations, ClickHouse data models, Dagster orchestration, full-stack applications, APIs, CI/CD, data quality, observability, and reliable write workflows. Architect AWS infrastructure with Terraform, build AI evaluation and prompt-versioning systems, manage cost and latency, and establish engineering standards, testing, documentation, and operational runbooks.

Design, develop, deploy, and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to long-term platform strategy, apply advanced AI research in production, and collaborate with engineers, scientists, program managers, and product leaders while mentoring teams.

The Senior Data Platform Engineer will design, maintain, and enhance Hinge's data platform, including data pipelines, monitoring systems, and user feedback incorporation while ensuring data quality and system reliability.

Install, operate, and lifecycle-manage bare-metal OpenShift and Kubernetes platforms. Build Terraform and Ansible infrastructure-as-code provisioning, support platform services and virtualization, implement STIG and cybersecurity compliance, monitor performance, and document architecture and operations. The role also supports OCONUS hardware bootstrap activities and transitions manual provisioning to an IaC-managed model.

Leads application support and platform enablement across AWS Commercial and GovCloud environments. Owns incident response, root-cause analysis, remediation, and production reliability for OpenShift and EKS workloads. Builds Terraform modules and GitLab templates for developer self-service, CI/CD, security scanning, observability, and compliance. Operates AWS infrastructure, Kubernetes platforms, and monitoring tools while enforcing least-privilege access, STIG, CIS, FedRAMP, and DoD requirements. Provides technical leadership, architecture guidance, mentoring, and cross-functional coordination.

Build and own core platform infrastructure, including Terraform, cloud environments, CI/CD pipelines, developer tooling, observability, monitoring, alerting, secrets management, and deployment abstractions. Partner with product engineers to improve delivery speed and reliability, support GCP messaging infrastructure, develop compliance tooling, improve cost visibility, and independently operate production systems in a regulated fintech environment.

Design and evolve reliable edge computing systems connecting cameras, sensors, deployed hardware, and cloud infrastructure. Responsibilities include architecture, distributed systems, diagnostic tooling, remote troubleshooting, sensor-fusion problems, code reviews, technical design discussions, and mentoring engineers. The role requires hands-on systems expertise across Linux, networking, hardware, and software, with ownership of ambiguous technical initiatives from conception through delivery.