Software Gigs

Staff Software Engineer role designing and implementing cloud-native platform components and services, with primary focus on Kubernetes workloads, distributed systems, and infrastructure automation. Requires 5+ years hands-on Kubernetes experience, proficiency in Go or systems languages, and demonstrated expertise building operators, controllers, and platform services. Will lead well-scoped projects from design through production, contribute to reliability and scalability, mentor junior engineers, and participate in on-call operations.

Design and implement Infrastructure as Code to automate provisioning, monitoring, and lifecycle management of NoSQL, Streaming, and Caching platforms (Cassandra, Aerospike, Kafka, Redis) across AWS and GCP. Build highly available, self-healing systems with automated failover and scaling, develop comprehensive observability solutions, and lead incident response for critical data platform issues. Drive automation-first practices and apply AI/ML approaches such as anomaly detection and predictive scaling to enhance reliability and reduce manual toil. Partner with engineering and platform teams to ensure resilient infrastructure supporting billions of transactions and millions of players globally.

As a Staff Software Engineer in Data Platform Engineering, you will design, build, and operate cloud-native platform components and services, taking features from design through production. You'll lead well-scoped technical projects, collaborate with senior engineers on architecture alignment, and ensure reliability and scalability through testing and instrumentation. You'll spend most of your time hands-on building operators, controllers, automation, and platform services that other teams depend on, while mentoring junior engineers. The role requires 8+ years of software development experience (or equivalent with advanced degrees), 5+ years of hands-on Kubernetes experience, and proficiency with at least one major hyperscaler (AWS, Azure, GCP).

Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers. Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments. The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.

Lead a small embedded Linux software team building mission-critical aerial systems and autonomous platforms for defense applications. You'll split your time between team management (standups, architecture reviews, task prioritization) and hands-on C/C++ development of Linux daemons, REST APIs, and system services. Design for real-time performance, security hardening, and field operability while collaborating across autonomy, avionics, mechanical, and electrical teams. Own the full software lifecycle from architecture and CI/CD pipelines to documentation and field diagnostics.

Own and evolve infrastructure, deployment systems, and cloud environments powering Clinically AI's healthcare AI platform. Design and maintain GCP infrastructure with focus on GKE, Kubernetes, and secure multi-environment deployments; build Infrastructure-as-Code using Terraform and Helm charts; optimize CI/CD pipelines with GitHub Actions; implement observability, security best practices, and incident response. Work closely with Backend, AI, and Product teams to support scalable infrastructure for services, AI pipelines, and high-throughput workloads.

Own and evolve the cloud infrastructure, deployment systems, and CI/CD pipelines powering Clinically AI's healthcare platform on GCP. Design and maintain cloud infrastructure using GKE, Terraform, and Helm; build reliable CI/CD pipelines with GitHub Actions; implement observability, security best practices, and incident response processes. Work closely with Backend, AI, and Product teams to support scalable infrastructure for AI pipelines, real-time processing, and multi-environment deployments. Operate with significant autonomy across cloud-native architecture while optimizing for performance, reliability, cost, and compliance.