TLM, Embedded Experiences
Lead the technical direction, architecture, and execution of critical Cooperative Systems initiatives. Manage and mentor a team of engineers while maintaining meaningful hands-on technical involvement. Partner closely with stakeholders across Support, Operations, Finance, IT, Sales, Legal, and other functions to identify opportunities for AI-driven improvements. Design and build production systems that leverage large language models and other AI technologies. Drive engineering excellence through strong technical decision-making, code quality, operational rigor, and thoughtful system design. Balance rapid experimentation with long-term platform investments. Establish technical roadmaps and execution plans for projects spanning multiple teams. Coach engineers through technical challenges, career growth, and project execution. Help shape the culture, processes, and engineering practices of a growing organization.
Software Engineer, Agents & Automations
Design, build, ship, and maintain core capabilities for North’s Agents & Automations platform. Build product and platform features that help users create, run, debug, evaluate, and improve agents and automations. Own features end-to-end, from technical design through implementation, testing, launch, and iteration. Work across the stack, from frontend product surfaces to backend systems, depending on what the product needs. Make practical technical decisions that balance speed, quality, depth, and user impact. Collaborate closely with product, design, modelling, customer-facing teams, and other engineers to define the right outcomes and ship measurable improvements. Use AI actively in your work, while staying intellectually engaged and accountable for the quality and reliability of what you ship.
Software Engineer, Knowledge Systems
As a Software Engineer on Knowledge Systems, you will help build systems that understand what is true about the world by extracting, connecting, retrieving, and reasoning over knowledge from the web and beyond to enable AI agents to answer questions with unprecedented precision and completeness.
VP of Engineering
Lead the design and evolution of the AI cloud platform including GPU orchestration, compute scheduling, networking, storage, and distributed systems. Make critical decisions regarding cloud infrastructure, bare-metal deployments, and platform scalability. Participate personally in architecture reviews and key technical initiatives. Build and scale large GPU clusters supporting customer workloads and design systems for GPU provisioning, scheduling, utilization optimization, and capacity management. Drive platform reliability and performance for AI training and inference workloads, partnering closely with engineering teams on infrastructure requirements for next-generation AI systems. Remain deeply involved in engineering decisions and technical direction, contribute directly to infrastructure design and implementation efforts, review architecture proposals, system designs, and major infrastructure changes, and act as the technical escalation point for complex infrastructure challenges. Establish best practices for Kubernetes, observability, CI/CD, security, and operational excellence. Build SRE and Platform Engineering functions from the ground up. Define reliability standards including SLOs, SLIs, incident response processes, and capacity planning. Drive automation across infrastructure operations. Recruit and develop Infrastructure, Platform, and SRE teams. Build a high-performance engineering culture focused on ownership and execution. Partner with executive leadership on company strategy and infrastructure investments. Manage infrastructure budgets, vendor relationships, and capacity planning.
Operations Program Manager (Computer Vision), Public Sector
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment. You will build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability. You will manage the technical lifecycle within diverse regulatory frameworks. You will lead the response for production issues in mission-critical environments, ensuring rapid resolution and building guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials. You will also partner with Engineering and ML teams to ensure lessons learned in the field influence the technical architecture and decisions of future use cases.
Systems Research Engineer Intern - GPU Programming (Fall 2026)
Participate in on-call rotation (Pagerduty) to respond to production incidents. Build and run infrastructure with Ansible, Terraform, and Kubernetes to enable scaling to a large number of concurrent users. Build monitoring systems to ensure the highest quality service for customers. Design and implement operational processes such as deployments and upgrades. Debug production issues across all services and levels of the stack. Identify improvements for the product architecture from the perspectives of reliability, performance, and availability. Plan the growth of Together AI's infrastructure.
Research Intern, Inference (Fall 2026)
As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.
Frontier Agents Intern (Fall 2026)
As an AI Infrastructure Engineer at Together AI, the responsibilities include participating in on-call rotation (Pagerduty) to respond to production incidents; building and running infrastructure with Ansible, Terraform, and Kubernetes to enable scaling for a massive number of concurrent users; building monitoring systems to ensure the highest quality service for customers; designing and implementing operational processes such as deployments and upgrades; debugging production issues across all services and levels of the stack; identifying improvements for the product architecture from reliability, performance, and availability perspectives; and planning the growth of Together AI's infrastructure.
Agentic Product Analyst
As an Agentic Product Analyst at Netomi, you will be responsible for designing, architecting, and deploying large-scale Agentic AI solutions for enterprise customers. This includes leading discovery sessions with customers to understand business processes, identifying automation opportunities, and designing agentic orchestration strategies using Netomi's AI platform. You will build detailed solution blueprints covering workflows, data exchanges, escalation logic, analytics, and agent lifecycle design. Defining end-to-end Agentic AI architectures and working with customer technical teams to map integration dependencies are also key tasks. You will own the creation of integration design documents, support Integration Engineers during implementation, and ensure agent workflows comply with enterprise standards. Collaboration with Product & Engineering to translate requirements into features, serving as product owner during deployment, validating solution behavior with QA, conducting user-experience reviews, training customer teams, and ensuring projects deliver on time with measurable impact also fall under your responsibilities. Additionally, you are expected to act as a trusted advisor to customer stakeholders, present architectural recommendations, drive continuous improvement, and maintain deep expertise in agentic AI, LLMs, workflow orchestration, and enterprise systems.
Senior Backend Engineer- AI Agents (Remote)
Design and build scalable backend systems powering AI Agents that operate in real-time enterprise environments. Develop agent orchestration frameworks involving multi-step reasoning, tool usage, and decisioning workflows. Build systems for agent memory, context management, and state persistence across interactions. Architect low-latency inference pipelines integrating Large Language Models, Small Language Models, and external tools/services. Implement evaluation frameworks to measure agent performance, accuracy, and reliability. Enable continuous improvement loops for AI agents in production including feedback, retraining, and deployment. Design and manage event-driven, asynchronous workflows for complex agent tasks. Optimize systems for high throughput, low latency, and cost-efficient inference at scale. Build and maintain robust APIs and service layers (REST/gRPC) for agent capabilities. Partner closely with Applied AI/ML teams to productionize models and agent behaviors. Collaborate with Product and Solutions teams to translate real customer workflows into agentic systems. Drive best practices in observability, monitoring, safety, and guardrails for AI systems. Contribute to architecture decisions for scaling multi-tenant, enterprise-grade AI platforms.
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