Full Stack Product Engineer
As a Full-Stack Product Engineer at Ideogram, you will build products that bring generative AI directly to creators, working across the entire technology stack from designing user experiences to optimizing backend systems that serve millions. Your focus will be on shipping features that users love by combining product intuition, strong ownership, and user empathy. You will design APIs and data models to support evolving product needs, utilize AI-native engineering tools to speed up development, debugging, and understanding of the codebase, and work effectively across frontend and backend systems. You will also be responsible for explaining technical concepts to both technical and non-technical stakeholders, participating in constructive code reviews, collaborating with the team, and taking full responsibility for the outcomes of your work, not just the code.
Solutions Architect (APAC)
The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They also design multi-agent systems using different patterns, implement agent logic with modern frameworks (langchain/langgraph), create evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. Additionally, they lead technical maturity assessments, work directly with enterprise customers to understand requirements and offer recommendations, and collaborate with Engagement Managers and Product/Engineering teams.
Senior Engineering Manager, Management Plane Systems
Lead the team responsible for the automation, observability, configuration management, and policy enforcement layer that runs across the entire network fleet. Own the architecture, development, and production operation of the SDN Management Plane, including the automation and observability platform for managing network fleet across all regions. Build and operate CI/CD pipelines for network configuration, including automated testing, policy validation, and push-on-green delivery of network changes. Design and implement software systems that enforce reconciliation between declared and actual network state, detect configuration drift, and trigger automated remediation workflows. Define provisioning and onboarding automation for new nodes, regions, and customer environments. Drive the design of network observability systems such as streaming telemetry, synthetic probing, anomaly detection, and real-time traffic monitoring across GPU clusters. Design and implement self-healing network capabilities using closed-loop automation to detect, diagnose, and resolve network faults without human intervention. Set the technical vision for applying GenAI and machine learning to network operations. Partner with Control Plane and Data Plane teams to ensure software interfaces between layers and collaborate with infrastructure and compute teams to support GPU cluster networking requirements. Act as internal platform owner for network automation and treat engineering teams as customers with real product requirements. Lead, mentor, and grow a team of senior and staff-level software and network automation engineers, set technical standards, review architecture and design decisions, and own team performance and development. Foster a high-ownership engineering culture focused on shipping production software.
Manager, Infrastructure Strategy & Operations
As an AI Infrastructure Engineer at Together, you are responsible for keeping all user-facing services and production systems running smoothly. You participate in on-call rotation (Pagerduty) to respond to production incidents. You build and run infrastructure with Ansible, Terraform, and Kubernetes to enable scaling to a massive number of concurrent users. You build monitoring systems to ensure the highest quality service for customers. You design and implement operational processes such as deployments and upgrades. You debug production issues across all services and levels of the stack. You identify improvements for the product architecture from the reliability, performance, and availability perspectives. You plan the growth of Together AI's infrastructure.
Trust Engineer
Own the implementation and optimization of Harvey's compliance automation tooling to automate workflows across compliance programs; design and build a compliance data layer in Snowflake by ingesting signals from infrastructure, security tools, and SaaS platforms to create a real-time view of control health and audit readiness; develop AI agents and automated pipelines for evidence collection, control testing, and continuous monitoring at scale; partner with Engineering and Security to map technical implementations to compliance controls and maintain a living, accurate control inventory; build reporting layers that translate compliance signals into clear narratives on risk posture and certification status for executive and cross-functional audiences.
Full Stack Engineer, AI systems
Build end-to-end product features across frontend, backend, and AI integrations; design agent workflows that handle planning, tool use, failure, and recovery across multiple steps; integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions; design real-time AI interactions with streaming, partial results, and tight latency constraints; improve system reliability, observability, and fallback mechanisms; collaborate closely with ML, backend, and product teams to ship features end-to-end; continuously iterate based on real usage and failure modes.
Backend Engineer, AI (Agent Systems)
As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. You build and operate backend systems that serve AI-powered features in production, design inference pipelines, orchestration layers, and service boundaries around models. You are responsible for production concerns such as monitoring, logging, alerting, and incident response. Additionally, you optimize latency and throughput across inference, caching, batching, and streaming. Your work enables backend systems to run reliably at scale, handling production AI traffic with low latency and high throughput, ensuring APIs are stable, clear, and support seamless integration with frontend and ML systems. You ensure production incidents are quickly detected, diagnosed, and resolved, minimizing user impact, and continuously improve system performance and reliability through iterative changes based on real usage.
Lead/Manager Together Cloud Infrastructure Engineer
As an AI Infrastructure Engineer at Together, you are responsible for keeping all user-facing services and production systems running smoothly. You participate in on-call rotation to respond to production incidents, build and run infrastructure using Ansible, Terraform, and Kubernetes to enable scaling to a massive 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 product architecture from reliability, performance, and availability perspectives, and plan the growth of Together AI's infrastructure.
Director, Technical Program Manager
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure 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, 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, build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead incident response for production issues in mission-critical environments ensuring rapid resolution and prevention measures, translate deep technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to incorporate field lessons into future technical architecture and decisions.
Software Engineer, Monetization ML Infrastructure
Design and build the machine learning infrastructure that powers OpenAI's monetization and ads systems. Develop large-scale data pipelines processing impressions, clicks, conversions, advertiser data, marketplace signals, and other inputs used to train and improve ML models. Create scalable model training platforms for ranking, conversion prediction, quality prediction, bidding, targeting, measurement, and optimization workloads. Develop systems to safely and reliably move models from experimentation into production environments. Build and improve real-time inference and serving infrastructure with strict requirements for latency, throughput, reliability, and availability. Design experimentation frameworks enabling A/B testing, holdouts, model comparisons, ramping strategies, and measurement at scale. Improve platform performance by optimizing training efficiency, inference latency, model throughput, infrastructure reliability, and cost effectiveness. Collaborate closely with ML engineers, product engineers, data scientists, and monetization teams to accelerate development and deployment of advertising systems.
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