TLM, Integrity
Architect and build next-generation system protections through hands-on design, model training, and deployment strategies. Lead and manage a small, senior team of Engineers, providing clear direction and autonomy. Collaborate with Research, Safety, Product, and Policy teams to use existing tools and advance new solutions. Utilize state-of-the-art models to detect and prevent problematic content. Establish evaluation frameworks and metrics to measure progress and identify improvement areas. Support team growth and maintain high performance through mentorship and career guidance.
AI Field Engineer - AI Natives
AI Field Engineers at Fireworks build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. They architect inference foundations for customers whose core product is built on GenAI, size deployments to scale without infrastructure bottlenecks, run load tests, establish latency, throughput, and cost baselines, tune deployments, and deploy and validate new model families on inference frameworks while determining optimal configurations and serving patterns. They guide customers on model selection, fine-tuning strategy, and evaluation methodology, build and run fine-tuning pipelines with customers, design and implement evaluation frameworks measuring production-quality metrics, and lead structured discovery conversations to understand customer pain points and success criteria. They own the technical relationship from first engagement through production deployment, embedding with customer engineering teams to build trust, spend time on-site, translate customer pain points into product proposals, codify repeatable deployment patterns, and feed customer signals back into the product roadmap with specificity and urgency.
Senior Engineering Manager, Managed Platform Services
Lead the Command Center Insights & Actions team to build systems that translate raw infrastructure telemetry into human-readable diagnostics and automated remediation workflows. Own and execute a technical roadmap including alerting engines, heuristic development, node health systems, and state machines that trigger proactive maintenance without impacting customer workloads. Explore integration of Large Language Models to build AI solutions within the Command Center product. Drive the Insights & Actions roadmap covering alerting infrastructure, control plane APIs, automated action systems, and telemetry-derived insights such as straggler node detection and GPU profiling. Contribute to strategic roadmaps, refine early product requirements, collaborate cross-functionally with product, design, and engineering teams, manage complex multi-engineer projects focused on customer outcomes, drive technical excellence through process improvements and best practices, and cultivate team growth by coaching and mentoring engineers, setting clear performance expectations, and defining career paths to build a high-performing and sustainable team.
Engineering Manager, RLE
Build and scale reinforcement learning environments and platforms behind them; drive architecture for scalable, reliable, extensible environment systems and data generation pipelines; partner with Research, Product, and Ops teams to turn ambiguous needs into production systems; build modular, plug-and-play domains that integrate cleanly with training and evaluation loops; improve reliability, observability, performance, and data quality of systems.
AI Product Engineer
AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems quickly. They build POCs, MVPs, and production integrations, and engage in executive-level conversations about architecture, strategy, and business outcomes. Responsibilities include shipping code, running benchmarks, debugging production issues, and architecting deployments. They lead discovery conversations, align stakeholders, and translate customer pain points into product improvements. Engineers work on building end-to-end POCs and MVPs inside customer codebases and infrastructure, architect inference foundations for GenAI core products, run load tests and tune deployments, deploy and validate new model families on inference frameworks, guide customers on model selection and fine-tuning strategies, build and run fine-tuning pipelines, and design evaluation frameworks. They manage customer engagement by leading discovery conversations, owning technical relationships, embedding with customer teams on-site, identifying recurring pain points, proposing product improvements, and codifying deployment patterns for internal use and platform improvement.
Senior Product Engineer, Growth & Lifecycle Infrastructure - Music & Audio
Lead efforts to drive the design and development of customer-facing multi-modal machine learning inference systems. Work with the Platform and Inference teams on building inference systems for the next generation of models, focusing on optimization, model tuning, and deployment. Partner with leading cloud providers to deliver hosted Stability AI inference solutions. Serve as a strategic thought partner for leaders across the organization on driving business impact through machine learning. Contribute to bringing new Stability models and pipelines into existence. Prototype and productionize inference platform improvements and new features.
Software engineer, generative AI (UK)
Design and develop robust, secure, and scalable generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation; build and optimize high-performance, low-latency APIs and microservices to integrate advanced AI models and sophisticated agentic workflows into the core platform; make meaningful system design decisions and own the architecture of core platform components from initial proposal through production deployment; implement and maintain responsive user interfaces using technologies like React and TypeScript; clearly communicate changes, plans, and proposals to cross-functional teams and collaborate with product managers, data scientists, and DevOps engineers; partner with DevOps teams to build continuous deployment, logging, and monitoring systems to ensure top-tier performance, security, and reliability across distributed workloads.
Host Systems Software Engineer
The Host Systems Software Engineer is responsible for designing, implementing, and debugging host-side systems software for AI infrastructure, including Linux kernel drivers and supporting userspace components. They build and optimize software paths for high-throughput, low-latency communication such as RDMA and related networking functionality, and develop software related to PCIe, DMA, NICs, accelerators, memory movement, and device interaction. The role involves bringing up new hardware platforms, diagnosing complex issues across kernel, firmware, networking, and hardware boundaries, and building tooling for integration, testing, diagnostics, observability, qualification, and performance characterization. Collaboration with hardware, networking, and platform teams to define interfaces and integrate new capabilities is essential, as is working with external vendors to integrate technologies and resolve issues. The engineer contributes across the systems software stack as the platform and team evolve and helps shape the technical direction and engineering practices for the growing systems software stack.
Software Engineer, ML Data Infrastructure
The Software Engineer, ML Data Infrastructure will collaborate with engineers to build advanced AI design experiences, tackle complex technical challenges including scaling distributed systems and enabling generative media experiences, build robust data infrastructure at petabyte scale ensuring reliability and performance across multi-modal training pipelines, optimize data processing workflows for high throughput involving distributed systems, TPU infrastructure, and large-scale storage, and partner with research scientists to understand data requirements and translate them into production-grade systems to accelerate model development cycles.
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.
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