Fireworks AI is seeking a Member of Technical Staff, Evals & Post-Training Product to build products and workflows that integrate evaluation and post-training. This role involves developing internal eval tooling, owning fine-tuning product experiences, and collaborating with users to identify needs and productize solutions. The position requires software engineering experience, hands-on LLM evaluation/post-training experience, product engineering skills, and an understanding of the GenAI lifecycle.

AI Product Manager focused on customer success and translating technical requirements into product offerings for generative AI infrastructure, specifically LLM inference and fine-tuning.

Software Engineer focused on Performance Optimization for AI infrastructure, optimizing speed and efficiency across the stack for LLMs, VLMs, and video models. Responsibilities include low-level GPU kernel optimization, distributed systems scaling, and performance analysis for training and inference.

Research role focused on advancing LLMs and multimodal systems through foundational research, enhancing model efficiency, accuracy, and scalability for generative AI infrastructure. The role involves designing, implementing, and evaluating novel architectures and training methods, with a focus on transitioning research into production.

Associate Product Manager role at Fireworks AI, a Series C company focused on generative AI infrastructure. The role involves working on the frontier of AI infrastructure, building tools for developers and AI teams. Responsibilities include acting as a technical advisor to customers, partnering with engineering teams to define and ship product features, and engaging with customers to identify use cases. The role is designed for early-career candidates with a technical background and curiosity about AI/LLMs.

Solutions Architect role focused on customer engagement, technical sales, and solution design for generative AI infrastructure, specifically LLM inference and fine-tuning. The role involves understanding customer needs, designing AI solutions using the Fireworks platform, executing Proofs of Concept (POCs), and providing performance engineering and model recommendations. It requires strong technical depth in the LLM stack and customer-facing skills, with two tracks: Enterprise SA and Applied AI SA.

Software Engineer on the AI Infrastructure team at Fireworks AI, focusing on designing and building core systems for their generative AI platform, including infrastructure for distributed training, inference, data pipelines, CI/CD, control plane, and model serving. The role emphasizes reliability, performance, and quality of the AI system, bridging customer needs with the inference engine.

Applied Machine Learning Engineer role focused on developing, fine-tuning, and operationalizing ML models for customer applications and internal platform features. Involves customer collaboration, PoCs, application building, model enablement, and performance optimization.

The role focuses on designing, building, and optimizing the infrastructure for large-scale model training operations, including distributed training pipelines, performance optimization, and data storage solutions for LLMs and multimodal models.

This role is for a backend software engineer focused on building the core infrastructure for a generative AI platform, including web applications, model orchestration, billing, APIs, and developer tooling. The role emphasizes platform engineering with product impact, working closely with various teams to ship end-to-end features and improve system reliability and performance. Experience with AI systems and a desire to build products in the AI space are required.

Software Engineer on the Cloud Infrastructure team responsible for architecting and building foundational systems for a generative AI platform, focusing on serving AI workloads globally with high reliability, efficiency, and scalability. The role requires deep expertise in distributed systems, cloud-native infrastructure, and ML platforms, with responsibilities including designing and implementing backend services, optimizing infrastructure, and collaborating with ML and product teams.