Fireworks AI currently has 10 active AI-related job listings. The majority of these roles, 60%, are focused on serving infrastructure. The top function for hiring is Engineering, with 7 positions. Frequent tech tags include model serving, inference infrastructure, and fine-tuning. Fireworks AI posted 1 new AI role in the last 30 days.
Currently tracking 10 active AI roles, up 17% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $170k–$240k (avg $202k).
Fireworks AI currently has 14 active AI-related roles in our index. The most common open titles are: Software Engineer, AI Infrastructure, AI Field Engineer - AI Natives, AI Field Engineer - Enterprise, AI Field Engineer - Microsoft Foundry, Applied Machine Learning Engineer. Most positions are in Engineering and Product.
Fireworks AI's active AI hiring is concentrated in: serving infrastructure (71%), application (7%), evaluation (7%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Fireworks AI is hiring AI talent in: United States (14 roles).
Job postings at Fireworks AI most frequently reference: model serving, inference infra, fine tuning, multimodal, evals.
In the past 30 days, Fireworks AI has posted 6 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| AI Field Engineer - Enterprise AI Field Engineer role focused on embedding with enterprise customers to build and deploy generative AI solutions, architecting inference foundations, guiding fine-tuning strategies, and translating customer needs into product improvements. Requires strong Python, Kubernetes, cloud infrastructure, and LLM stack knowledge (inference, fine-tuning, evaluation). | ServePost-train | 9 |
| AI Field Engineer - Microsoft Foundry AI Field Engineer for Microsoft Foundry, focusing on making Fireworks the default inference and fine-tuning layer in Azure AI architectures. Responsibilities include building reference architectures, running benchmarks, debugging integrations, co-developing POCs, guiding customers on model selection and fine-tuning, and owning the feedback loop between partners and product development. Requires strong Python, Kubernetes, LLM inference, fine-tuning, and Azure AI stack experience. | ServePost-train | 9 |
| AI Field Engineer - AI Natives AI Field Engineer role focused on embedding with AI-native customers to build production GenAI systems, architect inference foundations, deploy models, guide fine-tuning strategies, and provide product feedback. Requires strong hands-on engineering, customer-facing skills, and experience with inference and fine-tuning pipelines. | ServePost-train | 9 |
| Member of Technical Staff, Performance Optimization 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. | ServePost-train | 9 |
| Member of Technical Staff This role focuses on designing, developing, and maintaining large-scale backend and cloud-native infrastructure for a generative AI platform, specifically supporting distributed machine learning training, inference, and data processing pipelines. The goal is to achieve fast and scalable inference, with a focus on efficiency and low latency. | Serve | 8 |
| AI Product Engineer AI Product Engineer role focused on building the product surfaces and developer experience for a generative AI infrastructure platform. The role involves end-to-end ownership of features, working across the full stack (frontend, backend, APIs, data), and collaborating with various teams to translate platform capabilities into user-facing products. The company emphasizes fast iteration, high autonomy, and building at enormous scale. | ShipServe | 8 |
| Solutions Architect 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. | ServePost-train | 8 |
| Software Engineer, AI Infrastructure 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. | Serve | 8 |
| Applied Machine Learning Engineer 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. | ShipPost-train | 8 |
| Member of Technical Staff, AI Training Infrastructure 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. | Data | 8 |
| Member of Technical Staff, Software Engineer 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. | Serve | 7 |
| Member of Technical Staff, Cloud Infrastructure 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. | Serve | 7 |