Sr. Manager, Integrated Campaigns and ABX
Build and deploy AI Agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Set up telephony systems including SIP/CCaaS/PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Write and refine prompts for LLM-driven agents, monitor performance, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions and work consultatively to unblock challenges in security, connectivity, or knowledge ingestion. Collaborate with product and engineering teams to address platform gaps and resolve technical issues, independently driving leading client implementations.
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.
Full Stack Software Engineer, Codex
Build end-to-end product experiences that span frontend applications, backend services, agent workflows, cloud infrastructure, and developer tooling. Design AI-powered workflows that generalize across a wide variety of software engineering teams, languages, codebases, and development practices. Discover and implement novel ways to apply AI to eliminate friction throughout the software development lifecycle. Partner closely with product, design, and research to understand developer needs and rapidly translate insights into shipped product improvements. Work directly with users—including developers at OpenAI, open-source contributors, startups, and large enterprises—to understand pain points and validate solutions. Improve the reliability, observability, scalability, and performance of the systems and workflows you build.
Member of Technical Staff
Build core primitives end to end including entity ownership, audit, authorization, and orchestration, ensuring the right actions are the default and incorrect actions are difficult. Own the domain model by turning Fluidstack's concepts of power, datacenters, and chips into composable entities that remain durable over time. Define interactions with external systems by interfacing with vendor systems and ingesting domain-specific formats such as KMZ, BIM, Revit, and vendor documents. Enable AI agents to be first-class operators of Fluidstack's systems by providing tools, guardrails, and audit trails to allow safe and effective operation beyond mere advice.
AI Field Engineer - Enterprise
AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems quickly. Responsibilities include building POCs, MVPs, and production integrations; 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 spend most of their time on-site with customers, building relationships and trust in person. They work specifically on technical delivery and deployment by building end-to-end POCs and MVPs inside customer codebases, architecting inference foundations, running load tests, tuning deployments, and deploying new model families on inference frameworks. They guide customers on model selection and fine-tuning strategies, build and run fine-tuning pipelines, and design evaluation frameworks. They engage in structured discovery conversations, own technical relationships from engagement to deployment, and spend time on-site embedded with customer teams. Finally, they identify recurring customer pain points, propose product improvements, codify deployment patterns, and feed customer signals back into the product roadmap.
Member of Technical Staff
AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems. They build POCs, MVPs, and production integrations, ship code, run benchmarks, debug production issues, and architect deployments. They also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements. The role involves spending time on-site with customers to build relationships and trust. Responsibilities include building end-to-end POCs and MVPs with customer engineering teams, architecting inference foundations and sizing deployments for GenAI core products, running load tests to establish performance baselines, tuning deployments, deploying and validating new model families, guiding customers on model selection and fine-tuning strategies, building fine-tuning pipelines, designing evaluation frameworks, leading discovery conversations, owning technical relationships from first engagement to production deployment, and feeding customer signals back into the product roadmap. They also codify repeatable deployment patterns and contribute to internal tooling, documentation, and platform improvements.
AI Field Engineer - Microsoft Foundry
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, participate in executive-level discussions about architecture, strategy, and business outcomes. Responsibilities include shipping code, running benchmarks, debugging production issues, architecting deployments, leading discovery conversations, aligning stakeholders, and translating customer pain points into product improvements. They work on technical delivery and deployment by building end-to-end POCs and MVPs inside customer codebases and infrastructure, architecting inference foundations, sizing deployments for scale, running load tests, and tuning deployments to meet latency, throughput, and cost targets. They deploy and validate new model families on inference frameworks, determining optimal configurations and serving patterns. They guide customers in model selection, fine-tuning strategy, and evaluation methodology, build and run fine-tuning pipelines, and design evaluation frameworks for production metrics. They also manage customer engagement by leading discovery conversations, owning the technical relationship, embedding with customer engineering teams on-site, and building trust in person. Lastly, they provide product feedback by identifying recurring pain points, proposing product improvements, codifying deployment patterns, contributing to internal tooling and documentation, and feeding customer signals back into the product roadmap with specificity and urgency.
Director, Revenue Strategy & Analytics
As an AI Field Engineer, responsibilities include embedding with customers and technology partners to convert complex AI problems into production systems quickly. The role involves hands-on development by building proofs of concept (POCs), minimum viable products (MVPs), and production integrations. Duties comprise shipping code, running benchmarks, debugging production issues, and architecting deployments. Leading discovery conversations, aligning stakeholders, and translating customer pain points into product improvements are part of the role. Specifically, the engineer builds end-to-end POCs and MVPs inside customer codebases and infrastructure, architects inference foundations for GenAI core products, sizes scalable deployments, runs load tests to establish performance baselines, tunes deployments, and deploys models on inference frameworks while optimizing configurations. The role also includes guiding customers on model selection and fine-tuning strategies, building fine-tuning pipelines, designing evaluation frameworks, and leading engagements to embed deeply with customer teams. Field Engineers spend time on-site to build trust, identify recurring customer pain points, translate these into product proposals, codify deployment patterns to contribute back to internal tooling and platform improvements, and feed customer feedback into the product roadmap with specificity and urgency.
Paid Growth Marketer
AI Field Engineers at Fireworks embed with ambitious customers and technology partners to turn complex AI problems into production systems quickly. They build proofs of concept (POCs), MVPs, and production integrations by shipping code, running benchmarks, debugging production issues, and architecting deployments. They lead discovery conversations, align stakeholders, and translate customer pain points into product improvements, compressing the feedback loop from field to roadmap. The role involves being on-site with customers to build strong relationships and trust. Responsibilities include building end-to-end POCs and MVPs alongside customer engineering teams within their codebases and infrastructure; architecting inference foundations for GenAI core products and sizing deployments for scalability; running load tests and tuning deployments for latency, throughput, and cost targets; deploying and validating new model families on inference frameworks, optimizing shapes, quantization, and serving patterns; guiding customers on model selection, fine-tuning strategies, and evaluation methodologies; building and running fine-tuning pipelines while balancing model families, compute cost, and quality targets; designing evaluation frameworks that measure production-quality metrics; leading structured discovery conversations to understand customer pain points and proposing solutions; owning the technical relationship from first engagement through deployment; spending time on-site embedding with customers; identifying recurring customer pain points and translating them into product proposals; codifying repeatable deployment patterns and contributing to internal tooling and documentation; and feeding back customer signals into the product roadmap with specificity and urgency.
ML/AI Engineer - Vehicle Intelligence
Develop AI-powered vehicle intelligence features that understand user intent, trip goals, vehicle state, and system constraints. Apply reinforcement learning, planning, optimization, and data-driven modeling to improve vehicle-level decisions across energy, comfort, charging, routing, and proactive vehicle preparation. Build models using vehicle telemetry, navigation data, user behavior, weather, traffic, cabin conditions, charging patterns, and fleet data. Create personalization models that learn user routines, comfort preferences, driving patterns, charging habits, and trip priorities while preserving privacy and user control. Use simulation, digital twins, and scenario-based testing to train, evaluate, and validate AI behavior before production deployment. Collaborate with autonomous driving and VLA teams to define interfaces for sharing user intent, route objectives, vehicle constraints, energy targets, comfort preferences, and system-level recommendations. Integrate ML models into production vehicle and cloud platforms, considering latency, compute efficiency, reliability, safety, explainability, and over-the-air update readiness. Work cross-functionally with Product, UX, Systems Engineering and Controls.
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