VP of Engineering
Lead the design and evolution of the AI cloud platform including GPU orchestration, compute scheduling, networking, storage, and distributed systems. Make critical decisions regarding cloud infrastructure, bare-metal deployments, and platform scalability. Participate personally in architecture reviews and key technical initiatives. Build and scale large GPU clusters supporting customer workloads and design systems for GPU provisioning, scheduling, utilization optimization, and capacity management. Drive platform reliability and performance for AI training and inference workloads, partnering closely with engineering teams on infrastructure requirements for next-generation AI systems. Remain deeply involved in engineering decisions and technical direction, contribute directly to infrastructure design and implementation efforts, review architecture proposals, system designs, and major infrastructure changes, and act as the technical escalation point for complex infrastructure challenges. Establish best practices for Kubernetes, observability, CI/CD, security, and operational excellence. Build SRE and Platform Engineering functions from the ground up. Define reliability standards including SLOs, SLIs, incident response processes, and capacity planning. Drive automation across infrastructure operations. Recruit and develop Infrastructure, Platform, and SRE teams. Build a high-performance engineering culture focused on ownership and execution. Partner with executive leadership on company strategy and infrastructure investments. Manage infrastructure budgets, vendor relationships, and capacity planning.
Systems Research Engineer Intern - GPU Programming (Fall 2026)
Participate in on-call rotation (Pagerduty) to respond to production incidents. Build and run infrastructure with Ansible, Terraform, and Kubernetes to enable scaling to a large 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 the product architecture from the perspectives of reliability, performance, and availability. Plan the growth of Together AI's infrastructure.
Research Intern, Inference (Fall 2026)
As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.
Frontier Agents Intern (Fall 2026)
As an AI Infrastructure Engineer at Together AI, the responsibilities include participating in on-call rotation (Pagerduty) to respond to production incidents; building and running infrastructure with Ansible, Terraform, and Kubernetes to enable scaling for a massive number of concurrent users; building monitoring systems to ensure the highest quality service for customers; designing and implementing operational processes such as deployments and upgrades; debugging production issues across all services and levels of the stack; identifying improvements for the product architecture from reliability, performance, and availability perspectives; and planning the growth of Together AI's infrastructure.
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.
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.
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