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.
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.
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.
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.
Member of Technical Staff (Machine Learning Engineer)
Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.
Warehouse Supervisor (Temporary)
Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.
Deployment Engineer
Translate business requirements into AI/ML model requirements. Prepare data to train and evaluate AI/ML/DL models. Build AI/ML/DL models using state-of-the-art algorithms, especially transformers, sometimes leveraging existing algorithms from research. Test and evaluate models, benchmark quality, and publish models, datasets, and evaluations. Deploy models in production by containerizing them. Work with customers and internal employees to refine model quality. Establish continuous learning pipelines for models with online or transfer learning. Build and deploy containerized applications on cloud or on-premise environments.
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