AI Jobs in New York

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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AI Field Engineers at Fireworks build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. They architect inference foundations for customers whose core product is built on GenAI, size deployments to scale without infrastructure bottlenecks, run load tests, establish latency, throughput, and cost baselines, tune deployments, and deploy and validate new model families on inference frameworks while determining optimal configurations and serving patterns. They guide customers on model selection, fine-tuning strategy, and evaluation methodology, build and run fine-tuning pipelines with customers, design and implement evaluation frameworks measuring production-quality metrics, and lead structured discovery conversations to understand customer pain points and success criteria. They own the technical relationship from first engagement through production deployment, embedding with customer engineering teams to build trust, spend time on-site, translate customer pain points into product proposals, codify repeatable deployment patterns, and feed customer signals back into the product roadmap with specificity and urgency.

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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, and engage in executive-level conversations about architecture, strategy, and business outcomes. Responsibilities include 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 work on building end-to-end POCs and MVPs inside customer codebases and infrastructure, architect inference foundations for GenAI core products, run load tests and tune deployments, deploy and validate new model families on inference frameworks, guide customers on model selection and fine-tuning strategies, build and run fine-tuning pipelines, and design evaluation frameworks. They manage customer engagement by leading discovery conversations, owning technical relationships, embedding with customer teams on-site, identifying recurring pain points, proposing product improvements, and codifying deployment patterns for internal use and platform improvement.