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.
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.
Software Engineer, Applied AI
Build production AI workflows including agentic workflows over enterprise and government data with clear rules for model visibility, tool usage, and human review. Design context and grounding systems for models to provide relevant information without violating permissions or performance constraints. Work across backend services, APIs, async workers, data pipelines, internal tools, and product-facing surfaces. Engineer reliable LLM systems by building evaluations and feedback loops for model behavior and workflow outcomes. Own tracing and runtime visibility across models, context, tool calls, and generated outputs. Debug failures using context, traces, tool responses, user reviews, and production logs. Improve quality without compromising latency, cost, or security. Build reusable AI systems by creating shared primitives for context assembly, grounding, tool use, and reviewable outputs. Develop systems that turn domain-specific AI behavior into product infrastructure rather than one-off customer logic. Move quickly from prototype to production quality systems with founders and engineers. Lead through ownership and engineering quality by taking ownership of important product and platform surfaces without needing heavy direction. Write clean, maintainable code and create clear abstractions. Use tools like Claude Code, Codex, ChatGPT, Cursor, and similar to accelerate development while maintaining the same standards for generated and hand-written code. Treat LLMs as architectural components with failure modes and costs to manage, not just black boxes to call.
Medical Review Nurse - Clinical Validation
Design agent systems from first principles including deciding the loop, tools, context strategy, evaluation harness, and system topology. Engineer the context by focusing on prompt construction, context windows, tool surfaces, structured outputs, and citation grounding. Drive evaluation rigor by building evaluations prior to agent construction, diagnosing failures, fixing root causes, and proving improvements through metrics. Use AI tooling such as Claude Code and Codex extensively to plan, scaffold, refactor, and debug work. Become a domain expert in healthcare claims, coding guidelines, and medical records as an integral part of the job.
AI Engineer - Data Intelligence
Build and maintain components of Clarium's master data enrichment pipeline, which classifies and enriches every product flowing through the platform; design and own classification and entity resolution workflows that combine deterministic logic and large language models (LLMs) for production data processing; build and operate evaluation harnesses, label sets, and regression suites to measure and improve pipeline quality; write production-level Python and SQL code; analyze complex datasets using statistics and machine learning to surface actionable insights and inform pipeline improvements; proactively audit data for quality issues, diagnose root causes, and implement fixes.
Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.
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