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.
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.
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.
Materials Engineer & Python Expert - Freelance AI Trainer
Design computational material science problems to challenge a frontier AI model that must have an answer verifiable by code and require a specialized tool like ObsPy, instaseis, pyrocko, MITgcm, flopy/MODFLOW, or others. Pick an anchor tool and design a problem based on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or community-validated data pipelines. Write a Python reference solution, supply input files and model or domain definitions where needed. Decide the numerical answer and determine the domain-appropriate tolerance to count as correct. Test the problem against the model in batches of parallel attempts, tuning the difficulty until the agent only succeeds in a small number of attempts. Once finalized, the task is reviewed by a senior reviewer for quality feedback. Calibrate the problem by tuning it against batches of agent runs to reach a pass rate between 10–30%, involving rewriting waveform scenarios, tightening inversion parameters and solver tolerances, and monitoring agent behavior. Gain deeper command of the anchor tool and develop an intuition for how the frontier model navigates complex seismic, oceanographic, and sub-surface flow problems.
Mechanical Engineer & Python Expert - Freelance AI Trainer
Design computational engineering problems to challenge a frontier AI model, ensuring each problem has an answer verifiable by code and requires a specialized tool like Cantera, CoolProp, CalculiX, OpenFAST, or others. Each problem runs inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model's answer. Pick an anchor tool and design a problem that hinges on its solvers, simulation kernels, or domain-specific models. Write a Python reference solution, supply input files and geometry or mechanism definitions where needed. Decide the numerical answer and determine the acceptable tolerance for the model's correct response. Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent succeeds only in a small number of attempts. After finalizing the task scoring, submit it to a senior reviewer in the subfield for feedback to ensure task quality is high. Tune problems through rewriting thermodynamic cycles, tightening material models and boundary conditions, and monitoring agent performance to maintain a pass rate in the 10–30% range while deepening command of the anchor tool and developing understanding of how frontier models navigate complex thermal, structural, and fluid mechanics problems.
US Corporate Attorney - 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.
Mid/Senior AI Cinematic Video Editor (Full Remote - Brazil)
Conceptualise scripts based on current production needs and centred around existing AI characters. Create narrative-driven, longform video content, including stylized and explicit NSFW visuals, focusing on storytelling, atmosphere, and visual coherence. Own and manage end-to-end AI video production workflows from ideation and prompting to generation, editing, and post-production. Work extensively with ComfyUI pipelines by building, customizing, and optimizing node-based workflows for image and video generation. Utilize AI video tools such as Stable Diffusion (AUTOMATIC1111), ComfyUI, Runway, and others to produce high-quality visual sequences. Develop and maintain consistent character appearance, style, and scene continuity across longer narratives. Integrate motion graphic design and color correction to deliver cohesive final outputs. Experiment with new AI models, tools, and techniques, incorporating them into production workflows and sharing skills with the team. Align with the Content Lead's creative direction while maintaining high autonomy in execution and technical decisions. Continuously refine workflows for efficiency, scalability, and output quality.
Software Engineer (Brazil)
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
Principal Applied AI Researcher - Domain- Specific Models (Brazil)
The Principal Applied AI Researcher is responsible for setting the company-level technical direction for domain-specific model strategy, defining how models are built, evaluated, scaled, and sustained across continued pre-training, fine-tuning, post-training, and release quality standards. They architect the agentic model development paradigm by designing research infrastructure such as experiment orchestration, data pipeline automation, continuous evaluation, and competitive benchmarking to enhance research productivity. They lead deep research on model adaptation methodology, data curation, post-training methods, and training dynamics using agentic systems for parallel experiments and failure analysis. They also shape model strategy across all company domains by prioritizing new model domains and using agent-driven competitive intelligence and market analysis. The role includes defining evaluation strategies involving benchmark design, expert assessment, model failure analysis, robustness standards, and building continuous evaluation systems that inform real-time investment decisions. They lead cross-cutting research initiatives to advance data perception, retrieval, post-training, and runtime orchestration, ensuring these advancements compound across the platform. The researcher influences platform-level decisions such as model lifecycle management, portfolio strategy, release criteria, and integration architecture to support human and agentic system co-evolution. Additionally, they mentor senior researchers to enhance experimental rigor and technical judgment, participate in hiring, and maintain hands-on research impact through technical work, publications, patents, and visible output.
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