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Red-team conversational AI models to find jailbreaks and prompt injections, annotate failures, and document risks for safety-critical deployments.
Senior engineer builds and evaluates AI training data for LLM code agents, writing prompts, producing reference code, and critiquing model outputs for correctness and quality.
Design and advise on scalable, secure AI architectures for AWS customers, focusing on GenAI/ML and Agentic systems, and evangelize best practices through technical content and workshops.
Business Development Manager at DeepLLMData, sourcing clients and partnerships for AI/LLM model training services, crafting proposals, and collaborating with technical teams.
Build and fine-tune large language models and deep-learning systems on Cerebras’ wafer-scale AI hardware to solve real-world customer problems, from training bespoke models to deploying agentic AI.
Build and deploy cutting-edge AI models, including LLMs, by collaborating with researchers to pre-train, fine-tune, and evaluate large-scale machine learning systems.
Build and deploy production-grade ML models and RAG pipelines to replace rule-based systems, focusing on model quality, evaluation, and self-hosted LLM inference.
Design and train cutting-edge AI agents and models for sovereign public-sector use cases, publish research at top venues, and lead AI-safety initiatives for national-scale deployments.
Build and deploy large language models and deep-learning systems to power search, recommendations, and personalization for Instacart’s grocery marketplace.
Build and fine-tune open-source LLMs for safety and customer-care agents using SFT, LoRA, and RLHF; design agentic workflows in LangGraph and ship production-grade AI systems handling millions of interactions.
Build and scale production LLM-powered healthcare applications, including RAG pipelines, agentic systems, and evaluation frameworks, while ensuring compliance and reliability in a regulated environment.
Build and ship the AI backbone for a property-management assistant: RAG pipelines, multi-step agent workflows, and LLM integrations that power daily operations for 20,000+ HOA and condo communities.
Build and deploy AI/ML models, focusing on language technologies like NLP and dialogue systems using Python, PyTorch, and Hugging Face frameworks.
Build and scale the backend platform powering AI agents (voice, chat, custom) that operate Close’s CRM via MCP, LangFuse, and Python/Temporal, shipping non-deterministic features to 11,000 customers.
Центр Исследований занимается созданием SoTA технологий искусственного интеллекта для анализа финансовых данных и их применения для AI-трансформации Блока Риски. Наша задача разработать и обучить мультимодальную…
Build and deploy agentic AI workflows for legal SaaS, using FastAPI backends and LLM tooling to automate contract review and legal research.
ML engineer builds and runs online reinforcement-learning pipelines to improve GigaChat’s post-training, designing experiments, reward signals, and distributed training workflows.
Lead the online reinforcement learning team for STEM-focused LLM post-training at GigaChat, designing RLHF pipelines, reward models, and data curation to improve reasoning in math, physics, and other sciences.
Lead the online reinforcement-learning team for GigaChat, designing post-training methods, reward models, and data pipelines to make the LLM more helpful and user-friendly.
Build AI agents and MCP servers that let LLMs autonomously use internal financial data; optimize GPU/CPU and storage for high-performance AI workloads; and implement end-to-end MLOps pipelines with RAG over knowledge graphs.
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