Member of Engineering (Post-training)
Research and experiment on ways to specialize foundational models to agentic use cases, build and maintain data and training pipelines, keep up with latest research and be familiar with state of the art in LLMs, alignment, synthetic data generation, and code generation, design, analyze, and iterate on training, fine-tuning, and data generation experiments, write high-quality and pragmatic code, and work as part of a team by planning future steps, discussing, and communicating clearly with peers.
Member of Technical Staff - ML Performance
The role involves engineering work focused on making machine learning systems performant at scale. This includes contributing to open-source projects and enhancing Modal's container runtime to improve the throughput and reduce the latency of language and diffusion models.
AI/ML Engineer, Rome
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Paris
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Madrid
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, London
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
AI/ML Engineer, Berlin
Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into the applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.
IT Engineer
Collaborate directly with the GTM team including Account Executives and Solutions Architects to ensure smooth integration and successful deployment of machine learning solutions. Build and present compelling demonstrations and proof of concepts that showcase AI technology capabilities. Design, develop, and deploy end-to-end AI-powered applications tailored to customer needs. Contribute to the internal machine learning platform by adding features and fixing bugs. Integrate and enable new machine learning models into the existing platform or client environments. Improve system performance, efficiency, and scalability of deployed models and applications. Work closely with partners to enable joint AI solutions and ensure seamless collaboration.
Finance Analytics Engineer
Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines such as SGLang- or vLLM-style systems and Together's inference stack, including kernel backends, speculative decoding like ATLAS, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post-training pipelines with methods such as RLHF, RLAIF, GRPO, DPO-style methods, and reward modeling, optimizing these workloads with inference-aware training loops. Use these pipelines to train, evaluate, and iterate on frontier models on top of the inference stack. Co-design algorithms and infrastructure to tightly couple objectives, rollout collection, and evaluation with efficient inference, identifying bottlenecks across training engines, inference engines, data pipelines, and user-facing layers. Run ablations and scale-up experiments to understand trade-offs among model quality, latency, throughput, and cost and feed insights into the design process. Profile, debug, and optimize inference and post-training services under production workloads. Drive roadmap items requiring engine modification, including changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks for rigorous validation of improvements. Set technical direction for cross-team efforts at the intersection of inference, RL, and post-training. Mentor other engineers and researchers on full-stack ML systems work and performance engineering.
Freelance n8n Workflow Developer - AI Trainer
Design, build, and evaluate advanced workflows in self-hosted n8n environments. Architect multi-system integrations for scalable automation pipelines. Develop and optimize AI-powered workflows such as content generation, automation pipelines, and enrichment systems. Build and maintain lead generation, outreach, and data processing automation systems. Implement web scraping workflows and ensure reliable data extraction and processing. Optimize workflow execution, node sequencing, and error handling to prevent failures, delays, and API timeouts.
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