Research Engineer, Post-Training
Research Engineer, Post-Training improves voice AI models by turning customer problems into measurable targets, designing evaluations, curating data, fine-tuning models, and shipping production-ready systems.
60 total jobs
6 new jobs this week
Research Engineer, Post-Training improves voice AI models by turning customer problems into measurable targets, designing evaluations, curating data, fine-tuning models, and shipping production-ready systems.
Research browser behavior and develop device-identification and bot-detection techniques, taking findings from experiments through engineering implementation to improve the accuracy and stability of core products.
Develop and communicate experimental AI experiences by turning emerging research in AI video, world models, and agentic systems into working prototypes, polished demonstrations, and compelling stories.
Drive innovation in model compression and efficient deployment for advanced multimodal AI systems, focusing on reducing model footprint while preserving accuracy.
As a Research Engineer, you will shape audio AI models for products used by millions, focusing on training, improving, and evaluating state-of-the-art models.
Define and build the intelligence for Dispatch OS, leading the technical direction and architecture for ML-assisted dispatch decisions to enhance operational efficiency.
As an Applied Research Engineer, you'll develop AI research assets, focusing on building practical solutions and collaborating with cross-functional teams to enhance customer engagements.
Drive innovation in model serving and inference architectures for advanced AI systems, optimizing deployment strategies for high responsiveness and efficiency.
The Clinical Research Engineer will gain in-depth knowledge of FDA approved and investigational products, support training, and serve as a resource for clinical studies and protocols.
Research Engineer applying frontier machine-learning research—including reinforcement learning, RLHF and LLM post-training—to build and ship capabilities for mission-critical AI platforms.
As an Abuse Research Engineer, you will proactively hunt for advanced threats and dissect complex fraud vectors to safeguard Stripe's financial ecosystem.
The Applied AI Research Engineer will lead hands-on experimentation with AI technologies to shape engineering standards and practices across the organization.
As a Senior Research Engineer, you will monitor the threat landscape, coordinate security responses, and apply data analysis to guide research priorities and improve decision-making.
Build and operate large-scale web crawling systems that source, extract, and prepare high-quality data from the open web for frontier AI models, while enabling researchers to explore crawled data.
Deploy and optimize frontier AI models for real-time production use, owning the path from research checkpoints to reliable serving systems and improving latency, throughput, and cost at scale.
As an Applied Research Engineer in the Video team, you will develop and optimize production-grade foundation models for human-centric video generation, directly impacting product quality and deployment.
Deepgram seeks a skilled Machine Learning Engineer to join the Research team, focusing on scalable model training and developing tools for speech technologies.
Drive innovation in AI model architecture development, focusing on Large Language Models and Multi-Modal systems to enhance intelligence and efficiency.
Lead research and engineering for multimodal generative models, shaping architectures and shipping low-latency, interactive voice and video systems that enable natural, expressive conversations.
Drive innovation in training and optimizing vision-language models, focusing on real-world deployment and enhancing model quality and efficiency.
Drive innovation in training and optimizing vision-language models, focusing on real-world deployment and enhancing model quality and efficiency.
Drive innovation in training and optimizing vision-language models, focusing on real-world deployment and enhancing model quality and efficiency.
Drive innovation in training and optimizing vision-language models, focusing on real-world deployment and spanning the full model development lifecycle.
Drive innovation in AI model architecture development, focusing on Large Language Models and Multi-Modal systems to enhance intelligence and efficiency.