Rackner is seeking a Software Engineer to build R&D technology, working directly with our CTO, with experience in Python, deploying containerized software in a cloud environment (such as AWS), and fine-tuning and deploying machine learning models. This remote role requires occasional on-site work and travel to customer locations.

Collaborate with perception experts and experienced roboticist to build solutions that scale from the lab to the customer, for one of the largest-scale robotics projects in history. You will architect, design, and implement state of the art solutions in Sensing, Machine Learning & Data Platform, Mapping, Labeling, State Estimation, Localization, Calibration and Performance. Develop production quality software while maintaining a strong safety culture.

You'll contribute to exploratory experimental research on AI safety, focusing on risks from powerful future systems and collaborate with teams like Interpretability, Fine-Tuning, and the Frontier Red Team with topics including Scalable Oversight and AI Control. Responsibilities include testing the robustness of safety techniques, running multi-agent reinforcement learning experiments and contributing to research papers, blog posts, and talks.

The team is responsible for products, tools and services to manage and streamline all AI efforts at LivePerson. You will collaborate on the design and implementation of our internal machine learning platform and consumer-facing Generative AI development product. The role involves designing, building, and integrating systems to manage the full machine learning lifecycle.

As a Senior AI Developer, you will be instrumental in integrating OpenAI technologies across our entire website by leveraging generative AI to create automated systems, utilizing company data for personalized content creation, and applying your data science expertise to enhance site-wide search, analytics, and image identification functionalities.

As a research engineer for Learnable Planner you will support integration of new AI technologies into our autonomy/planner stack enabling our launch of fully driverless autonomous trucks, contributing towards Waabi's vision of a single AI system that learns end-to-end and in a provably safe manner as well as our revolutionary high-fidelity, closed-loop simulator, Waabi World.

As a research scientist working on Learnable Planner, you will invent new AI technologies that support scalable planning solutions enabling our launch of fully driverless autonomous trucks. You'll design and execute a research agenda for deep-learning based motion planning for self-driving. You will also leverage and advance the state-of-the-art in robotics and machine learning to enable safe self-driving at scale.

The ML Training Team’s core mandate is training models as fast as possible for the company, ensuring models have 100% gpu utilization and can scale linearly from 8 gpus -> 256 gpus. Empowering MLEs by building profiling/debugging tools, setting up efficiency monitoring and integrating the trainer into the experiment management system. Optimize dataloaders / training data formats and distributed training configurations.

As a Staff Machine Learning Engineer on the Data Science team, you'll lead the implementation of cutting-edge AI products, focusing on technical design, delivery, and maintenance of a new machine learning platform. You will design, build, and maintain robust, production-grade ML models and MLOps systems to power personalized experiences across the MyFitnessPal ecosystem and mentor a team of engineers and data scientists.

Contribute to designing, building, evaluating, shipping, and refining Spotify’s personalization products by hands-on ML development. Collaborate with a cross functional agile team to build new product features that advance our mission to connect artists and fans. Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users.