Senior CFD Engineer
Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.
Principal Machine Learning Engineer
The role involves building a platform used by Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. The candidate will work on a focused, stream-aligned, and cross-functional team that includes back-end, front-end, and design members, empowered to make its own implementation decisions towards meeting its objectives. Responsibilities include gathering and leveraging domain knowledge and experience from the Data Scientists and Simulation Engineers using the product, taking ownership of work from implementation to production, ensuring quality, scalability, and observability at every step, which includes testing, containerization, continuous integration and delivery, authentication, authorization, telemetry, observability, and monitoring.
Principal Forward Deployed Software Engineer
Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.
Principal Data Scientist
Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.
Delivery Engineer
Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.
MEP Manager, Data Centers
Develop novel architectures, system optimizations, optimization algorithms, and data-centric optimizations that significantly improve over state-of-the-art. Take advantage of the computational infrastructure of Together to create the best open models in their class. Understand and improve the full lifecycle of building open models; release and publish insights through blogs, academic papers, etc. Collaborate with cross-functional teams to deploy models and make them available to a wider community and customer base. Stay up-to-date with the latest advancements in machine learning.
Software Engineer, AI Video Agent
You will be building a new team in the US to develop the next generation smart AI video maker that can ingest user's content and compose quality videos for social media. You will work closely with product and marketing teams to quickly prototype, beta test, and produce the final version of this product using agent technology. The technology stack includes GCP, Typescript, Python, Redis, MongoDB, Cloud Storage, and various AI models. You will be involved in rushing prototype and production versions of this product, contributing to an innovative and ambitious project.
AI Engineer (New Graduate)
As an AI Engineer (New Graduate) at Distyl, you will design, implement, and deploy GenAI applications under the guidance of senior engineers, contributing to prompt design, agent logic, retrieval-augmented generation (RAG), and model evaluation to build full-stack AI applications that deliver measurable business value. You will gain exposure to customer-facing work by shadowing technical conversations and learning how business needs are translated into system design, with opportunities to take on more responsibility in technical decisions and implementation. You will partner with senior engineers to understand customer problems and translate requirements into technical solutions, participate in customer discussions, solution design sessions, and iterative delivery. Additionally, you will help improve Distillery, Distyl’s internal LLM application platform, by building reusable components, tools, and workflows and learn best practices for scalable, maintainable AI infrastructure. You will write clean, well-tested, observable production-quality code that meets reliability, performance, and security standards and learn how production AI systems are monitored, debugged, and improved over time. You will assist with evaluating AI systems across accuracy, latency, cost, and robustness, applying user feedback and metrics to improve system performance. Finally, you will continuously develop your skills in LLMs, software engineering, and AI through mentorship, code reviews, and hands-on project work, learning modern development workflows and deployment practices used in enterprise AI.
Software Engineer, ML Data Infrastructure
The Software Engineer, ML Data Infrastructure will collaborate with engineers to build AI design experiences, tackle complex technical challenges including scaling distributed systems, build robust data infrastructure for foundation models at petabyte scale ensuring reliability and performance across multi-modal training pipelines, optimize data processing workflows for massive throughput, work with distributed systems, TPU infrastructure, and large-scale storage solutions, and partner with research scientists to translate data requirements into production-grade systems that accelerate model development cycles.
AI / ML Solutions Engineer
The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.
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