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.
Software Engineer, macOS Core Product - Virginia Beach, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions addressing the highest priority issues.
Software Engineer, macOS Core Product - Rialto, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to their customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to gain visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Waco, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - South Bend, USA
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.
Software Engineer, macOS Core Product - Stockholm, Sweden
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases; deploy and operate the core ML inference workloads for the AI Voices serving pipeline; introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models; build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Enterprise Account Executive - Italy
The AI Outcomes Manager will partner with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on Glean. They will lead strategic reviews and advise customers on their AI roadmap to ensure maximum value from Glean's platform. The role involves translating business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D to shape priorities. They will conduct discovery workshops, scope pilots, and guide rollouts to drive broad and deep adoption of the Glean platform. Additionally, they will design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. The manager will proactively identify expansion opportunities and drive engagement across teams and functions.
Senior AI Engineer - San Mateo, CA
The role involves training, evaluating, and monitoring new and improved LLMs and other algorithmic models. The engineer will test and deploy content moderation models in production and iterate based on real-world performance metrics and feedback loops. They are expected to develop medium to long-term vision for content understanding-related R&D, collaborating with management, product, policy & operations, and engineering teams. The position requires taking ownership of results delivered to customers, advocating for changes in approach where needed, and leading cross-functional execution.
Senior Backend Engineer (Learn (Core Systems) & Search)
The Senior Backend Engineer, Learn (Core Systems) is responsible for redesigning existing components to support enterprise-scale workloads, analyzing and resolving bottlenecks in storage, query performance, APIs, and data models, leading migrations away from legacy implementations to sustainable replacements, improving reliability and efficiency of APIs and integrations for internal and external clients, driving technical projects from definition to delivery with Product Managers and other teams, maintaining a long-term view of system health and architecture, and sharing technical knowledge, reviewing designs, and setting best practices for backend and systems design. The Senior Backend Engineer, Search is responsible for architecting and scaling search infrastructure to billions of documents in multi-tenant environments, designing hybrid search combining keyword search with semantic understanding and vector search, building ranking and personalization systems that learn from user behavior, collaborating with AI engineers to integrate large language models into the search pipeline and build retrieval augmented systems, optimizing search performance across query parsing, index design, and distributed architecture, leading development of search observability and quality frameworks with clear metrics and monitoring, and working closely with product and design to shape the future of knowledge discovery at Sana.
Founding AI Engineer
The Founding AI Engineer is responsible for building end-to-end training pipelines covering data, training, evaluation, and inference. They design new model architectures or adapt open-source frontier models, fine-tune models using state-of-the-art methods such as LoRA/QLoRA, SFT, DPO, and distillation. They architect scalable inference systems using technologies like vLLM, TensorRT-LLM, and DeepSpeed, and build data systems for high-quality synthetic and real-world training data. The engineer develops alignment, safety, and guardrail strategies and designs evaluation frameworks across performance, robustness, safety, and bias. They own deployment responsibilities including GPU optimization, latency reduction, and scaling policies. Additionally, they shape early product direction, experiment with new use cases, and build AI-powered experiences from zero, exploring frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models. They take ownership of end-to-end problem solving, prototype, test, iterate, ship developments, and work with a mindset that values speed, discipline, humility, curiosity, and high standards within a founding team context.
Access all 4,256 remote & onsite AI jobs.
Frequently Asked Questions
Need help with something? Here are our most frequently asked questions.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.