Product Engineer
Translate research into product by working with client-side researchers on post-training, evaluations, safety, and alignment to build the necessary primitives, data, and tooling. Partner closely with core customers and frontier research labs to tackle technical challenges related to model improvement, performance, and deployment. Shape and propose model improvement work by translating customer and research objectives into technically rigorous proposals and execution plans. Lead the end-to-end lifecycle of projects including discovery, writing PRDs and technical specs, prioritizing trade-offs, running experiments, shipping solutions, and scaling successful pilots. Lead high-stakes engagements with senior stakeholders, define success metrics, identify risks, and drive programs to measurable outcomes. Collaborate across teams including research, platform, operations, security, and finance to deliver production-grade results. Design and implement robust evaluation frameworks, ensure data quality and feedback loops, and share learnings to elevate technical execution across accounts.
Site Reliability Engineer / DevOps
The role involves translating AI research into product solutions by working with client-side researchers on post-training, evaluations, safety, and alignment, building the necessary primitives, data, and tooling. The engineer partners closely with leading AI teams and frontier research labs to solve complex technical problems related to model improvement, performance, and deployment, shaping and proposing technically rigorous model improvement work. Responsibilities include leading the end-to-end lifecycle from discovery to scalable pilots, conducting technical working sessions with senior stakeholders, defining success metrics, managing risks, and driving programs to measurable outcomes. The role requires collaboration with research, platform, operations, security, and finance teams to deliver reliable, production-grade solutions. Additionally, the engineer designs and establishes robust evaluation frameworks, closes feedback loops on data quality, and shares best practices across accounts.
Freelance Electrical Engineer with Python Experience - AI Trainer
Contributors may design rigorous electrical engineering problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python (NumPy, Pandas, SciPy), improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.
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.
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.
MCP & Tools Python Developer - Agent Evaluation Infrastructure
The role involves developing and maintaining MCP-compatible evaluation servers, implementing logic to check agent actions against scenario definitions, creating or extending tools used by writers and QAs to test agents, working closely with infrastructure engineers to ensure compatibility, and occasionally assisting with test writing or debug sessions.
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