Software Engineer, Infrastructure
The Infrastructure Team builds the underlying tooling and infrastructure that powers all of Exa's systems, including building GPU cluster orchestration in Kubernetes, map-reduce batchjobs on Ray, and observability tooling. Responsibilities include building and scaling large-scale infrastructure such as GPU clusters, Kubernetes clusters, and cloud batchjob systems to enable the engineering organization to move rapidly and efficiently.
Senior ML Operations (MLOps) Engineer
The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.
Manual Quality Assurance Engineer, Web Core Product
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to 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 that improve 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.
Safety Engineer
The AI Safety Engineer is responsible for designing and building scalable backend infrastructure for content moderation, abuse detection, and agents guardrails by deploying AI/ML models into production systems. They will architect robust APIs, data pipelines, and service architectures to support real-time and batch moderation workflows. The role includes implementing comprehensive monitoring, alerting, and observability systems, establishing SLIs, SLOs, and performance benchmarks. The engineer will collaborate with ML engineers to translate research models into production-ready systems and integrate them across the product suite. Additionally, they will drive technical decisions and contribute to the vision for the safety roadmap to build next-generation platform guardrails for scale and precision.
Applied AI Engineer, Fullstack Software Engineer - Singapore
Collaborate closely with researchers, AI engineers, and product engineers on complex customer projects, integrating cutting-edge AI models into clients' software products. Design, develop, and maintain scalable and robust full-stack applications, ensuring seamless integration between front-end and back-end systems. Develop complex use cases with customers, providing guidance and ensuring the best production integration with back-end and front-end interfaces. Collaborate with product and science teams to continuously improve product and model capabilities based on customer feedback.
Applied AI Engineer - Agentic Workflows (Singapore)
Work with enterprise customers and internal teams to turn business workflows into scalable, production-ready agentic AI systems. Design and build LLM-powered agents that reason, plan, and act across tools and data sources with enterprise-grade reliability. Balance rapid iteration with enterprise requirements, evolving prototypes into stable, reusable solutions. Define and apply evaluation and quality standards to measure success, failures, and regressions. Debug real-world agent behavior and systematically improve prompts, workflows, tools, and guardrails. Contribute to shared frameworks and patterns that enable consistent delivery across customers.
Senior Engagement Manager
Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help customers achieve success. Scope and co-develop production-level data science projects with customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.
Member of Technical Staff, MLE [Singapore]
As a Member of Technical Staff, Applied ML, you will work directly with enterprise customers to design and deliver custom LLM solutions that address high-value problems by rapidly understanding customer domains. You will train and customize frontier models using Cohere's foundation model stack, CPT recipes, post-training pipelines including RLVR, and data assets. Additionally, you will develop state-of-the-art modeling techniques to enhance model performance for customer use cases and contribute improvements back to the foundation-model stack, including new capabilities, tuning strategies, and evaluation frameworks. You are expected to translate ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies. Part of your role also includes functioning within a customer-facing MLE team to identify opportunities where LLMs can create transformative impacts for enterprise customers, while operating with early-startup ownership to set a high technical bar and define the role of Applied ML at Cohere.
Senior Machine Learning 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.
Senior 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) empowered to make implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from Data Scientists and Simulation Engineers using the product.
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