Senior ML Operations (MLOps) Engineer
As a Senior ML Operations Engineer at Eight Sleep, you will pioneer cutting-edge ML technologies and integrate them into products and processes for health monitoring. You will own the design and operation of robust ML infrastructure by building scalable data, model, and deployment pipelines to ensure reliable model delivery to production. Your role involves partnering cross-functionally with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales across Pods globally. You will optimize ML systems for cost-effectiveness, scalability, and high performance by managing compute, storage, and deployment resources during training and inference. Additionally, you will develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment, and maintain clear and direct communication within a remote work environment.
Tech Lead, Android Core Product - Seongnam, South Korea
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 architectures to improve the performance, latency, throughput, and efficiency of deployed models. Build tools for visibility into bottlenecks and sources of instability and design and implement solutions to address high priority issues.
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.
[MS/PhD Intern] AI Engineer (정규직 전환형)
The position involves participating in an internship for Autonomous Driving Group where the candidate will engage in research and development aiming to connect research results to actual mass-production autonomous driving systems. Responsibilities include End-to-End design, implementation, and validation of core autonomous driving system technologies; designing and validating algorithms and models based on real vehicle data; performance analysis and improvement through simulation and real-road experiments; implementing research outcomes into applicable system forms; and close collaboration with production teams within the AD Group to solve problems. Depending on the specialization, tasks may include implementing perception and prediction ML models, preprocessing and analyzing driving data, evaluating model performance and analyzing results, object-level fusion and tracking using sensor data, real-time fusion logic improvement, SLAM and localization algorithm development, integration and debugging of vehicle software under Linux environment, designing data pipelines for autonomous driving data collection and analysis, vision-language-action model research, and building learning and evaluation pipelines with cross-department collaboration.
Senior AI Data Pipeline Engineer
Design and build high-performance, scalable data pipelines to support diverse AI and Machine Learning initiatives across the organization. Architect and implement multi-region data infrastructure to ensure global data availability and seamless synchronization. Develop flexible pipeline architectures that allow for complex branching and logic isolation to support multiple concurrent AI projects. Optimize large-scale data processing workloads using Databricks and Spark to maximize throughput and minimize processing costs. Maintain and evolve the containerized data environment on Kubernetes, ensuring robust and reliable execution of data workloads. Collaborate with AI researchers and platform teams to streamline the flow of high-quality data into training and evaluation pipelines.
AI Infrastructure Engineer
Operate and maintain a large-scale GPU cluster consisting of thousands of GPUs across multiple data centers using Kubernetes and Slurm. Monitor and diagnose failures across the GPU hardware and software stacks to ensure high availability and rapid recovery. Develop automation tools and scripts using Python or Shell to streamline repetitive infrastructure management tasks and improve operational efficiency. Manage GPU resource quotas and provide technical support to ML researchers to ensure optimal utilization of computing resources. Participate in the architectural design and performance tuning of distributed training environments for large-scale autonomous driving models.
Freelance Electrical Engineering & Python Expert - 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.
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 – Agentic Workflows (Korea)
Work closely with enterprise customers to translate high-value, ambiguous business problems into well-framed agentic problems with clear success criteria and evaluation methodologies. Provide technical leadership across the full development and evaluation lifecycle, including post-deployment iteration, for agentic workflows. Lead the design, build, and delivery of LLM-powered agents that reason, plan, and act across tools and data sources with enterprise-grade reliability and performance. 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. Mentor engineers across distributed teams. Drive clarity in ambiguous situations, build alignment, and raise engineering quality across the organization. Contribute to shared frameworks and patterns that enable consistent delivery across customers.
Marketing Intern - Seoul
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 our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.
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