Freelance AI Evaluation Engineer (Python/Full-Stack)
Create challenging coding test cases to push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases. Craft challenges that are fair but hard, where the AI has all the context it needs, requiring complex reasoning with information scattered across files and external sources. Analyze AI failures to understand the model's struggles and strengths. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.
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.
[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.
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.
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.
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.
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