Support the daily operations and maintenance of AI-accelerated high-performance computing infrastructure as part of a Singapore-based Operations Readiness team.
Responsibilities
Support deployment, configuration, and maintenance of GPU servers, storage servers, networking equipment and software components
Perform hardware diagnostics, systems functionality and firmware updates
Collaborate with engineering teams for tailored customer environments deployment
Serve as first line of engineering support for onsite operational issues
Troubleshoot incidents, escalations and provide feedback
Requirements
Bachelor’s degree in computer engineering, computer science, or a related technical field
The role focuses on developing Python applications, quantitative tools and data infrastructure that support trading decisions, research and risk management across Commodities, Agriculture, Metals and Energy.
Responsibilities
Design, develop and deploy quantitative tools supporting trading and research, including pricing, backtesting, risk analysis and PnL reporting
Build Python applications and data pipelines to explore, integrate and analyse alternative datasets, including weather and cargo data
Collaborate with Quantitative Researchers and Traders to translate trading ideas and business requirements into production applications and quantitative models
Architect, develop and maintain cloud applications in collaboration with infrastructure engineers, using AWS and related technologies
Develop and apply statistical and machine learning models to support research and trading across Agriculture, Metals and Energy
Design and build the AI data layer for a multi-cloud GPU fleet, including data placement, caching, and lifecycle management, while owning reliability and capacity across research teams.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Design and build the AI data layer for a multi-cloud GPU fleet
Build high-throughput data paths for training and RL
Build Kubernetes-native APIs, operators and storage integrations
Own quotas, capacity and data lifecycle across hundreds of research teams
Measure workloads use of data and build I/O telemetry
Requirements
BS or MS in Computer Science, Computer Engineering, Electrical Engineering or a related field, or equivalent experience
The System Architect will own the architecture of Hivemind Forge – an AI development ecosystem enabling the build, train, tune, evaluate, optimize, and deploy learning-based autonomy solutions.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Own and evolve the architecture of the Hivemind Forge AI development ecosystem.
Define and maintain architecture products in an integrated model-based systems engineering (MBSE) environment.
Establish architectural patterns, interfaces, APIs, SDK concepts, and technical standards across software, AI/ML, data, simulation, and deployment capabilities.
Define the data and metadata architecture underpinning the AI development lifecycle, including schemas, relationships, lineage, provenance, versioning, and lifecycle management.
Ensure traceable pedigree across datasets, training configurations, model artifacts, evaluations, software versions, and deployed capabilities.
Senior Staff Engineer to help define and build the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI systems across internal and external customer environments.
Compensation
Pay within range listed + Bonus + Equity
Responsibilities
Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and deployment.
Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement learning, distributed training, and emerging research workflows.
Design self-service AI development workflows that enable engineers to move seamlessly from local experimentation to large-scale distributed execution using familiar open source tools and frameworks.
Build the infrastructure required to support distributed training, simulation, inference, and reinforcement learning workloads.
Design and optimize shared GPU infrastructure across cloud and on-premises environments.
Requirements
Nice to have: Ray
Nice to have: KAI
Nice to have: Slurm
Nice to have: reinforcement learning
Nice to have: simulation-driven training
Skills
Senior Staff Engineer, ML Ops (R4941)
f7d8749a-72d6-407c-bf98-292ea630c73a · Posted Nov 9, 2026
Lead the design and implementation of the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI systems.
Compensation
Pay within range listed + Bonus + Equity
Responsibilities
Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and deployment.
Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement learning, distributed training, and emerging research workflows.
Design self-service AI development workflows that enable engineers to move seamlessly from local experimentation to large-scale distributed execution using familiar open source tools and frameworks.
Build the infrastructure required to support distributed training, simulation, inference, and reinforcement learning workloads.
Evaluate and integrate orchestration, scheduling, and resource management technologies to maximize scalability and developer productivity.
Lead the design and delivery of distributed systems that run GPU infrastructure and AI workloads at scale, providing technical vision and architecture for a core part of Radiant's AI cloud.
Responsibilities
Set the technical vision and architecture for your team's area, and lead its design and implementation.
Design and build distributed systems that run reliably on GPU infrastructure at scale.
Translate product and customer requirements into robust architectures, APIs, and technical designs, working closely with Product.
Own the performance, reliability, and cost of your team's systems as the platform grows.
Partner with SRE, Infra Ops, and customers on operations, incident response, and the most demanding workloads.
Develop, deploy, and maintain monitoring, alerting, and observability tools for Livermore Computing’s high-performance computing clusters, storage systems, networks, and data center facilities.
Compensation
$121,830 - $185,544 annually
$121,830 - $154,500 annually for SES.1 level
$146,340 - $185,544 annually for SES.2 level
Responsibilities
Develop, deploy, configure, and maintain monitoring, alerting, and observability tools for LC HPC clusters, storage systems, high-speed networks, and data center facility infrastructure.
Create and maintain software tools and processes that streamline alarm response, diagnostics, and routine operational tasks.
Configure and integrate data from building management and facility systems, such as power and cooling, with HPC system monitoring to provide a unified operational view.
Share alarm response and monitoring duties with operations staff, and analyze, diagnose, and resolve hardware, software, network, and facility problems of limited complexity.
Participate in the deployment, testing, and validation of new HPC systems and components, and the decommissioning of retired systems.
Requirements
Bachelor’s degree in computer science, computer engineering, or related field or the equivalent combination of education and related experience.
Lead and own day‑to‑day operations for US neocloud customers, managing uptime, incident response, capacity, and customer outcomes for GPU‑dense data centres and Kubernetes‑based platforms.
Responsibilities
Own operational delivery for US neocloud customers: availability, SLAs, incident and problem management, and change control.
Lead and develop the US operations team, including engineers, NOC/SRE staff and site‑level coordinators. Set hiring plans, on‑call structure and performance standards.
Run GPU cluster operations: health monitoring, firmware and driver lifecycle, fabric and storage performance, node replacement and capacity planning.
Act as leadership escalation point for major incidents and lead the communication with customer executives.
Work with customer data centre and engineering teams to define runbooks, handover processes and operating models.
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