Software Engineer - Reliability GPU Infrastructure Job at Luma AI (Palo Alto)

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Software Engineer - Cloud FinOps & Reliability

This is a foundational engineering position for a technical, data-driven expert ...

Location

United States , Palo Alto

Salary:

120000.00 - 255000.00 USD / Year

Luma AI

Expiration Date

Until further notice

Requirements

5+ years of experience in a technical role such as Site Reliability Engineer, DevOps Engineer, Infrastructure Engineer, or a dedicated Cloud Cost Engineer
Deep, hands-on expertise with the cost models and optimization levers of at least one major cloud provider (AWS, GCP), and a willingness to learn others
Proficient in Python for the purpose of scripting, data analysis, and building automation tooling
Strong, foundational understanding of cloud infrastructure, including containerization (Docker, Kubernetes), networking, and storage
Not an accountant
you are a systems thinker who is passionate about applying engineering principles to solve financial challenges at scale
A tenacious troubleshooter and a data-driven decision-maker who thrives on finding the 'why' behind the numbers

Job Responsibility

Analyze & Optimize: Actively monitor and analyze costs across our entire technical ecosystem—including multi-cloud infrastructure (AWS, GCP, OCI), on-premise clusters, and third-party services—to identify and execute on opportunities for cost optimization. Develop forecasting models to predict future spend and inform our capacity planning
Manage & Commit: Develop and actively manage a multi-million dollar portfolio of Reserved Instances (RIs) and Savings Plans to maximize commitment-based discounts across our global GPU and CPU fleets
Automate & Build: Apply a software engineering approach to design, build, and maintain custom tools and automation in Python and SQL. Your systems will track, analyze, and report on costs across our entire fleet of providers and services, with a focus on detecting anomalies immediately
Partner & Advise: Working closely as an embedded member of the SRE team, you will partner with fellow SREs and research teams to model the cost implications of new models and infrastructure designs, providing expert guidance on cost-performance trade-offs
Visualize & Report: Create and manage a centralized observability stack for cloud costs, building dashboards in tools like Grafana to give a real-time, granular view of our financial posture to all stakeholders

Fulltime

Machine Learning Platform / Backend Engineer

We are seeking a Machine Learning Platform/Backend Engineer to design, build, an...

Location

Serbia; Romania , Belgrade; Timișoara

Salary:

Not provided

Everseen

Expiration Date

Until further notice

Requirements

4-5+ years of work experience in either ML infrastructure, MLOps, or Platform Engineering
Bachelors degree or equivalent focusing on the computer science field is preferred
Excellent communication and collaboration skills
Expert knowledge of Python
Experience with CI/CD tools (e.g., GitLab, Jenkins)
Hands-on experience with Kubernetes, Docker, and cloud services
Understanding of ML training pipelines, data lifecycle, and model serving concepts
Familiarity with workflow orchestration tools (e.g., Airflow, Kubeflow, Ray, Vertex AI, Azure ML)
A demonstrated understanding of the ML lifecycle, model versioning, and monitoring
Experience with ML frameworks (e.g., TensorFlow, PyTorch)

Job Responsibility

Design, build, and maintain scalable infrastructure that empowers data scientists and machine learning engineers
Own the design and implementation of the internal ML platform, enabling end-to-end workflow orchestration, resource management, and automation using cloud-native technologies (GCP/Azure)
Design and manage Kubernetes-based infrastructure for multi-tenant GPU and CPU workloads with strong isolation, quota control, and monitoring
Integrate and extend orchestration tools (Airflow, Kubeflow, Ray, Vertex AI, Azure ML or custom schedulers) to automate data processing, training, and deployment pipelines
Develop shared services for model behavior/performance tracking, data/datasets versioning, and artifact management (MLflow, DVC, or custom registries)
Build out documentation in relation to architecture, policies and operations runbooks
Share skills, knowledge, and expertise with members of the data engineering team
Foster a culture of collaboration and continuous learning by organizing training sessions, workshops, and knowledge-sharing sessions
Collaborate and drive progress with cross-functional teams to design and develop new features and functionalities
Ensure that the developed solutions meet project objectives and enhance user experience

Fulltime

Senior Manager, Performance AI/ML Network Deployment Engineering

The Senior Manager, DC GPU Advanced Forward Deployment and Systems Engineering i...

Location

United States , Santa Clara

Salary:

210400.00 - 315600.00 USD / Year

AMD

Expiration Date

Until further notice

Requirements

Expertise in networking and performance optimization for large-scale AI/ML networks, including network, compute, storage cluster design, modelling, analytics, performance tuning, convergence, scalability improvements
Prefer candidates with solid, hands-on expertise in at least one or more of 3 domains, namely compute, network, storage
Experience in working with large customers such as Cloud Service Providers and global enterprise customers
Proven leadership in engaging customers with diverse technical disciplines in avenues such as Proof of Concept, Competitive evaluations, Early Field Trials etc
Direct experience in working with large customers and can operate with sense of urgency, own the problems and resolve it
Demonstrated leadership in network architecture, hands on experience in RoCEv2 Design, VXLAN-EVPN, BGP, and Lossless Fabrics
Proven ability to influence design and technology roadmaps, leveraging a deep understanding of datacenter products and market trends
Extensive hands-on Network deployment expertise and proven track record of delivering large projects on time. Cisco, Juniper or Arista experience is preferred
Direct, co-development/deployment experience in working with strategic customers/partners in bringing solutions to market
Excellent communication level from engineer to mid-management to C-level of audience

Job Responsibility

Collaborate with strategic customers on scalable designs involving compute, networking, storage environment, work with industry partners, Internal teams to accelerate the deployment, adoption of various AI/ML models
Engage system-level triage and at-scale debug of complex issues across hardware, firmware, and software, ensuring rapid resolution and system reliability
Drive the ramp of Instinct-based large scale AI datacenter infrastructure based on NPI base platform hardware with ROCm, scaling up to pod and cluster level, leveraging the best in network architecture for AI/ML workloads
Enhance tools and methodologies for large-scale deployments to meet customer uptime goals and exceed performance expectations
Engage with clients to deeply understand their technical needs, ensuring their satisfaction with tailored solutions that leverage your past experience in strategic customer engagements and architectural wins
Provide domain specific knowledge to other groups at AMD, share the lessons learnt to drive continuous improvement
Engage with AMD product groups to drive resolution of application and customer issues
Develop and present training materials to internal audiences, at customer venues, and at industry conferences

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