Software Engineer, ML Platform
SF Bay Area, CA
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingDesign and implement dynamic, traffic-based systems for hotswapping models on our GPU workers to maximize fleet efficiency and meet product SLOs.
Own the end-to-end CI/CD pipelines, including creating a resilient artifact store to manage all model checkpoints across multiple versions and providers.
Develop and maintain user-friendly APIs and interaction patterns that empower our product and research teams to ship groundbreaking features at high velocity.
From the employer’s posting
Build robust and sophisticated scheduling systems to manage jobs based on cluster availability and user priority, ensuring we optimally leverage thousands of expensive GPU resources. Design and implement dynamic, traffic-based systems for hotswapping models on our GPU workers to maximize fleet efficiency and meet product SLOs. Own the end-to-end CI/CD pipelines, including creating a resilient artifact store to manage all model checkpoints across multiple versions and providers.
Design and implement dynamic, traffic-based systems for hotswapping models on our GPU workers to maximize fleet efficiency and meet product SLOs. Own the end-to-end CI/CD pipelines, including creating a resilient artifact store to manage all model checkpoints across multiple versions and providers. Develop and maintain user-friendly APIs and interaction patterns that empower our product and research teams to ship groundbreaking features at high velocity.
Own the end-to-end CI/CD pipelines, including creating a resilient artifact store to manage all model checkpoints across multiple versions and providers. Develop and maintain user-friendly APIs and interaction patterns that empower our product and research teams to ship groundbreaking features at high velocity. Manage and optimize our complex inference workloads at scale, operating across multiple clusters and hardware providers.
What you’ll bring
All qualificationsCore experience
- 5+ years of professional engineering experience with deep, hands-on proficiency in Python and complex distributed systems architecture.
- Deep expertise in our core infrastructure stack: Linux, Docker, and Kubernetes.
- Strong experience with Redis, S3-compatible storage, and public cloud platforms (AWS).
- Experience with high-performance, large-scale ML systems (managing >100 GPUs).
- Deep familiarity with PyTorch and CUDA.
- Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink).
Qualification wording
5+ years of professional engineering experience with deep, hands-on proficiency in Python and complex distributed systems architecture.
Deep expertise in our core infrastructure stack: Linux, Docker, and Kubernetes.
Strong experience with Redis, S3-compatible storage, and public cloud platforms (AWS).
Experience with high-performance, large-scale ML systems (managing >100 GPUs).
Deep familiarity with PyTorch and CUDA.
Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink).
Tools in this posting
- Python
- Docker
- Redis
- PyTorch
- AWS
- S3
- Kubernetes
Source — Tool mentions in context
We are looking for a world-class builder who has a proven history of creating and managing large-scale, high-performance systems. You are a non-negotiable fit if you have: - 5+ years of professional engineering experience with deep, hands-on proficiency in Python and complex distributed systems architecture. - Extensive, practical experience building and managing systems at scale, specifically with queues, scheduling, traffic-control, and fleet management.
- Extensive, practical experience building and managing systems at scale, specifically with queues, scheduling, traffic-control, and fleet management. - Deep expertise in our core infrastructure stack: Linux, Docker, and Kubernetes. - Strong experience with Redis, S3-compatible storage, and public cloud platforms (AWS).
- Deep expertise in our core infrastructure stack: Linux, Docker, and Kubernetes. - Strong experience with Redis, S3-compatible storage, and public cloud platforms (AWS). What Sets You Apart (Bonus Points)
- Experience with high-performance, large-scale ML systems (managing >100 GPUs). - Deep familiarity with PyTorch and CUDA. - Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink).
About Lumalabs-Ai
We believe that reliable, high-performance infrastructure is the single biggest differentiating factor between success and failure in achieving our mission.
In the employer’s words · Read in context
Job description
About Luma AI
Luma's mission is to build multimodal AI to expand human imagination and capabilities. We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. So we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change.
Where You Come In
This is a rare opportunity to build the foundational infrastructure that powers our large-scale multimodal models. We believe that reliable, high-performance infrastructure is the single biggest differentiating factor between success and failure in achieving our mission. You will be a foundational member of the team, designing the critical systems that allow us to train and serve next-generation AI to millions of users.
What You'll Do
This is a 0-to-1 opportunity, not a maintenance role. You will have massive ownership to:
- Architect end-to-end model serving pipelines and integrate new model architectures from our research team into our core, high-throughput inference engine.
- Build robust and sophisticated scheduling systems to manage jobs based on cluster availability and user priority, ensuring we optimally leverage thousands of expensive GPU resources.
- Design and implement dynamic, traffic-based systems for hotswapping models on our GPU workers to maximize fleet efficiency and meet product SLOs.
- Own the end-to-end CI/CD pipelines, including creating a resilient artifact store to manage all model checkpoints across multiple versions and providers.
- Develop and maintain user-friendly APIs and interaction patterns that empower our product and research teams to ship groundbreaking features at high velocity.
- Manage and optimize our complex inference workloads at scale, operating across multiple clusters and hardware providers.
Who You Are
We are looking for a world-class builder who has a proven history of creating and managing large-scale, high-performance systems. You are a non-negotiable fit if you have:
- 5+ years of professional engineering experience with deep, hands-on proficiency in Python and complex distributed systems architecture.
- Extensive, practical experience building and managing systems at scale, specifically with queues, scheduling, traffic-control, and fleet management.
- Deep expertise in our core infrastructure stack: Linux, Docker, and Kubernetes.
- Strong experience with Redis, S3-compatible storage, and public cloud platforms (AWS).
What Sets You Apart (Bonus Points)
You'll stand out as an exceptional candidate if you also bring:
- Experience with high-performance, large-scale ML systems (managing >100 GPUs).
- Deep familiarity with PyTorch and CUDA.
- Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink).
- Familiarity with FFmpeg and multimedia processing pipelines.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
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Source & posting history
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
SF Bay Area, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 124
- Last seen by us
- Oct 9, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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