Deep Learning Engineer
Palo Alto, CA
Engineering /
Full Time /
On-site
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Work Permit
Are you authorized to work in the country where this position is based?
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If you don't have work authorization, will you require work authorization sponsorship?
Yes
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Relocation
Are you willing to relocate if you do not live close to the local office of choice?
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How soon can you move to the area of the local office?
within 1 month after offer signed
1-3 month after offer signed
3-6 months after offer signed
Short Answer
Briefly describe the most relevant project you have worked on. Be sure to outline your specific contributions.
Deep Learning Engineer
What is your experience level with designing and scaling large foundation model training pipelines (e.g., LLMs)?
Advanced – I have independently designed and scaled large-scale training pipelines using frameworks like PyTorch, DeepSpeed, or Megatron-LM, including distributed training, scheduling (e.g., SLURM), and data handling at 100M+ parameter scale.
Intermediate – I have contributed to foundation model training pipelines and used tools like HuggingFace Transformers or Accelerate, but not at large scale or without guidance.
Beginner – I have experimented with model training in research or side projects, typically using prebuilt scripts or small models.
No experience – I have not worked on foundation model training pipelines.
What is your experience level with building production-ready back-end infrastructure to serve ML models or scientific workflows?
Advanced – I have built and maintained robust back-end systems (e.g., Django, Flask, Node.js) for serving ML models or tools, including API design, CI/CD, auth, and deployment via Docker/Kubernetes.
Intermediate – I have implemented or modified APIs and worked with model serving systems, but was not responsible for end-to-end infrastructure or production deployment.
Beginner – I’ve built simple back-ends or followed tutorials for serving models, but have no experience in a production context.
No experience – I have not worked on ML-serving or back-end systems.
What is your experience level with infrastructure tools for distributed training or deployment (e.g., SLURM, Kubernetes, cloud compute, Docker)?
Advanced – I have independently configured and managed distributed training or deployment environments using tools like SLURM, Kubernetes, Docker, and cloud platforms (e.g., AWS, GCP).
Intermediate – I have used these tools within existing pipelines or environments, but have not independently set them up or managed them end-to-end.
Beginner – I’ve experimented with some of these tools in side projects or learning environments but not in real-world settings.
No experience – I have not used any of these infrastructure tools.
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