Embedding MLOps
Read straight from Interpretai Tech's careers page, verified 1d ago.Seekless can tailor your résumé to it.
Seekless can write your résumé for this posting first.
Before you apply
LocationSF, San Francisco, United States
Typefull-time
SeniorityMid
Experience2+ yrs
DepartmentEngineering
Company size1+ people
PostedMay 13, 2025 · 1y ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Bachelor's degree in Computer Science, Engineering, or related technical field
2+ years of experience building large-scale, high-performance backend systems
2+ years of experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools
Strong proficiency in at least one programming language such as Python, Go, C++, or Java
Experience with containerization and orchestration technologies (Docker, Kubernetes)
Knowledge of distributed systems concepts like sharding, replication, and consensus algorithms
Demonstrated experience with database systems, search technologies, or AI/ML systems
Understanding of embedding techniques and their applications for text, images, or other data types
Experience with memory management, networking, and troubleshooting distributed systems
Proven ability to solve complex problems independently in fast-moving environments
Master's degree in Computer Science or related field
Experience with vector databases and similarity search systems (FAISS, Pinecone, Milvus, etc.)
Knowledge of ML frameworks (PyTorch, TensorFlow) and model optimization techniques
Experience with SOTA embedding models from a variety of domains (CV, LLMs, etc.)
Understanding of embedding evaluation metrics and quality assessment techniques
Experience with high-performance computing (HPC) environments
Background in designing systems for machine learning training and/or inference workloads
Knowledge of data sharding strategies, parallel processing, and memory optimization techniques
Experience with real-time streaming data processing frameworks
Contributions to open-source projects related to embeddings or machine learning infrastructure
Skills
Python
Go
C++
Java
AWS
GCP
Azure
Docker
Kubernetes
FAISS
Pinecone
Milvus
PyTorch
TensorFlow
Embedding Cluster Engineer: Cloud Infrastructure Specialist
Summary
We are seeking an experienced Cluster Infrastructure Engineer to design, implement, and maintain our vector embedding infrastructure on cloud platforms and support our distributed training platform on the cloud. In this role, you will be responsible for creating a scalable, high-performance system that supports both the training of embedding models and efficient inference workflows. The ideal candidate will combine expertise in machine learning infrastructure with strong system design skills to build robust embedding systems that power our AI applications, including vector searches, recommendations, and SOTA model inference for foundation models.
Key Responsibilities
•
Design and architect a multi-tenanted, cloud-native embedding infrastructure that supports both training and inference workloads.
•
Build scalable vector search and embedding generation services that handle high throughput, maintain low latency, and measure index time.
•
Implement fault-tolerant, high-performance systems for serving embedding models at scale.
•
Develop infrastructure automation using containerization, orchestration, and infrastructure-as-code practices.
•
Optimize embedding storage, indexing, and retrieval systems for performance and cost efficiency.
•
Design and implement robust monitoring and observability solutions to ensure system health and performance.
•
Collaborate with ML engineers and data scientists to understand and support embedding-related workloads.
•
Create scalable pipelines for generating and updating embeddings from various data sources (text, images, audio).
•
Implement security best practices and ensure compliance with data protection requirements3
•
Lead technical discussions regarding vector database architecture and performance optimization.
•
Support hiring efforts for building the core infrastructure.
Requirements
•
Bachelor’s degree in Computer Science, Engineering, or related technical field (Master’s preferred).
•
2+ years of experience building large-scale, high-performance backend systems9
•
2+ years of experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools39
•
Strong proficiency in at least one programming language such as Python, Go, C++, or Java.
•
Experience with containerization and orchestration technologies (Docker, Kubernetes).
•
Knowledge of distributed systems concepts like sharding, replication, and consensus algorithms.
•
Demonstrated experience with database systems, search technologies, or AI/ML systems8
•
Understanding of embedding techniques and their applications for text, images, or other data types.
•
Experience with memory management, networking, and troubleshooting distributed systems.
•
Proven ability to solve complex problems independently in fast-moving environments.
Preferred Qualifications
•
Experience with vector databases and similarity search systems (FAISS, Pinecone, Milvus, etc.).
•
Knowledge of ML frameworks (PyTorch, TensorFlow) and model optimization techniques.
•
Experience with SOTA embedding models from a variety of domains (CV, LLMs, etc.).
•
Understanding of embedding evaluation metrics and quality assessment techniques.
•
Experience with high-performance computing (HPC) environments.
•
Background in designing systems for machine learning training and/or inference workloads.
•
Knowledge of data sharding strategies, parallel processing, and memory optimization techniques.
•
Experience with real-time streaming data processing frameworks.
•
Contributions to open-source projects related to embeddings or machine learning infrastructure.
Impact of This Role
As an Embedding Cluster Engineer, you will build the foundation for our AI capabilities. Your work will enable our company to efficiently process unstructured data, power advanced search capabilities, enhance recommendation systems, and improve the accuracy of our introspection platform for our customers. Your infrastructure will be critical to both the training and deployment of our embedding models, directly impacting the speed and quality of our AI services.
About Our Team
Our infrastructure team is focused on building scalable, reliable systems that powers data instrospection and data curation platforms. We value collaboration, innovation, and a strong focus on production excellence. You’ll work closely with machine learning engineers, data scientists, and other infrastructure specialists to create systems that turn cutting-edge research into production-ready services.
Technical Environment
You’ll be working with technologies including:
•
Cloud environments (AWS/GCP/Azure)
•
Kubernetes for container orchestration
•
Vector databases and search engines
•
Distributed computing frameworks
•
ML model serving platforms
•
Infrastructure automation tools
•
Monitoring and observability solutions