Experience with vector databases (Milvus, Qdrant, Pinecone, or FAISS). Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar stacks. Experience with cloud environments ...
Experience with vector databases (Milvus, Qdrant, Pinecone, or FAISS). Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar stacks. Experience with cloud environments ...
Founding Engineer
Bodega Bay, CA · On-site
LangChain, LangGraph, OpenAI, Anthropic, LLaMA, Pinecone, Qdrant Governance: Keycloak, Open Policy Agent (OPA), OpenTelemetry, Slack integrations Visualization: Vega, Flourish, React ReChart ...
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Founding Engineer
Bodega Bay, CA · On-site
LangChain, LangGraph, OpenAI, Anthropic, LLaMA, Pinecone, Qdrant Governance: Keycloak, Open Policy Agent (OPA), OpenTelemetry, Slack integrations Visualization: Vega, Flourish, React ReChart ...
AI/Machine Learning Engineer
Irvine, CA · On-site
$175 - $200/hr
Python, PyTorch / TensorFlow, Hugging Face, LangChain / LlamaIndex, Vector DBs (e.g., Pinecone, Qdrant, pgvector); MLOps & Infrastructure: Docker, Kubernetes, MLflow / Weights & Biases, Cloud ML ...
AI/Machine Learning Engineer
Irvine, CA · On-site
$175 - $200/hr
Python, PyTorch / TensorFlow, Hugging Face, LangChain / LlamaIndex, Vector DBs (e.g., Pinecone, Qdrant, pgvector); MLOps & Infrastructure: Docker, Kubernetes, MLflow / Weights & Biases, Cloud ML ...
LangChain, LangGraph, OpenAI, Anthropic, LLaMA, Pinecone, Qdrant * Governance: Keycloak, Open Policy Agent (OPA), OpenTelemetry, Slack integrations * Visualization: Vega, Flourish, React ReChart
LangChain, LangGraph, OpenAI, Anthropic, LLaMA, Pinecone, Qdrant * Governance: Keycloak, Open Policy Agent (OPA), OpenTelemetry, Slack integrations * Visualization: Vega, Flourish, React ReChart
Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector * Voice AI, WebSockets, WebRTC, or real-time streaming systems * AI evaluations, observability, prompt injection protection, and model ...
Quick apply
Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector * Voice AI, WebSockets, WebRTC, or real-time streaming systems * AI evaluations, observability, prompt injection protection, and model ...
Full Stack AI Engineer | Onsite in San Francisco | Local Candidates Only, 5
San Francisco, CA · On-site
$150 - $210/hr
Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector * Voice AI, WebSockets, WebRTC, or real-time streaming systems * AI evaluations, observability, prompt injection protection, and model ...
Full Stack AI Engineer | Onsite in San Francisco | Local Candidates Only, 5
San Francisco, CA · On-site
$150 - $210/hr
Vector databases such as Pinecone, Qdrant, Weaviate, or pgvector * Voice AI, WebSockets, WebRTC, or real-time streaming systems * AI evaluations, observability, prompt injection protection, and model ...
Senior AI Engineer
Los Angeles, CA · On-site
$112K - $154K/yr
Ingest from BigQuery, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant/PgVector); enforce governance via OpenMetadata and column-level ACLs. * Scalable ...
Senior AI Engineer
Los Angeles, CA · On-site
$112K - $154K/yr
Ingest from BigQuery, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant/PgVector); enforce governance via OpenMetadata and column-level ACLs. * Scalable ...
Senior AI Engineer
$112K - $154K/yr
Data & Storage Architecture: Ingest from BigQuery, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant/PgVector); enforce governance via OpenMetadata and ...
Quick apply
Senior AI Engineer
$112K - $154K/yr
Data & Storage Architecture: Ingest from BigQuery, object-store lakes (Parquet, Avro); generate embeddings and persist to vector DBs (Qdrant/PgVector); enforce governance via OpenMetadata and ...
Data Engineer (Founding Team)
Bodega Bay, CA · On-site
$135K - $163K/yr
Weaviate, Qdrant, Pinecone) and embedding pipelines * Experience building or contributing to enterprise connector ecosystems * Knowledge of ontology versioning , graph diffing , or semantic schema ...
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Data Engineer (Founding Team)
Bodega Bay, CA · On-site
$135K - $163K/yr
Weaviate, Qdrant, Pinecone) and embedding pipelines * Experience building or contributing to enterprise connector ecosystems * Knowledge of ontology versioning , graph diffing , or semantic schema ...
... Qdrant, FAISS, Chroma) • Implemented model-level RBAC, usage tracking, audit trails • Integrated with API rate limits, tenant billing, and SLA observability • Experience with policy-as-code ...
... Qdrant, FAISS, Chroma) • Implemented model-level RBAC, usage tracking, audit trails • Integrated with API rate limits, tenant billing, and SLA observability • Experience with policy-as-code ...
DevOps Engineer (Founding Team)
Bodega Bay, CA · On-site
$62.50 - $85.75/hr
Familiarity with vector DBs (Weaviate, Qdrant, Pinecone) and embedding pipelines * Monitoring and governing long-running or multi-agent chains * Auditability and replay systems for agent decision ...
Quick apply
DevOps Engineer (Founding Team)
Bodega Bay, CA · On-site
$62.50 - $85.75/hr
Familiarity with vector DBs (Weaviate, Qdrant, Pinecone) and embedding pipelines * Monitoring and governing long-running or multi-agent chains * Auditability and replay systems for agent decision ...
Senior Engineer - Machine Learning
San Diego, CA · On-site
$140.80 - $211.20/hr
Vector databases and search systems (OpenSearch, Qdrant, etc.). * Cost-aware system design - model routing (small vs. large models), dynamic batching, and caching strategies. Pay range and Other ...
Senior Engineer - Machine Learning
San Diego, CA · On-site
$140.80 - $211.20/hr
Vector databases and search systems (OpenSearch, Qdrant, etc.). * Cost-aware system design - model routing (small vs. large models), dynamic batching, and caching strategies. Pay range and Other ...
Senior Engineer - Machine Learning
San Diego, CA · On-site
$110K - $152K/yr
Vector databases and search systems (OpenSearch, Qdrant, etc.) * Cost-aware system design - model routing (small vs. large models), dynamic batching, and caching strategies Qualcomm is an equal ...
Senior Engineer - Machine Learning
San Diego, CA · On-site
$110K - $152K/yr
Vector databases and search systems (OpenSearch, Qdrant, etc.) * Cost-aware system design - model routing (small vs. large models), dynamic batching, and caching strategies Qualcomm is an equal ...
Senior Engineer - Machine Learning
San Diego, CA · On-site
$110K - $152K/yr
Vector databases and search systems (OpenSearch, Qdrant, etc.) * Cost-aware system design - model routing (small vs. large models), dynamic batching, and caching strategies Qualcomm is an equal ...
Senior Engineer - Machine Learning
San Diego, CA · On-site
$110K - $152K/yr
Vector databases and search systems (OpenSearch, Qdrant, etc.) * Cost-aware system design - model routing (small vs. large models), dynamic batching, and caching strategies Qualcomm is an equal ...
Platform Support Architect
Sacramento, CA · On-site
$140 - $210/hr
Experience with one or more vector databases (Milvus, Qdrant, Pinecone, pgVector, OpenSearch/Elasticsearch vectors, etc.), including schema design, ingestion, and operations. * Solid understanding of ...
Platform Support Architect
Sacramento, CA · On-site
$140 - $210/hr
Experience with one or more vector databases (Milvus, Qdrant, Pinecone, pgVector, OpenSearch/Elasticsearch vectors, etc.), including schema design, ingestion, and operations. * Solid understanding of ...
AI Engineer (Application Design & Rapid Deployment)
El Segundo, CA · On-site
$100/hr
Proficient with vector databases (e.g., Pinecone, Qdrant, Milvus), relational databases, and integrating unstructured data pipelines. * Prompt Engineering: Excellent at prompt engineering best ...
AI Engineer (Application Design & Rapid Deployment)
El Segundo, CA · On-site
$100/hr
Proficient with vector databases (e.g., Pinecone, Qdrant, Milvus), relational databases, and integrating unstructured data pipelines. * Prompt Engineering: Excellent at prompt engineering best ...
Senior Staff Machine Learning Engineer, AI Agent Platform
Palo Alto, CA · On-site
$122K - $168K/yr
... Qdrant, Kafka, PostgreSQL, MongoDB, Spark, Ray, Temporal, Redis, Neo4j, etc.). * 5+ years managing end-to-end SDLCs (CI/CD, Kubernetes, testing, monitoring, production support). * 4+ years building ...
Senior Staff Machine Learning Engineer, AI Agent Platform
Palo Alto, CA · On-site
$122K - $168K/yr
... Qdrant, Kafka, PostgreSQL, MongoDB, Spark, Ray, Temporal, Redis, Neo4j, etc.). * 5+ years managing end-to-end SDLCs (CI/CD, Kubernetes, testing, monitoring, production support). * 4+ years building ...
Sr AI/Agentic Engineer
Tustin, CA · On-site
$115K - $158K/yr
... Qdrant, Pinecone, Weaviate, OpenSearch, or pgvector), retrieval, and re-ranking -- including measuring and improving retrieval quality. • Demonstrated experience leading agentic workflows in ...
Sr AI/Agentic Engineer
Tustin, CA · On-site
$115K - $158K/yr
... Qdrant, Pinecone, Weaviate, OpenSearch, or pgvector), retrieval, and re-ranking -- including measuring and improving retrieval quality. • Demonstrated experience leading agentic workflows in ...
Senior AI/ML Engineer
$180K - $350K/yr
Qdrant * Apache Iceberg * Implement entity resolution, deduplication, temporal versioning, and confidence-weighted data fusion across multiple sources. Pattern Recognition & Adversarial Detection
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Senior AI/ML Engineer
$180K - $350K/yr
Qdrant * Apache Iceberg * Implement entity resolution, deduplication, temporal versioning, and confidence-weighted data fusion across multiple sources. Pattern Recognition & Adversarial Detection
... Qdrant for RAG pipelines) * You've built data pipelines using SQL and distributed data processing tools * You're familiar with cloud platforms such as AWS, GCP, or Azure * You've deployed ...
... Qdrant for RAG pipelines) * You've built data pipelines using SQL and distributed data processing tools * You're familiar with cloud platforms such as AWS, GCP, or Azure * You've deployed ...
Qdrant information
What is the difference between Qdrant vs Data Scientist?
| Aspect | Qdrant | Data Scientist |
|---|---|---|
| Required Credentials | Technical certifications, knowledge of vector databases | Degree in Data Science, Statistics, or related field |
| Work Environment | Tech companies, startups, AI-focused firms | Research labs, tech companies, consulting firms |
| Industry Usage | AI, machine learning, data storage | Data analysis, predictive modeling, research |
Qdrant primarily focuses on managing and deploying vector similarity search databases, requiring technical skills in database management and AI tools. Data Scientists analyze data, build models, and interpret results. While both roles operate within the tech and AI industry, Qdrant specialists are more technical and infrastructure-oriented, whereas Data Scientists focus on data analysis and modeling.
What are popular job titles related to Qdrant jobs in California?
For Qdrant jobs in California, the most frequently searched job titles are:
What job categories do people searching Qdrant jobs in California look for?
The top searched job categories for Qdrant jobs in California are:
What cities in California are hiring for Qdrant jobs?
Cities in California with the most Qdrant job openings:
Full-time
Posted 22 days ago
Apple rating
8.0
Based on 677 frontline employees who took The Breakroom Quiz
7th of 30 rated technology retailers
Job description
We're building a massive, real-time search experience that sits at the intersection of Generative AI and Information Retrieval! We make sense of high-volume structured and multimodal data and complex behavioral signals which deliver results that feel instant and relevant while still being private.
Join our team as a ML Search Engineering Manager and take part in this rare opportunity to shape a user-facing product that millions of Apple customers rely on every day!
Description
We are looking for a Search Engineering Manager & Lead to serve as both the senior technical
authority and the people leader for our search team. You'll own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, from query understanding and hybrid retrieval through ranking and evaluation, and you'll also build, grow, and lead the team of search engineers who bring that roadmap to life.
This is a hands-on leadership role with dual scope: you set the technical vision and personally shape the hardest retrieval and ranking decisions, and you also manage, mentor, and grow the engineers executing against it. Your leverage comes equally from what you design and from the team you build.
Minimum Qualifications
MS in Computer Science, Engineering, or a related technical field, or equivalent experience. PhD preferred.
12+ years of experience in Machine Learning, Data Science, or Software Engineering, with a significant focus on search infrastructure and information retrieval, including at least 5 years operating in a technical leadership or engineering management capacity
Proven experience leading and managing engineers, including hiring, performance management, and technical mentorship of senior and staff ICs.
Track record of leading the architecture of large-scale search systems from design through production.
Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
Experience designing offline evaluation frameworks and online A/B testing methodology to validate search relevance and ranking quality.
Experience with vector databases (Milvus, Qdrant, Pinecone, or FAISS).
Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar stacks.
Experience with cloud environments (AWS or GCP), containerization (Docker, Kubernetes), and streaming platforms (Kafka or comparable brokers).
Excellent written and verbal communication, with the ability to align engineers, partner teams, and senior leadership around a shared technical direction.
Strong proficiency in a systems language such as Go or C++, with working proficiency in Java or Python
Deep familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, or similar) and ML system design, model lifecycle, and experimentation pipelines.
Extensive experience with large datasets, data processing pipelines (Spark, Flink), and scalable architectures.
Working knowledge of data privacy principles (e.g., data minimization, privacy-preserving techniques) and experience applying them to systems that use user behavioral signals.
Experience implementing safety guardrails for generative AI outputs, including hallucination mitigation, harmful-content filtering, and red-teaming or adversarial evaluation practices.
Preferred Qualifications
Published work or patents in search systems, information retrieval, or related ML fields.
Strong foundation in deep learning architectures for search and retrieval (transformers, graph neural networks, learned sparse representations).
Exposure to multi-objective optimization in search (relevance, diversity, freshness, fairness).
Track record of scaling engineering teams and modernizing infrastructure with measurable cost and reliability improvements.
Join our team as a ML Search Engineering Manager and take part in this rare opportunity to shape a user-facing product that millions of Apple customers rely on every day!
Description
We are looking for a Search Engineering Manager & Lead to serve as both the senior technical
authority and the people leader for our search team. You'll own the architecture and long-term technical roadmap for large-scale, low-latency search infrastructure, from query understanding and hybrid retrieval through ranking and evaluation, and you'll also build, grow, and lead the team of search engineers who bring that roadmap to life.
This is a hands-on leadership role with dual scope: you set the technical vision and personally shape the hardest retrieval and ranking decisions, and you also manage, mentor, and grow the engineers executing against it. Your leverage comes equally from what you design and from the team you build.
Minimum Qualifications
MS in Computer Science, Engineering, or a related technical field, or equivalent experience. PhD preferred.
12+ years of experience in Machine Learning, Data Science, or Software Engineering, with a significant focus on search infrastructure and information retrieval, including at least 5 years operating in a technical leadership or engineering management capacity
Proven experience leading and managing engineers, including hiring, performance management, and technical mentorship of senior and staff ICs.
Track record of leading the architecture of large-scale search systems from design through production.
Deep understanding of information retrieval, ranking algorithms, and user modeling techniques.
Experience designing offline evaluation frameworks and online A/B testing methodology to validate search relevance and ranking quality.
Experience with vector databases (Milvus, Qdrant, Pinecone, or FAISS).
Experience with search infrastructure such as OpenSearch, Elasticsearch, or similar stacks.
Experience with cloud environments (AWS or GCP), containerization (Docker, Kubernetes), and streaming platforms (Kafka or comparable brokers).
Excellent written and verbal communication, with the ability to align engineers, partner teams, and senior leadership around a shared technical direction.
Strong proficiency in a systems language such as Go or C++, with working proficiency in Java or Python
Deep familiarity with ML frameworks (TensorFlow, PyTorch, XGBoost, or similar) and ML system design, model lifecycle, and experimentation pipelines.
Extensive experience with large datasets, data processing pipelines (Spark, Flink), and scalable architectures.
Working knowledge of data privacy principles (e.g., data minimization, privacy-preserving techniques) and experience applying them to systems that use user behavioral signals.
Experience implementing safety guardrails for generative AI outputs, including hallucination mitigation, harmful-content filtering, and red-teaming or adversarial evaluation practices.
Preferred Qualifications
Published work or patents in search systems, information retrieval, or related ML fields.
Strong foundation in deep learning architectures for search and retrieval (transformers, graph neural networks, learned sparse representations).
Exposure to multi-objective optimization in search (relevance, diversity, freshness, fairness).
Track record of scaling engineering teams and modernizing infrastructure with measurable cost and reliability improvements.
About Apple
Sourced by ZipRecruiter
Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.
Industry
Computer and electronic product manufacturing
Company size
10,000+ Employees
Headquarters location
Cupertino, CA, US
Year founded
1976