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Qdrant Jobs (NOW HIRING)

Paramount

Machine Learning Engineer, Entry

Manhattan, NY · On-site

$124K - $186K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends. * Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a ...

Paramount

Machine Learning Engineer, Entry

Manhattan, NY

$124K - $186K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends. * Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a ...

Ombud

Senior Full Stack AI Engineer

Denver, CO · Hybrid

We use LlamaIndex and Qdrant today; we will evolve the stack as needed. * LLM integration with Anthropic Claude (primary), with multi-model routing where it makes sense. * Evaluation pipelines: how ...

ViacomCBS

Machine Learning Engineer, Entry

Manhattan, NY · On-site

$124K - $186K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends. * Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a ...

New

ConglomerateIT LLC

Agentic AI Engineer

Georgia, VT · Remote

RAG + a vector DB (Pinecone, Weaviate, pgvector, Qdrant, or similar) * Hands-on LLM API experience (OpenAI / Anthropic / Gemini) * One cloud (AWS, Azure, or GCP) * Demonstrated experience taking at ...

Paramount

Machine Learning Engineer, Entry

West Hollywood, CA

$124K - $186K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends. * Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a ...

Paramount

Machine Learning Engineer, Entry

Burbank, CA · On-site

$124K - $186K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends. * Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a ...

ConglomerateIT

Agentic AI Engineer

Georgia, VT · On-site

RAG + a vector DB (Pinecone, Weaviate, pgvector, Qdrant, or similar) * Hands-on LLM API experience (OpenAI / Anthropic / Gemini) * One cloud (AWS, Azure, or Google Cloud Platform) * Demonstrated ...

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Showing results 41-60

All JobsQdrant Jobs

Qdrant information

What is the difference between Qdrant vs Data Scientist?

AspectQdrantData Scientist
Required CredentialsTechnical certifications, knowledge of vector databasesDegree in Data Science, Statistics, or related field
Work EnvironmentTech companies, startups, AI-focused firmsResearch labs, tech companies, consulting firms
Industry UsageAI, machine learning, data storageData 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.

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Qdrant jobs near you
Infographic showing various Qdrant job openings in the United States as of August 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 79% Physical, 3% Hybrid, and 18% Remote job distribution.

Principal Software Engineer, AI Platform Engineering

Saviynt

Milpitas, CA • On-site

$274K - $304K/yr

Full-time

Re-posted 20 days ago

Job description

ABOUT SAVIYNT
Saviynt is a leader in identity security, delivering an AI-powered platform that governs and secures access to applications, data, and business processes for global enterprises and government institutions. Built for the AI era, Saviynt helps organizations move faster - securely and compliantly.
ABOUT THE ROLE
You set the architectural direction for how training data flows, evolves, and is governed across the AI Platform. You define the standards ML engineers and scientists build on, and ensure every training signal is tenant-isolated, PII-free, and traceable from source to model.
WHAT YOU'LL OWN
  • AI Data Lake on GCS: bucket layout, raw - silver - gold tier separation, CMEK encryption, lifecycle rules
  • Batch pipelines: Spark on Dataproc for TB-scale feature backfills, Iceberg compaction, and daily S3-GCS incremental sync
  • Streaming pipelines: Apache Beam on Dataflow for sub-5-min CDC ingestion with exactly-once semantics and PII assertion gates
  • Schema registry: Avro / Protobuf schema versioning, compatibility modes, and migration playbooks for safe schema evolution
  • Orchestration: Flyte as primary DAG layer - task authoring standards, domain isolation, retry policies, DataCatalog memoization; evaluate Kubeflow Pipelines where relevant
  • Multi-tenancy: strict per-tenant GCS prefix isolation, quota policies, and cross-tenant contamination validation
  • Data Anonymizer and Data Labeler microservices: strip PII and attach ML labels before signals leave each customer environment
  • Feature store: Feast offline (GCS Parquet) and online (Redis) with point-in-time correctness and < 0.1% consistency SLA
  • Vector database: operate Pgvector (Cloud SQL) for POC and Qdrant on GKE for production-scale embedding storage; design index strategies (IVFFlat, HNSW) and manage ANN query latency SLAs
  • RAG data pipeline: build embedding generation pipelines that chunk, encode, and upsert document embeddings into the vector store; own the data refresh cadence and staleness SLAs for retrieval context
  • Service APIs: expose data platform services (feature serving, embedding upsert, schema validation) over HTTPS with mTLS and gRPC where low-latency streaming is required
  • Synthetic data pipelines for dev/staging where real customer data is not permitted
  • Data quality gates: Great Expectations / dbt checks as Flyte tasks, blocking on schema and PII-absence failures

YOU'LL THRIVE HERE IF YOU HAVE
  • 1+ years of experience as a Principal SWE at a SaaS company
  • Demonstrated principal impact: platform standards you defined adopted org-wide, or major cross-team pipeline/schema migrations you led
  • Data lake ownership (essential): you have designed and operated a production data lake end-to-end - storage layout, partitioning strategy, tiered retention (hot/warm/cold), table format (Iceberg or Delta Lake), compaction, and access control; not just consumed one
  • Deep Spark (PySpark / Scala): executor tuning, shuffle diagnosis, Iceberg table maintenance
  • Hands-on Beam / Dataflow: windowing, exactly-once, side inputs, autoscaling
  • Schema registry experience: Protobuf / Avro compatibility rules, breaking-change migrations in production
  • Orchestration at scale: Flyte, Kubeflow Pipelines, Airflow, or Prefect - operated in production, ideally benchmarked two
  • Multi-tenant data architecture: per-tenant isolation as a hard requirement, not a post-hoc concern
  • Feature store operations: Feast or Tecton, point-in-time joins, online/offline consistency
  • Vector databases: Pgvector or Qdrant in production - index tuning, ANN search, embedding upsert pipelines
  • RAG data fundamentals: chunking strategies, embedding model selection, retrieval quality evaluation, and context freshness management
  • API transport: gRPC and HTTPS/mTLS for service-to-service communication; comfortable defining proto contracts and managing certificate lifecycle
  • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience or equivalent military experience

NICE TO HAVE
  • Differential privacy or k-anonymity for ML training datasets
  • Open source contributions: Feast, Great Expectations, Apache Beam, or dbt
  • Familiarity with IAM / access governance data: entitlements, provisioning events, access graphs
  • Iceberg or Delta Lake at petabyte scale

WHY JOIN SAVIYNT
  • Work on a large-scale, Kubernetes-based SaaS platform
  • Solve challenging cloud and reliability problems at scale
  • Collaborate with strong engineers in a reliability-focused culture
  • Competitive compensation, benefits, and growth opportunities

SECURITY & COMPLIANCE
This role requires adherence to Saviynt's information security and privacy policies, including annual security training.
$274,000 - $304,000 a year
We offer you a competitive total rewards package, learning and tremendous opportunities to grow and advance in your career. At Saviynt, it is not typical for an individual to be hired at or near the top of the range for their role and final compensation decisions are dependent on many factors including but are not limited to location; skill sets; experience and training; licensure and certifications; and other relevant business and organizational needs. A reasonable estimate of the current range is $240,000 - $260,000 annually.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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