Elastic3 days ago
Principal Product Manager II, AI/Vectors
RUB 16,642–26,325per month · before tax
MARKET
65,900 ₽median for this role
AI Researcher · 37 jobs with disclosed pay
5,600half of the offers: 16,833–78,900143,300
Below market · above 24% of offers
United States
Responsibilities
- 01Conduct market research to uncover emerging trends in search/vector databases as well as SOTA model research in emerging areas to manage unstructured data
- 02Evaluate the competitive landscape to enhance product positioning and develop user personas that guide feature decisions
- 03Collaborate with cross-functional teams to align the product strategy with business goals while prioritizing features based on customer feedback and market demands
- 04Establish long-term product goals and milestones for the vector use cases, document AI and unstructured data management to drive its development
- 05Prioritize features and enhancements based on user feedback and technical feasibility
- 06Allocate resources skillfully to ensure timely updates, while coordinating with engineering teams to validate roadmap assumptions
- 07Communicate updates and expectations clearly to stakeholders and executives
- 08Own the vision, strategy, and multi-quarter roadmap for Elastic's vector database as well as core search use cases, from low-level indexing internals to the developer-facing APIs and SDKs
- 09Partner deeply with engineering and applied research on trade-offs, be a credible technical peer in those conversations
- 10Define and defend the metrics that matter: recall, latency, index size, ingestion throughput, and total cost of ownership
- 11Advance the competitiveness of Elastic AI and Vector products across all deployments - Serverless and Self-managed
- 12Define a unified strategy and roadmap across embedding/reranking models and vector indexing/semantic ingestion, model lifecycle, and end-to-end retrieval quality
- 13Manage the operating cadence
- 14Make sure shared metrics and priorities align the research, cloud, and engineering teams
- 15Address trade-offs where model and index decisions intersect
Requirements
- 01Bachelor's degree in Computer Science, Engineering, or related field
- 02Master's degree in a relevant discipline preferred
- 038+ years in product management, with significant time on technical infrastructure, databases, search, or ML/AI platforms
- 04Good working knowledge of vector search fundamentals including embeddings, ANN indexing, similarity metrics, and trade-offs between recall and latency
- 05Knowledge of hybrid retrieval
- 06Working knowledge of generative models, model fine tuning, model capabilities and document processing for large corpus
- 07Ability to hold your own with engineers on technical topics
- 08Track record of shipping developer- or infrastructure-facing products that were adopted at scale
What we offer
- 01Competitive pay based on the work you do
- 02Company-matched 401k with dollar-for-dollar matching up to 6% of eligible earnings
- 03Range of other benefits offered with a holistic emphasis on employee well-being
- 04Distributed company with diversity driving identity
- 05Ability to balance great work with great life
- 06Elastic stock program participation eligibility