Product Manager, Search Systems | MongoDB | SF or Remote US

The Briefed; San Francisco (CA); Remote; USD 170,000 - 210,000; Full time

Benefits offered by this job

Equity and stock purchase program

Flexible paid time off

Parental leave (20 weeks)

Job summary

MongoDB seeks a seasoned Product Manager for the Search Systems Engineering team in San Francisco to drive the long-term vision for search, vector, and AI-native applications. You will translate customer needs into scalable architecture, collaborate with engineering leadership, and balance innovation with platform health and reliability.

You will partner with SRE and Solutions Architects to implement observability, SLOs/SLIs, and incident response improvements while communicating value to

Qualifications

• 5+ years in infrastructure or distributed systems product management.
• In-depth understanding of CAP theorem, consistency models, and sharding strategies.
• Experience translating technical trade-offs into business value for leadership.
• Ability to drive operational excellence and narrate infra health to executives.

Responsibilities

• Set long-term technical vision for MongoDB Search and AI-native apps.
• Voice of customers; translate needs into scalable, high-performance architecture.
• Balance innovation with production stability and platform health.
• Collaborate with SRE and architects to turn telemetry into improvements.

Skills

Distributed systems PM

CAP theorem & consistency

Observability & SLOs/SLIs

Search & AI-native architectures

Executive storytelling

Tools

Elasticsearch

Solr

Lucene

Vector databases

Job description

Join MongoDB’s Search Systems Engineering team, which builds next-generation database infrastructure for highly performant search and AI applications at enterprise scale. You will act as both strategic partner and technical translator to deeply technical engineering teams, helping them reach operational excellence while anticipating market needs well ahead of current development cycles. This role suits someone who can hold their own in technical debates about consensus protocols while keeping the team focused on customer value.

Key Responsibilities

• Drive the long-term technical vision for MongoDB Search, anticipating emerging patterns in vector, hybrid, and AI-native applications to build conviction around platform investments 12-18 months ahead of market demand
• Act as the voice of customers building mission-critical AI applications, using deep technical discovery to uncover needs around scalability and consistency, and keep the architecture leading in performance and multi-tenancy
• Partner with engineering leadership to translate complex technical trade-offs into business value, balancing innovation velocity with the production stability needed for long-term platform health
• Collaborate with SRE and Solutions Architects to build advanced instrumentation strategies and feedback loops that turn production signals and incident learnings into proactive improvements

Requirements

• 5+ years of product management experience focused on infrastructure or distributed systems
• Mastery of concepts such as the CAP theorem, consistency models, and sharding strategies, with real empathy for engineering work like on-call duty and debugging production incidents
• Ability to operate at the intersection of complex architecture and business strategy, including debating consensus algorithms with engineers
• Track record of driving operational excellence, with strong narrative skills: moving from low-level system metrics to executive stories that connect infrastructure health to business outcomes
• Ability to earn the trust of senior engineers through technical depth and guide architectural decisions without formal authority
• Customer obsession for platform products, designing deeply technical products around the long-term success of customers who build on your abstractions
• Education: Not specified

Skills

Technical Skills

• Distributed systems and infrastructure product management
• CAP theorem, consistency models, and sharding strategies
• Observability, SLOs/SLIs, and incident response
• Search and AI-native application architectures (vector, hybrid retrieval)

Soft Skills

• Influence without formal authority
• Data-driven storytelling and executive communication
• Strategic vision
• Customer focus

Nice to Have

• Direct experience with search technologies (Elasticsearch, Solr, Lucene) or vector databases in production at scale
• Experience with AI/ML infrastructure or platform capabilities that enable AI applications (vector search, embeddings management, hybrid retrieval)

Success Measures

• Month 3: Complete an architectural audit of the search stack, report the top 3 sources of operational toil and observability blind spots, and ship one quick reliability win
• Month 6: Publish official SLOs/SLIs with SRE, deliver a strategic whitepaper on the next 18 months of architectural investment, and roll out high-signal alerting
• Month 12: Show measurable system health gains (e.g., lower p99 latency, better MTTR, or zero capacity-related incidents), take a major infrastructure capability from concept to General Availability, and become the internal authority on search scaling

Benefits

Benefits for eligible U.S.-based employees may include:

• Equity and an employee stock purchase program
• Flexible paid time off
• 20 weeks of fully paid, gender-neutral parental leave
• Fertility and adoption assistance
• 401(k) plan
• Mental health counseling
• Transgender-inclusive health insurance coverage and other health benefits