Netflix

2 months ago

Distributed Systems Engineer 6 - Decisioning & Optimization

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Own the technical direction for the Decisioning & Optimization team within Netflix Ads, driving architecture reviews, incident leadership, capacity planning, and scaling. Build and evolve real-time ad decisioning and optimization systems—multi-stage auctions, ranking/scoring, bidding, pacing, and goal-based delivery optimization—while scaling hot-path ML model serving to meet strict latency/throughput requirements. Partner with Science and Platform teams and develop simulation/testing frameworks for safe marketplace rollout.

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: IT Operations (Systems/Network Admin)

Seniority: Mid level

Key Responsibilities

•Own the technical direction of the Decisioning & Optimization team, including architecture reviews, incident leadership, capacity planning, and scaling.
•Architect and evolve the real-time ad decisioning optimization path, including multi-stage auction mechanics, ranking/scoring, bidding, and pacing.
•Scale ad model serving infrastructure for dozens of concurrent hot-path ML models with sub-20ms P99 inference, including routing, lifecycle management, fallbacks, and calibration serving.
•Partner with Science and Platform teams to ensure model productionization and algorithm deployment.
•Build simulation/containerized testing frameworks for offline validation and rollout of marketplace changes, and implement real-time pacing and goal-based delivery optimization systems.

Pay and Benefits

Salary: USD 499,000 - 900,000 annually

Equity and Bonus:Equity

Perks:Health Insurance401kEquityDisability ProgramsPaid LeaveFlexible TimeHsaLife Insurance

Key Requirements

•10+ years building distributed systems and backend services at large scale, plus 3+ years in the ads domain.
•Deep experience scaling real-time ML inference on the hot path at high QPS with sub-20ms P99 latency, including model deployment pipelines and fallback strategies.
•Built and operated core ad tech systems such as ad servers, bidders, pacers, or ranking/scoring components.
•Designed APIs, platform abstractions, and data models that enable interoperability across multi-team ads platforms.
•Demonstrated technical leadership across multiple teams, setting architectural direction and influencing cross-functional roadmaps.

Experience:10+ yearsDistributed systemsBackend servicesAds domainML model servingAd tech

Skills:Technical leadershipIncident leadershipCross-functional collaborationReliability focusPlatform thinking

Tech Stack:APIsModel deployment pipelinesA/B testingContainerized testingSimulation frameworksReal-time inference

Company Brief

Netflix

Global subscription streaming service that produces and distributes movies, TV series, and games across multiple devices, operating worldwide with extensive original content and localized offerings.

Industry: Streaming Platforms

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Los Gatos, United States

Founded: 1997

Glassdoor: 4.1

Website LinkedIn Glassdoor

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Netflix

2 months ago

Distributed Systems Engineer 6 - Decisioning & Optimization

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 22 hours agoStatus: Live

Job Summary

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: IT Operations (Systems/Network Admin)

Seniority: Mid level

Key Responsibilities

•Own the technical direction of the Decisioning & Optimization team, including architecture reviews, incident leadership, capacity planning, and scaling.
•Architect and evolve the real-time ad decisioning optimization path, including multi-stage auction mechanics, ranking/scoring, bidding, and pacing.
•Scale ad model serving infrastructure for dozens of concurrent hot-path ML models with sub-20ms P99 inference, including routing, lifecycle management, fallbacks, and calibration serving.
•Partner with Science and Platform teams to ensure model productionization and algorithm deployment.
•Build simulation/containerized testing frameworks for offline validation and rollout of marketplace changes, and implement real-time pacing and goal-based delivery optimization systems.

Pay and Benefits

Salary: USD 499,000 - 900,000 annually

Equity and Bonus:Equity

Perks:Health Insurance401kEquityDisability ProgramsPaid LeaveFlexible TimeHsaLife Insurance

Key Requirements

•10+ years building distributed systems and backend services at large scale, plus 3+ years in the ads domain.
•Deep experience scaling real-time ML inference on the hot path at high QPS with sub-20ms P99 latency, including model deployment pipelines and fallback strategies.
•Built and operated core ad tech systems such as ad servers, bidders, pacers, or ranking/scoring components.
•Designed APIs, platform abstractions, and data models that enable interoperability across multi-team ads platforms.
•Demonstrated technical leadership across multiple teams, setting architectural direction and influencing cross-functional roadmaps.

Experience:10+ yearsDistributed systemsBackend servicesAds domainML model servingAd tech

Skills:Technical leadershipIncident leadershipCross-functional collaborationReliability focusPlatform thinking

Tech Stack:APIsModel deployment pipelinesA/B testingContainerized testingSimulation frameworksReal-time inference

Company Brief

Netflix

Global subscription streaming service that produces and distributes movies, TV series, and games across multiple devices, operating worldwide with extensive original content and localized offerings.

Industry: Streaming Platforms

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Los Gatos, United States

Founded: 1997

Glassdoor: 4.1

Website LinkedIn Glassdoor