Netflix

1 year ago

Machine Learning Engineer 5 - Decisioning & Optimization

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Build and operate end-to-end ML model serving infrastructure for real-time ad decisioning, including zero-downtime hot-swap, high-QPS inference, and strict latency budgets. Design feature-serving pipelines with low-latency hydration and online/offline consistency, productionize multi-stage scoring and ranking for auction decisions, and run production monitoring for drift, calibration, and regressions. Partner with Data Science and Platform teams and create offline simulation to validate marketplace changes before rollout.

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build and operate end-to-end ML model serving infrastructure for real-time ad decisioning, including publishing, packaging, validation, and zero-downtime deployment/hot-swap.
•Scale inference to support dozens of concurrent models per request at 1M+ QPS while meeting strict latency budgets (batching, CPU/GPU allocation, versioning, fallback tiers).
•Design and optimize the feature serving path, including feature hydration from Chronon and Signal Service and low-latency consistency for online/offline use.
•Productionize scoring and ranking models for multi-stage ad selection and integrate model outputs into auction decisioning.
•Build production monitoring and offline simulation infrastructure to replay traffic against candidate models and validate marketplace changes before rollout.

Pay and Benefits

Salary: USD 466,000 - 750,000 annually

Equity and Bonus:Equity

Perks:Health Insurance401kEquityDisability ProgramsPaid Leave

Key Requirements

•7+ years of software engineering experience with 3+ years focused on ML infrastructure, model serving, or ML platform work in ads or real-time decisioning contexts.
•Build and operate real-time model serving systems at high QPS with sub-20ms latency, including online inference and model hot-swap with canary/shadow rollout.
•Proficiency in Java, Python, or Scala with strong multi-threading, memory management, and performance optimization for latency-critical paths.
•Hands-on ML serving experience including serialization, runtime optimization, and deployment constraints.
•Experience with real-time feature engineering pipelines and production monitoring (drift detection, prediction distribution analysis, calibration, and latency profiling).

Experience:7+ yearsML infrastructureModel servingAdsReal-time decisioning

Skills:Operational excellenceReliabilityObservabilityCapacity planningIncident response

Tech Stack:JavaPythonScalaChrononSignal ServiceCPU/GPUSerializationFeature storesModel registriesModel hot-swapCanary rolloutShadow rolloutBatchingCachingInference latencyDrift detectionCalibrationA/B testingCTV constraintsJVM ecosystem

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

1 year ago

Machine Learning Engineer 5 - Decisioning & Optimization

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Location: New York

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build and operate end-to-end ML model serving infrastructure for real-time ad decisioning, including publishing, packaging, validation, and zero-downtime deployment/hot-swap.
•Scale inference to support dozens of concurrent models per request at 1M+ QPS while meeting strict latency budgets (batching, CPU/GPU allocation, versioning, fallback tiers).
•Design and optimize the feature serving path, including feature hydration from Chronon and Signal Service and low-latency consistency for online/offline use.
•Productionize scoring and ranking models for multi-stage ad selection and integrate model outputs into auction decisioning.
•Build production monitoring and offline simulation infrastructure to replay traffic against candidate models and validate marketplace changes before rollout.

Pay and Benefits

Salary: USD 466,000 - 750,000 annually

Equity and Bonus:Equity

Perks:Health Insurance401kEquityDisability ProgramsPaid Leave

Key Requirements

•7+ years of software engineering experience with 3+ years focused on ML infrastructure, model serving, or ML platform work in ads or real-time decisioning contexts.
•Build and operate real-time model serving systems at high QPS with sub-20ms latency, including online inference and model hot-swap with canary/shadow rollout.
•Proficiency in Java, Python, or Scala with strong multi-threading, memory management, and performance optimization for latency-critical paths.
•Hands-on ML serving experience including serialization, runtime optimization, and deployment constraints.
•Experience with real-time feature engineering pipelines and production monitoring (drift detection, prediction distribution analysis, calibration, and latency profiling).

Experience:7+ yearsML infrastructureModel servingAdsReal-time decisioning

Skills:Operational excellenceReliabilityObservabilityCapacity planningIncident response

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