Member of Technical Staff (Machine Learning Engineer, Ranking Quality - Search)

Perplexity
Belgrade +1 more

Who this role is best for

Best suited to senior machine learning engineers with deep expertise in neural ranking or production ranking systems, working in search and recommendation domains.

Best fit for

  • Senior ML engineers with a track record of leading large-scale ranking systems in search or recommendation domains
    — “Proven ownership of a large-scale production ranking system or a substantial class of quality problems.”
  • Individuals with strong cross-team collaboration experience and the ability to drive ambiguous problems independently
    — “Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome.”

Things to consider

  • The role demands significant autonomy in problem ownership and decision-making across cross-functional teams
    — “Ability to drive ambiguous, cross-team problems without continuous task decomposition.”
  • Candidates must have at least five years of industry experience in machine learning and software engineering
    — “Minimum 5 years of relevant industry experience.”

How to stand out

  • Highlight experience in end-to-end ranking system ownership and measurable quality improvements
    — “Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely.”
  • Demonstrate expertise in both neural ranking methods and low-latency production systems
    — “Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.”
  • Emphasize your ability to make trade-offs across quality, latency, cost, and engineering complexity
    — “Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity.”
  • Showcase your experience with model training, evaluation, and deployment for search and ranking tasks
    — “Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate.”
  • Demonstrate experience with feature computation and monitoring in production ranking systems
    — “Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • own ranking-quality problems end to end
  • train and evaluate retrieval, ranking, and classification models
Typical background
5+ years of relevant industry experiencedeep understanding of search or recommender systems

Skills & requirements

Required

Search Quality ImprovementRanking SystemsNeural RankingProduction Ranking SystemsRetrieval ModelsRanking InfrastructureLow-latency Ranking SystemsMachine LearningSoftware Engineering

Preferred

Llm-based ApproachesMulti-stage CascadesMonitoring

Stack & domain

Search QualityRankingMachine LearningNeural RankingProduction Ranking SystemsRetrievalClassificationFeature ComputationLow-latency InferenceMulti-stage CascadesDeploymentMonitoringProblem-solvingTeamworkCommunicationSearchRecommender SystemsRanking Systems

About the role

Original posting from Perplexity via Ashby

Perplexity is seeking an experienced Machine Learning Engineer to improve search quality across the middle and later stages of ranking. We are looking for a strong ranking generalist who can own ambiguous problems end to end and brings exceptional depth in either neural ranking or production ranking systems.

Responsibilities

  • Relentlessly push search quality forward through models, data, evaluation, infrastructure, or any other leverage available.
  • Own ranking-quality problems end to end: define the evaluation, identify the bottleneck, build the solution, and ship it safely.
  • Train and evaluate retrieval, ranking, and classification models, including neural and LLM-based approaches where appropriate.
  • Build and operate ranking infrastructure, including feature computation, low-latency inference, multi-stage cascades, deployment, and monitoring.
  • Make sound trade-offs across quality, latency, reliability, cost, and engineering complexity.
  • Collaborate across Data, AI, Infrastructure, and Product while retaining ownership of the final quality outcome.

Qualifications

  • Deep understanding of search or recommender systems and their evaluation.
  • Proven ownership of a large-scale production ranking system or a substantial class of quality problems.
  • Strong machine-learning and software-engineering skills across data, models, serving, and monitoring.
  • Ability to drive ambiguous, cross-team problems without continuous task decomposition.
  • Exceptional depth in either modern neural ranking methods or low-latency ranking systems and runtime.
  • Minimum 5 years of relevant industry experience.

Source: Perplexity careers (Ashby)

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