Machine Learning Manager — Applied AI & LLM Systems

About CuratelyCurately AI is building the next generation of AI-powered recruiting infrastructure.Our platform helps enterprises and staffing firms automate candidate discovery, evaluation, engagement, and hiring workflows using large language models, intelligent ranking systems, and real-time data pipelines.We operate at the intersection of AI, search, and enterprise workflow automation, enabling organizations to hire faster and make better hiring decisions through intelligent systems.Our platform powers capabilities such as:AI candidate-job matchingautomated candidate screeningconversational recruiting agentssemantic search across talent datasetsintelligent sourcing and recruiter copilotsWe are looking for a Machine Learning Manager with deep experience building production AI systems to lead the development of the intelligence layer powering our platform.Role OverviewAs the Machine Learning Manager, you will lead the design and deployment of applied machine learning and LLM-powered systems that power core Curately capabilities.This role focuses on building production AI systems used daily by recruiters and hiring teams, not research prototypes.You will lead a team responsible for building systems such as:candidate-job matching and ranking enginessemantic search across large talent datasetsLLM-powered screening and evaluation systemsconversational recruiting agentsAI-driven sourcing automationYou will work closely with engineering, product, and platform teams to ensure our AI systems are accurate, scalable, explainable, and continuously improving through feedback loops.What You’ll BuildExamples of systems you may work on include:AI ranking systems that match candidates to jobs in real timehybrid retrieval pipelines combining embeddings and structured signalsautomated candidate screening and evaluation modelsconversational AI recruiters that interact with candidatesrecruiter copilots that assist sourcing and outreach workflowsYour work will directly impact how companies discover and evaluate talent at scale.Key ResponsibilitiesLead Applied AI StrategyDefine Curately’s applied AI roadmap for candidate matching, screening, and sourcingEvaluate emerging AI models and determine where they provide real product valueTranslate AI capabilities into scalable product featuresBuild Production AI SystemsDesign and deploy systems such as:candidate-job matching algorithmslearning-to-rank models for candidate relevancesemantic retrieval pipelinesLLM-powered evaluation and screening workflowsYou will design systems that combine:embeddingsstructured signalsmachine learning modelsLLM reasoningto deliver accurate results at scale.Develop Retrieval and Ranking SystemsBuild retrieval pipelines for candidate and job dataDesign hybrid search architectures combining vector and structured searchDevelop ranking models that improve candidate relevanceContinuously improve ranking quality using recruiter feedback and hiring outcomesOwn AI Evaluation and Model QualityDefine evaluation metrics for AI matching and screening systemsBuild automated evaluation pipelines for LLM outputsImplement human-in-the-loop evaluation workflowsRun experimentation and A/B testing to measure model impactBuild Data FlywheelsDesign feedback loops that improve model quality over timeDevelop data pipelines for training and evaluation datasetsBuild infrastructure for continuous model improvementLead the Data Science / ML TeamHire and mentor data scientists and machine learning engineersEstablish experimentation and model development best practicesPartner with engineering teams to productionize modelsRequired QualificationsExperience7+ years of experience in machine learning or data science2+ years leading ML or data science teamsProven experience building machine learning systems deployed in productionCandidates should have experience designing and deploying AI systems used in real-world applications, including architecture, evaluation metrics, and deployment.Applied Machine LearningStrong experience with:ranking or recommendation systemssearch or retrieval systemsfeature engineering and model evaluationreal-world ML pipelinesLLM SystemsHands-on experience building systems using:Retrieval Augmented Generation (RAG)embeddings and semantic searchagentic AI workflowsLLM evaluation frameworksExperience with models such as:OpenAI GPT modelsClaudeGeminiopen-source LLMs (Llama, Mistral)Technical SkillsMachine LearningPythonPyTorch or TensorFlowclassical ML and deep learning techniquesLLM Infrastructurevector databasesembedding pipelinesLangChain / LlamaIndex or similar frameworksMLOpsmodel deployment and versioningmonitoring and evaluation pipelinesscalable inference systemsData SystemsSQL / Postgresdistributed data pipelineslarge-scale datasetsCloud InfrastructureAWS / GCP / Azurecontainerized deploymentmicroservices architecturesNice to Haveexperience building ranking or recommendation systemsexperience with knowledge graphs or skill ontologiesexperience in HR tech or recruiting platformsexperience building multi-agent AI systemsexperience with human-in-the-loop AI workflowsWhy This Role Is ExcitingYou will help build the AI intelligence layer for the future of recruiting.You will work on real-world AI systems that:influence hiring decisionsoperate on large-scale talent datasetscombine LLM reasoning with structured ML modelspower enterprise recruiting workflowsThis role offers the opportunity to build AI systems that directly impact how companies hire talent worldwide.

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