Machine Learning Manager Applied AI & LLM Systems

About Curately

Curately 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 matching
• automated candidate screening
• conversational recruiting agents
• semantic search across talent datasets
• intelligent sourcing and recruiter copilots

We 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 Overview

As 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 engines
• semantic search across large talent datasets
• LLM-powered screening and evaluation systems
• conversational recruiting agents
• AI-driven sourcing automation

You 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 Youll Build

Examples of systems you may work on include:

• AI ranking systems that match candidates to jobs in real time
• hybrid retrieval pipelines combining embeddings and structured signals
• automated candidate screening and evaluation models
• conversational AI recruiters that interact with candidates
• recruiter copilots that assist sourcing and outreach workflows

Your work will directly impact how companies discover and evaluate talent at scale.

Key Responsibilities

Lead Applied AI Strategy

• Define Curatelys applied AI roadmap for candidate matching, screening, and sourcing
• Evaluate emerging AI models and determine where they provide real product value
• Translate AI capabilities into scalable product features

Build Production AI Systems

Design and deploy systems such as:

• candidate-job matching algorithms

• learning-to-rank models for candidate relevance
• semantic retrieval pipelines
• LLM-powered evaluation and screening workflows

You will design systems that combine:

• embeddings
• structured signals
• machine learning models
• LLM reasoning

to deliver accurate results at scale.

Develop Retrieval and Ranking Systems

• Build retrieval pipelines for candidate and job data
• Design hybrid search architectures combining vector and structured search
• Develop ranking models that improve candidate relevance
• Continuously improve ranking quality using recruiter feedback and hiring outcomes

Own AI Evaluation and Model Quality

• Define evaluation metrics for AI matching and screening systems
• Build automated evaluation pipelines for LLM outputs
• Implement human-in-the-loop evaluation workflows
• Run experimentation and A/B testing to measure model impact

Build Data Flywheels

• Design feedback loops that improve model quality over time
• Develop data pipelines for training and evaluation datasets
• Build infrastructure for continuous model improvement

Lead the Data Science / ML Team

• Hire and mentor data scientists and machine learning engineers
• Establish experimentation and model development best practices
• Partner with engineering teams to productionize models

Required Qualifications

Experience

• 7+ years of experience in machine learning or data science
• 2+ years leading ML or data science teams
• Proven experience building machine learning systems deployed in production

Candidates should have experience designing and deploying AI systems used in real-world applications, including architecture, evaluation metrics, and deployment.

Applied Machine Learning

Strong experience with:

• ranking or recommendation systems
• search or retrieval systems
• feature engineering and model evaluation
• real-world ML pipelines

LLM Systems

Hands-on experience building systems using:

• Retrieval Augmented Generation (RAG)
• embeddings and semantic search

  Looking to get Placed? Try our Placement Guarantee Plan

  Get Hired
• agentic AI workflows
• LLM evaluation frameworks

Experience with models such as:

• OpenAI GPT models
• Claude
• Gemini
• open-source LLMs (Llama, Mistral)

Technical Skills

Machine Learning

• Python
• PyTorch or TensorFlow
• classical ML and deep learning techniques

LLM Infrastructure

• vector databases
• embedding pipelines
• LangChain / LlamaIndex or similar frameworks

MLOps

• model deployment and versioning
• monitoring and evaluation pipelines
• scalable inference systems

Data Systems

• SQL / Postgres
• distributed data pipelines
• large-scale datasets

Cloud Infrastructure

• AWS / GCP / Azure
• containerized deployment
• microservices architectures

Nice to Have

• experience building ranking or recommendation systems
• experience with knowledge graphs or skill ontologies
• experience in HR tech or recruiting platforms
• experience building multi-agent AI systems
• experience with human-in-the-loop AI workflows

Why This Role Is Exciting

You will help build the AI intelligence layer for the future of recruiting.

You will work on real-world AI systems that:

• influence hiring decisions
• operate on large-scale talent datasets
• combine LLM reasoning with structured ML models
• power enterprise recruiting workflows

This role offers the opportunity to build AI systems that directly impact how companies hire talent worldwide.