Yale University
Yale University

AI/ML Engineer

Remote • Posted 1 day ago • Updated 9 hours ago
Full Time
Remote
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Robotic Process Automation
  • Reporting
  • Research
  • Translation
  • Transcription
  • Decision-making
  • Data Processing
  • System Integration
  • Systems Design
  • Quality Assurance
  • Benchmarking
  • GPU
  • SQL
  • RDBMS
  • PostgreSQL
  • Language Models
  • Open Source
  • Graph Databases
  • Neo4j
  • Apache HTTP Server
  • Version Control
  • Git
  • Docker
  • Large Language Models (LLMs)
  • API
  • Data Structure
  • Algorithms
  • Testing
  • Evaluation
  • Python
  • Middleware
  • Artificial Intelligence
  • Machine Learning (ML)
  • Collaboration
  • Systems Architecture
  • Workflow
  • Cloud Computing
  • Distributed Computing
  • Thread
  • Servers
  • React.js
  • Microsoft
  • Interfaces
  • Data Analysis
  • Unstructured Data
  • Natural Language Processing
  • Data Science
  • Rust
  • Programming Languages

Summary

Job Title: AI/ML Engineer

Duration: 5 years (07/01/2026 to 06/30/2031)

Location: Remote

Interview Mode: MS Teams or Zoom

Job Summary:

The AI/ML Software Engineer will build software tools that incorporate AI/ML techniques to automate narrowly defined tasks with high accuracy, assist internal users with their job functions, and improve the experience external users have when interacting with the Maryland Judiciary. This includes, but is not limited to, RPA work, building or refining chatbots, incorporating AI/ML into reporting tools, building llm agents for knowledge retrieval, deep research, translation, transcription, redaction, document analysis, document generation, agentic coding, and data processing.

Key Responsibilities:
  • System Design & Collaboration:
  • Work within established constraints regarding infrastructure, programming languages, and model selection
  • Contribute to technical decision-making related to data processing, retrieval strategies, and system integration
  • Collaborate with team members to define agent architectures, workflows, and system design decisions
  • Evaluate and select appropriate approaches for given tasks, including determining when to use LLM-based versus non-LLM techniques
  • Designing and building software systems that integrate AI/ML techniques to automate tasks, assist internal users, and improve user-facing services.
  • Testing, Evaluation, and Quality Assurance:
  • Assist in the design and implementation of testing and evaluation pipelines for AI/ML systems
  • Develop unit and integration tests for AI-enabled workflows and data pipelines
  • Generate and utilize synthetic data to support evaluation and benchmarking efforts
  • Contribute to improving system performance, including accuracy, latency, and cost efficiency
  • Deployment & Operations:
  • Support deployment of AI/ML applications within a hybrid cloud environment
  • Work with containerized applications to ensure reliable deployment and updates.
  • Optimize systems for environments with limited computational resources, including minimal GPU availability
  • General Responsibilities:
  • Deliver production-grade systems aligned with defined requirements, while supporting iterative improvement of evolving tools
  • Document system designs, workflows, and technical decisions as required
  • Stay informed on relevant advancements in AI/ML and apply them where appropriate within project constraints

Experience with:
  • SQL and relational database systems (e.g., PostgreSQL)
  • Fine-tuning small language models or embedding models
  • Contributing to or maintaining open-source software projects
  • Graph databases or graph extensions (e.g., Neo4j, Apache AGE)
  • Designing and implementing multi-agent or task-oriented AI systems
  • Embedding models, vector similarity, re-ranking, and graph retrieval techniques in RAG systems
  • Version control systems (e.g., Git), containerization technologies (e.g., Docker), and service-oriented architectures
  • Collaborating with large language models (LLMs), including both API-based integration and local deployment
  • Validating AI-generated outputs, mitigating hallucinations, and integrating AI tools into production service pipelines

Ability to:
  • Understand data structures, algorithms, and clean coding principles
  • Select and apply appropriate techniques (LLM and non-LLM) based on task requirements
  • Develop and improve testing and evaluation pipelines for AI systems, including use of synthetic data
  • Demonstrate proficiency in Python, including the ability to develop production-grade backend services, APIs, middleware, and data pipelines.
  • Design and implement AI/ML systems that operate effectively on complex, inconsistent, or evolving datasets while balancing accuracy, latency, and cost (token consumption)
  • Collaborate with team members to define system architecture, agent workflows,anddata pipelines while working in constrained environments, including limited GPUavailability and predefined infrastructure

Knowledge of:
  • Hybrid cloud environments and distributed system considerations
  • Threading, asynchronous processing, and queues in backend servers
  • React and Microsoft Teams Toolkit for developing chatbot user interfaces
  • Non-llm data analysis techniques for structured, semi-structured, and unstructured data
  • Classical natural language processing (NLP) techniques in addition to LLM-based approaches
  • Data science and LLM-related libraries in Rust or other performance-oriented programming languages
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: RTL939169
  • Position Id: 37578e3f3eb1072d73ba2249896ff1f6
  • Posted 1 day ago
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