Joist AI is looking for a Machine Learning Engineer to lead the development of Agentic AI applications that transform how organizations interact with intelligent automation. You will design and deploy cutting-edge Generative AI models that power intelligent document automation and sophisticated agent-based workflows.

What You'll Do

Build sophisticated agentic AI applications that enable autonomous decision-making and action execution
Optimize and fine-tune large language models (LLMs) for specific use cases and performance requirements
Develop novel approaches to agent reasoning, planning, and tool usage
Build and maintain robust ML pipelines ensuring high performance, scalability, and reliability in production
Implement comprehensive evaluation frameworks to assess the effectiveness of generative models and agent-based solutions
Design monitoring and feedback systems that enable continuous model improvement
Optimize inference performance and cost efficiency at scale
Stay ahead of AI advancements, rapidly prototyping and implementing novel solutions
Experiment with emerging agentic frameworks and contribute to the evolution of autonomous AI systems
Collaborate with the research community through open-source contributions and knowledge sharing
Drive technical innovation that gives Joist AI competitive advantages
Partner with Product and Engineering teams to integrate AI solutions seamlessly into our platform
Work with Product Marketing to translate technical capabilities into user-facing value propositions
Collaborate with Customer Success to understand real-world usage patterns and optimization opportunities
Mentor other engineers and contribute to building our AI engineering culture

What We're Looking For

Degree in Computer Science, Mathematics, AI, or related technical field
5+ years of Machine Learning experience with recent focus on generative AI and autonomous agents
Strong expertise in deep learning frameworks (PyTorch or TensorFlow)
Hands-on experience with LLMs (OpenAI, Claude, Mistral, Llama) and diffusion models
Proficiency in Python and experience with agentic libraries (Langraph, LlamaIndex, Crew AI, etc.)
Deep understanding of RAG, embeddings, and vector databases (FAISS, Pinecone, Weaviate)
Experience with fine-tuning LLMs, prompt engineering, and reinforcement learning techniques
Proven track record building AI-driven applications with APIs, cloud platforms, and microservices
Strong problem-solving skills with ability to tackle open-ended, ambiguous challenges independently
Passion for solving complex problems and learning cutting-edge technologies
Strong sense of ownership and pride in delivering high-quality solutions
Excellent collaboration skills and thrives in team environments
Care deeply about scalability, reliability, and production-ready systems

Nice to Have

Deep expertise in NLP, computer vision, or multi-modal AI applications
Production experience deploying AI models on AWS (Lambda, Step Functions, S3, RDS)
Strong MLOps practices including model monitoring, A/B testing, and continuous improvement
Open-source contributions, Kaggle competitions, or published research in GenAI
Experience with agent frameworks and tool-calling mechanisms
Background in distributed systems and high-performance computing