Machine Learning Engineer

SynthBee is seeking a Machine Learning Engineer who can take AI models from research to production-integrating agentic AI systems into our product. This role is engineering-focused, working alongside a research counterpart to build and deploy real-world AI automation tools. This is an ideal role for a generalist who has built AI applications before and has strong intuition for AI-especially in deploying AI powered applications and AI agents.

At SynthBee we're building Collaborative Intelligence maximizing the potential of people and computers working together. CI will help humans solve the most important scientific, engineering, design, and creative challenges of our time. And create joy in the process.

What You'll Do

Develop AI-driven reasoning agents and frameworks for automating complex tasks.

Build and optimize RAG (Retrieval-Augmented Generation) pipelines

Distill and fine-tune AI models to improve reasoning, automation, and decision-making.

Deploy AI models into scalable, production-ready systems using Python and cloud-based infrastructure.

Collaborate closely with researchers to operationalize AI advancements into real applications.

Proven experience building and deploying AI applications (ideally in a startup or fast-moving environment).

Hands-on expertise with agentic frameworks (e.g., AutoGen, CrewAI, LangChain, DSPy, or Haystack).

Strong background in AI-powered automation & orchestration workflows.

Experience working with vector databases and retrieval systems (e.g., LlamaIndex, FAISS, Pinecone, Weaviate).

Some experience with fine-tuning AI models is a big plus, but not required.

Solid coding skills in Python, familiarity with cloud deployment tools (AWS, GCP, or Azure).

Who Should Apply?

You've built AI-powered applications-at a startup, in open-source projects, or in a side hustle.

You're excited about agentic AI and building autonomous workflows that solve real problems.

You want to be part of a small, fast-moving team where your work directly impacts the product.

You're comfortable with ambiguity and love figuring things out in a fast-paced startup.