About Us
At Rope Digital, we don’t just experiment with AI – we ship it.
We’re a globally distributed, fully remote team building intelligent systems that automate real workflows and power real businesses at scale. From MVPs to production-grade platforms, our work reaches users across the world.
If you love moving fast, working with cutting-edge models, and turning research into usable products, you’ll feel right at home here.
About the Role
We’re looking for a Machine Learning Engineer with strong LLM fine-tuning experience to join a fast-moving product team building an intelligent automation system powered by AI agents.
This role is hands-on and impact-driven. You’ll work directly on adapting large language models to real-world tasks—dealing with imperfect data, evolving requirements, and rapid iteration. If you enjoy bridging the gap between ML research and production systems, this role is for you.
What You’ll Do
- Fine-Tune Large Language Models: Apply instruction tuning, LoRA, QLoRA, PEFT, and related techniques to adapt open-source LLMs for task-specific use cases.
- Build Agentic Systems: Design and experiment with prompt strategies, few-shot learning, and agent-based reasoning frameworks to enable intelligent task execution.
- Work Close to Product: Collaborate with engineers, designers, and product owners to turn ML experiments into reliable, user-facing features.
- Experiment & Iterate Fast: Explore new model architectures, training strategies, and evaluation techniques in a research-heavy, fast-feedback environment.
- Ship Production-Grade ML: Help take models from notebooks to scalable systems that actually run in production.
What You Bring
- Strong experience fine-tuning LLMs, including:
- Instruction tuning
- LoRA / QLoRA
- PEFT or similar approaches
- Fluency in Python and hands-on experience with:
- HuggingFace Transformers
- PyTorch
- LangChain and/or LangGraph
- Experience working with open-source models and task-specific datasets
- Solid understanding of prompt tuning, few-shot learning, and agent-style reasoning
- Comfort operating in early-stage or research-heavy environments where data can be messy and iteration is fast
Extra Brownie Points If You Have
- Experience with agent systems such as LangGraph, AutoGen, or custom agent control flows
- Exposure to multimodal models (text + vision, screen understanding, etc.)
- Familiarity with RLHF or feedback-driven training loops
- Knowledge of tool-calling / toolformer-style architectures
- Contributions to open-source ML tools or models
- A strong interest in turning ML research into real, scalable products
Why You’ll Love Working Here
- High Impact Role: You’ll directly shape how real AI agents think, decide, and act
- Freedom to Experiment: Try new ideas, models, and architectures—we value learning and innovation
- Remote-First Culture: Work from anywhere, collaborate globally
- Fast, Focused Team: We move quickly, value ownership, and ship often
- Growth Potential: Competitive contract compensation with the possibility of long-term engagement, equity, or leadership opportunities