Machine Learning Engineer; Agents & LLMs

Genuinely fascinated by what large language models (LLMS) can do, not only as standalone systems but as the foundation of new software experiences built by developers? Are you someone who is excited by the challenge of enabling the next 100 million developers to build AI Agents faster and with less toil? Are you eager to bridge the gap between research and deployment?

Then this role is for you.

Katanemo is a research and development company building the open source stack for AI agents; our work sits at the edge of science and application - building open source tools, infrastructure solutions and experimenting in real-world customer scenarios to turn ideas into technologies others can rely on at scale.

We believe the network is the most critical insertion point in the modern AI stack for agents. Control flow, environment state, agent capabilities all move as text across the wire. By making that traffic observable and programmable, the network becomes the foundation for reliability, routing, and reinforcement learning. But that’s just the beginning. This viewpoint gives us a natural path into adjacent scenarios such as memory, evaluation, and agent coordination.

Over time, we aim to provide developers with the full set of infrastructure primitives needed to build production-grade agents—so they can focus on modeling business processes rather than wrestling with brittle frameworks or low-level plumbing.

Our first product, Plano, is a high-performance proxy designed for agents. Plano pulls rote plumbing out of your framework so you can stay focused on what matters most: the core product logic of your agentic applications. Plano is backed by industry-leading LLM research and built on Envoy by its core contributors, who built critical infrastructure at scale for modern workloads.

We’re looking for an Applied ML Engineer with deep intuition and working experience in LLMs, embedding models, re-ranking models, and evaluation techniques. This role is not “pure research” but sits right at the interface of research and deployment. You’ll work closely with research scientists to translate experimental techniques into production systems, and you’ll also play a direct role in customer deployments—helping adapt, tune, and evaluate models in real-world scenarios to improve product performance.

If you thrive at the edge of research and application, and you want to help define the agentic stack from the ground up, this is the role for you.

• Work on technologies core to modern agents: LLMs, embedding models, re-rankers, and evaluation systems
• Translate research prototypes into robust, scalable production systems
• Design and maintain ML training, fine-tuning, and deployment pipelines
• Rapidly experiment and iterate, even when data is imperfect or incomplete
• Run experiments, analyze results, and adapt models in real-world customer deployments
• Fine-tune models for specific domains and scenarios
• Optimize inference for performance and reliability in distributed and cloud environments
• Collaborate with research scientists to bring novel techniques into production
• Improve infrastructure to support fast-paced experimentation and customer-facing innovation

• A fearless, hands-on attitude and willingness to take on bold ML challenges
• MS/PhD in Computer Science (or related field). Bonus: specialization in NLP or NLU
• 5+ years of professional experience building and scaling ML systems
• Strong experience with LLMs, embeddings, re-ranking, and evaluation methods
• Solid understanding of modern ML techniques: transfer learning, multi-task learning, reinforcement learning, unsupervised/semi-supervised approaches
• Strong Python skills and experience with production-level codebases
• Expertise with ML frameworks (PyTorch, Tensor Flow) and deployment pipelines
• Proven ability to design, ship, and support ML-powered products in production
• Comfort collaborating with scientists, engineers, and customers, and explaining complex techniques in plain language

Qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, ancestry, sex, gender, gender identity, sexual orientation, marital status, age, physical or mental disability or medical condition, genetic information, veteran status, or any other consideration made unlawful by federal, state, or local laws.