Machine Learning Engineer

About the role

Founding ML Engineer

Moss is building the retrieval runtime for real-time AI. We help agents access the right knowledge, conversation history, and user context in milliseconds, and use that context to decide what to do next.

We’re looking for a Founding ML Engineer to own the models and machine learning systems behind that experience. You’ll work across embeddings, retrieval, reranking, multilingual understanding, agent intelligence, and our Action Layer; taking ideas from experiments into production.

The work comes with real constraints: limited memory, CPU execution, changing context, multiple languages, and latency budgets that leave little room for error. Your job is to improve intelligence and quality while making the models practical to run.

What You’ll Do

- Train and fine-tune embedding and reranking models for real-world retrieval workloads.

- Build multilingual embedding models that retrieve accurately across languages, regions, and mixed-language conversations.

- Improve the intelligence behind our Founding Agent. From understanding intent and retrieving context to choosing better responses and converting conversations into meaningful outcomes.

- Help build Moss’s Action Layer, enabling agents to move from retrieving context to determining and executing the right next action.

- Own the full model-development cycle: dataset creation, training, evaluation, optimization, deployment, and iteration.

- Build evaluation pipelines that measure retrieval relevance, multilingual quality, agent outcomes, latency, memory usage, and inference cost.

- Improve model efficiency through distillation, quantization, and inference optimization, particularly for CPU and ARM devices.

- Work with runtime and SDK engineers to ship models across cloud, browser, edge, and device environments.

- Investigate production failure cases and turn them into better datasets, evaluations, and models.

- Make practical decisions about what to train, what to adapt, and what to ship.

Core Stack

The work spans:

- Python and deep learning frameworks for training and experimentation.

- Embedding models, rerankers, contrastive learning, and semantic retrieval.

- Multilingual and cross-lingual representation learning.

- Agent evaluation, intent understanding, tool selection, and action prediction.

- Dataset curation, hard-negative mining, synthetic data, and reproducible evaluation.

- Model distillation, quantization, and portable inference.

- Moss’s Rust runtime and SDKs across cloud and on-device environments.

You don’t need to have worked with every part of the stack. You do need to understand how model decisions affect the system running them and the user experience they create.

Your First 90 Days

From day one: Work directly with our models, evaluation pipelines, Founding Agent, and production use cases. Start contributing code and experiments immediately.