Machine Learning Engineer
About the role
Founding ML Engineer responsible for models and ML systems powering Moss's real-time retrieval and agent actions, spanning embeddings, retrieval, multilingual understanding, and the Action Layer.
Responsibilities
- 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, including intent understanding, context retrieval, and response quality.
- Help build Moss’s Action Layer, enabling agents to move from retrieving context to executing the right next action.
- Own the full model-development cycle: dataset creation, training, evaluation, optimization, deployment, and iteration.
- Build evaluation pipelines measuring retrieval relevance, multilingual quality, agent outcomes, latency, memory usage, and inference cost.
- Improve model efficiency through distillation, quantization, and CPU/ARM-optimized inference.
- Collaborate with runtime and SDK engineers to ship models across cloud, browser, edge, and device environments.
- Investigate production failure cases and translate insights into better datasets, evaluations, and models.
- Make practical trade-offs about what to train, adapt, or ship.
Qualifications
- Experience training or fine-tuning models and deploying them into production.
- Strong foundations in representation learning, information retrieval, and model evaluation.
- Strong Python skills and ability to write maintainable production code.
- Understanding of how training data, objectives, and evaluation affect real-world model behavior.
- Ability to reason about tradeoffs between quality, latency, memory, and compute.
- Comfort with ambiguous problems and ownership of results.
- Clear communication of experiments, outcomes, and next steps.
Skills
About Moss
Real-time semantic search for Conversational AI
Moss is building the real-time semantic search runtime for conversational and multimodal AI. Our system enables voice agents, copilots, and chat interfaces to retrieve, reason, and respond in sub-10 ms, delivering the responsiveness that makes AI interactions feel truly natural. If you’ve ever built a conversational or voice AI product, you’ve felt the lag. That moment when an agent pauses and the illusion of intelligence breaks. The bottleneck is almost always retrieval. Each query hops across networks and databases, adding delay and cost. Moss eliminates that gap by keeping retrieval close to where the agent runs. Moss runs natively across browsers, mobile devices, and servers with an optimized vector index built in Rust and WebAssembly. It enables teams to build AI products that feel instant, contextual, and adaptive. The experiences that sustain engagement and unlock new kinds of interaction. For our customers, this translates into tangible business value: stronger user retention, higher conversion, and entirely new product categories made possible by real-time understanding. Moss is already powering production pilots across voice AI and developer platforms, achieving sub-10 ms retrieval and 70–90% token savings compared to traditional pipelines.