Senior Machine Learning Engineer — Rust, Models & Agent Systems

Loci is seeking a founding machine learning engineer to build an AI application that gives users control over their models, tools, and data. The role owns core systems spanning model training and fine-tuning, local inference, agent workflows, tool use, persistent memory, retrieval, and dependable Rust-based product engineering.

Responsibilities:

• Train, fine-tune, and evaluate models against product requirements, with clear objectives, reproducible experiments, and responsible use of training data
• Select models that can support the application’s features within practical limits on memory, latency, compute, and hardware. Establish when a smaller model is sufficient and when a capability requires a different approach
• Design the boundaries between models and the agent harness: the software that manages context, tools, state, and execution. Decide which responsibilities require model judgment and which should be enforced in code
• Build reliable, maintainable Rust software for inference, agent workflows, tool execution, memory, and retrieval, using the type system to make invalid states difficult to express
• Work with llama.cpp and related inference engines to integrate models, diagnose compatibility issues, and improve performance through profiling, quantization, caching, batching, and engine optimizations
• Write and maintain system prompts, chat templates, tool definitions, and tool-call handling. Design how the harness responds to malformed calls, unsupported capabilities, failures, and uncertain model output
• Build reproducible evaluation workflows for non-deterministic behavior that run separately from normal development gates. Measure task success, tool-use reliability, regressions, latency, and resource use, and turn findings into engineering decisions
• Recommend how the harness should manage context, retrieve information, preserve memory, request clarification, recover from errors, and determine when a task is complete
• Teach the team how models and agent systems behave. Explain limitations, share useful research and practices, and help engineers reason about model and harness interactions
• Structure code, experiments, documentation, and tickets so that assumptions, responsibilities, results, and next steps are easy to understand
• Discuss approaches before implementation, explain tradeoffs, communicate progress and blockers clearly, and validate your changes before merge