Senior Machine Learning Engineer — Rust, Models & Agent Systems

About the Role

This is a Founding Engineer role at Loci for a scrappy, versatile ML generalist who thrives on owning core systems.

We’re looking for a machine learning engineer to help build an AI application that gives users control over their models, tools, and data. The work spans model training and fine-tuning, local inference, agent workflows, tool use, persistent memory, and retrieval. You’ll help decide what belongs in the model and what belongs in the surrounding software, then build and evaluate both.

You’ll own core systems end to end, from investigation and experimentation through architecture, implementation, delivery, and ongoing reliability. You’ll move between model work and product engineering, identify the highest-impact bottleneck, and build practical solutions with limited time and resources. That means writing excellent Rust code, understanding model behavior, making sound architectural decisions, and helping the team turn AI capabilities into dependable product features.

Compensation

$200,000 annual base salary plus equity. We believe in paying a generous base salary and prefer candidates who value equity and long-term ownership in Loci more than maximizing base pay.

What You’ll Do

• 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.
• Design the boundaries between models and the agent harness: the software that manages context, tools, state, and execution.
• Build reliable, maintainable Rust software for inference, agent workflows, tool execution, memory, and retrieval.
• Work with llama.cpp and related inference engines to integrate models, diagnose compatibility issues, and improve performance.
• Write and maintain system prompts, chat templates, tool definitions, and tool-call handling.
• Build reproducible evaluation workflows for non-deterministic behavior.
• Recommend how the harness should manage context, retrieve information, preserve memory, and recover from errors.
• Teach the team how models and agent systems behave.
• Structure code, experiments, documentation, and tickets for clarity.
• Discuss approaches before implementation and validate changes before merge.

What You’ll Bring

• Broad practical ML judgment and the ability to frame ambiguous product problems.
• Hands-on experience training and fine-tuning language models.
• Strong Rust engineering skills, including ownership, traits, async, concurrency, and error handling.