Sentient Foundation

Machine Learning Engineer

• Reproduce and evaluate research methods using open-weight and API-accessible models
• Design evaluation datasets, probes, scoring methods, baselines, calibration tests, and experiment harnesses
• Work with model weights, logits, hidden states, activations, model APIs, and inference infrastructure
• Build and extend evaluation infrastructure, including runners, judges, persistence, experiment orchestration, and reporting
• Turn research workflows into product experiences, including experiment configuration, runs, traces, comparisons, reports, and review workflows
• Investigate verification methods under fine-tuning, merging, quantization, distillation, safety removal, and deliberate evasion
• Design controlled experiments that separate meaningful signals from artifacts or confounders
• Write technical reports distinguishing measured evidence, interpretation, and hypotheses
• Ship production-quality systems with APIs, background jobs, observability, testing, and documentation
• Reproduce a published model-provenance or verification method within six months
• Build a repeatable model-verification runner with versioned inputs, artifacts, metrics, and reports
• Add a verification workflow to Construct and make it accessible through the Eldros UI
• Run controlled experiments across base, fine-tuned, merged, quantized, and distilled models
• Improve understanding of when verification methods succeed, fail, and why

Applied ML Engineer