Applied ML Engineer

Sentient Foundation

. Reproduce and evaluate research methods using open-weight and API-accessible models .

Posted 9/23/2026full-timeRemote • Singapore, China, United Kingdom, United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong Python engineering skills and hands-on experience with PyTorch and Hugging Face Transformers, alongside a solid understanding of ML evaluation methods and production software development. Capable of designing controlled experiments and building robust evaluation infrastructure while effectively communicating technical findings.

Highest-signal resume keywords

Python Engineering SkillsPyTorch ExperienceHugging Face TransformersML Evaluation MethodsProduction Software Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Dataset DesignStatistical UncertaintyReproducibilityExperiment DesignAPIsAsynchronous JobsTestingDeploymentModel VerificationData Visualization

Soft Skills

Strong Technical JudgmentHigh AgencySense of OwnershipAdaptability in Fast-Moving Environments

Tools & Technologies

ReactTypeScriptPostgreSQLRayVLLMDSPyLiteLLMTemporalExperiment DashboardsGPU Model Serving

Industry Keywords

Model ProvenanceFingerprintingWatermarkingDistillation DetectionRed-TeamingSafety EvaluationsInterpretabilityActivation AnalysisAdversarial Evaluations

Tech Stack

Tools & technologies

JavaScriptNext.jsPostgresPythonPyTorchRayReactTypeScript

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

Strong Python engineering skills and hands-on experience with PyTorch and Hugging Face Transformers
Strong understanding of ML evaluation, including dataset design, baselines, metrics, calibration, false positives, false negatives, statistical uncertainty, and reproducibility
Ability to read ML research papers and implement methods from first principles
Experience building production software beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, and deployment
Comfort working with open-weight models and understanding modern LLM inference systems
Ability to work across backend and frontend boundaries; ability to work with React/TypeScript product surfaces
Strong technical judgment about experimental evidence
High agency and strong sense of ownership
Comfortable working in a fast-moving startup environment
Useful experience in model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluations, interpretability, activation and representation analysis, DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL/pgvector, Next.js, React, TypeScript, data visualization, experiment dashboards, GPU model serving, and adversarial evaluations