Applied ML Engineer

Job not on LinkedIn

🔥 0 minutes ago

🌐 Singapore, China, +2 more countries – Remote

🇸🇬 Singapore – Remote 🇨🇳 China – Remote 🇬🇧 United Kingdom – Remote 🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 14%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Sentient Foundation

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

🤝 Non-profit

₿ Crypto

Artificial Intelligence • Non-profit • Crypto

Sentient Foundation is pioneering a new era in AI, empowering communities to create Loyal AI—community-built, community-aligned, and community-owned. As a non-profit committed to advancing open-source AI technologies and building a decentralized, transparent ecosystem, we champion an Open AI economy where AI builders are key stakeholders. With AGI on the horizon, our mission is to ensure it serves humanity, not corporations.

📋 Description

• 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

• 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