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Jobs/Machine Learning Engineer Role/G2i Inc. - Machine Learning Evaluation Specialist
G2i Inc.

G2i Inc. - Machine Learning Evaluation Specialist

Remote - Bolivia, Ecuador, Paraguay...$416k - $832k1w ago
RemoteLATAMGenomicsArtificial IntelligenceMachine Learning Engineer

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Requirements

• Graduate-level expertise (MS or PhD preferred) in a scientific or technical domain that intersects with ML • Strong working knowledge of ML methods — model selection, feature engineering, evaluation metrics • Deep familiarity with active research problems in your field — you know where general ML knowledge runs out • Excellent written communication — you can articulate complex problems clearly and precisely. This cannot be overstated. • Self-motivated and comfortable working independently on intellectually demanding tasks • What you don't need • What you don't need • No prior AI training or RLHF experience required • No software engineering background needed — domain expertise and research instincts are what matter • Domains we're especially looking for • Computational Biology / Bioinformatics • Genomics / Molecular Biology • Physics / Astrophysics / Signal Processing • Climate / Environmental Modeling • Healthcare / Medical Imaging • Neuroscience / Brain-Computer Interfaces • Materials Science / Chemistry • Finance / Quantitative Modeling • Robotics / Control Systems / Reinforcement Learning • Advanced NLP (specialized domains) • Mathematics / Statistics (applied) • Logistics • Fully remote — work from anywhere • $200–$400/hr depending on domain and seniority • 10–40 hrs/week, hourly contract • Independent contractor (1099) — not compatible with F-1 OPT, STEM OPT, or visa statuses requiring W-2 employment or employer sponsorship • ⚠️ This is a project-based, freelance opportunity with no guaranteed hours. We recommend keeping other work options open while waiting for project assignment.

Responsibilities

• Propose and frame original, research-grade ML problems rooted in your domain • Design evaluation tasks that require specialized knowledge well beyond standard pipelines • Assess AI-generated solutions for correctness, creativity, and methodological rigor — and explain exactly where and why they fall short • Document problem difficulty, required domain knowledge, and expected failure modes

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