Machine Learning Engineer

About the job

Machine Learning Engineer Hourly Contract | Part-Time Remote | $80 –$120 per hour 1. About the Role At Mercor, we’re building the talent engine that helps leading labs and research organizations move AI forward. Our newest initiative focuses on benchmarking and improving model performance and training speed across real machine learning workloads. If you’re an early-career Machine Learning Engineer ...  or an ML-focused graduate student/Ph D who values innovation, rigor, and meaningful impact, we’d love to meet you. This is a remote, asynchronous, part-time opportunity ideal for individuals who thrive on clear structure and measurable outcomes. 2. What to Expect Schedule: Remote and asynchronous — set your own working hours Commitment: ~20 hours per week Duration: Through December 22nd , with potential extension into 2026 3. What You’ll Do Draft detailed natural-language plans and code implementations for machine learning tasks Convert novel ML problems into agent-executable tasks for reinforcement learning environments Identify failure modes and apply golden patches to LLM-generated trajectories for ML tasks 4. What You’ll Bring Experience: 0–2 years as a Machine Learning Engineer or Graduate-level experience in Computer Science, ML, or related coursework Required Skills: Strong Python skills Familiarity with ML libraries: Tensor Flow, XGBoost, scikit-learn , etc. Experience with model training, data preparation, and evaluation Bonus: Contributions to ML benchmarks or open-source ML tooling 5. Compensation & Terms Rate: $80–$120 per hour (based on experience and region) Payments: Weekly via Stripe Connect Engagement: Independent contractor 6. How to Apply Submit your resume Complete the System Design Session (Fill out the Machine Learning Engineer Screen (⚡ PS: Mercor reviews applications daily. Please complete your interview and onboarding steps to be considered for this opportunity. ⚡

Requirements

• Python
• Tensor Flow
• XGBoost
• scikit-learn

Qualifications

• Graduate-level experience in Computer Science
• ML
• related coursework

Preferred Technologies

• Python
• Tensor Flow
• XGBoost
• scikit-learn