Machine Learning Software Engineer (Part-Time | $80 –$120/hr)Join to apply for the Machine Learning Software Engineer (Part-Time | $80 –$120/hr) role at Call For Referral .Get AI-powered advice on this job and more exclusive features. This range is provided by Call For Referral. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.Base pay range : $80.00/hr - $120.00/hrAbout the RoleAt 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/PhD 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.What to ExpectSchedule: Remote and asynchronous — set your own working hoursCommitment: ~20 hours per weekDuration: Through December 22nd , with potential extension into 2026What You'll DoDraft detailed natural-language plans and code implementations for machine learning tasksConvert novel ML problems into agent-executable tasks for reinforcement learning environmentsIdentify failure modes and apply golden patches to LLM-generated trajectories for ML tasksWhat You'll BringExperience: 0–2 years as a Machine Learning Engineer or graduate-level experience in Computer Science, ML, or related courseworkRequired Skills: Familiarity with ML libraries: TensorFlow, XGBoost, scikit-learn , etc.; experience with model training, data preparation, and evaluationBonus: Contributions to ML benchmarks or open-source ML toolingCompensation & TermsRate: $80–$120 per hour (based on experience and region)Payments: Weekly via Stripe ConnectHow to ApplyComplete 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. #J-18808-Ljbffr