[Remote] Remote | Machine Learning Systems Evaluation Engineer — Up to $90/hour

Note: The job is a remote job and is open to candidates in USA. 24-MAG is offering a specialized remote consulting opportunity for experienced machine learning engineers. The role focuses on evaluating complex machine learning and AI engineering implementations, supporting workflows related to ML system evaluation, and providing structured feedback on MLOps and deployment processes.ResponsibilitiesUse modern coding agents to complete and evaluate complex machine learning and AI engineering tasksReview generated implementations involving model training, inference systems, MLOps workflows, LLM applications, and AI-powered product featuresAssess technical outputs for correctness, quality, maintainability, performance, reliability, and production-readinessApply professional machine learning engineering judgment to realistic technical scenariosEvaluate ML system workflows involving model deployment, inference infrastructure, monitoring, testing, and production integrationReview implementation choices related to scalability, latency, data flow, model serving, reliability, and system maintainabilityIdentify bugs, edge cases, performance issues, failure modes, and weak assumptions in ML engineering outputsProvide structured feedback on MLOps design, deployment patterns, and production ML system qualityCompare outputs from multiple coding agents and assess their strengths, weaknesses, accuracy, and practical usefulnessIdentify where generated solutions succeed, where they fail, and where additional ML engineering judgment is requiredEvaluate whether generated machine learning implementations reflect real-world engineering standardsDocument technical review findings clearly for project teams and quality evaluation workflowsProduce clear, structured evaluations of machine learning engineering tasks and generated outputsExplain reasoning around model training, inference systems, deployment infrastructure, LLM applications, performance, and architectural trade-offsSupport technical assessment workflows by documenting accepted work, improvement areas, and practical engineering conclusionsHelp ensure outputs reflect production-scale machine learning engineering expectationsSkills2+ years of professional machine learning engineering experienceHands-on experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered productsRegular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable toolsAbility to evaluate generated machine learning implementations and identify technical trade-offs, bugs, edge cases, and performance issuesStrong understanding of model training, inference workflows, MLOps, data pipelines, evaluation methods, deployment patterns, and system reliabilityClear written communication skills and comfort documenting technical reasoning in a remote, project-based environmentA degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, Statistics, Mathematics, or a related technical field is helpfulEquivalent professional experience in machine learning engineering, applied AI, MLOps, LLM applications, or production ML systems is also highly relevantExperience deploying ML systems to production is strongly preferredExperience with Python, PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, LlamaIndex, MLflow, Ray, or comparable ML toolsFamiliarity with model serving, feature pipelines, vector databases, embeddings, retrieval systems, LLM application architecture, or evaluation frameworksExperience with cloud platforms, Docker, Kubernetes, CI/CD pipelines, observability tooling, or production deployment workflowsBackground in technical code review, ML architecture review, model performance evaluation, or large-scale AI product engineeringStrong comfort working in sprint-based project environments with focused technical assessment windowsBenefitsRemote consulting work aligned with machine learning engineering, coding agent, and technical evaluation expertiseFully remote and flexible schedulingSprint-based, project-based availabilityPayments are made weekly via Stripe or Wise based on services renderedSome projects may use accepted-task compensation depending on the specific workflowCompany OverviewAt 24-MAG, we support emerging AI and consulting platforms by sourcing and connecting qualified professionals with remote, contract-based opportunities. It was founded in undefined, and is headquartered in Sheridan, Wyoming, US, with a workforce of 2-10 employees. Its website is https://24-mag.com/.