Remote Machine Learning Postdoc Jobs in New York, NY

Specialised Full-Time Consulting Opportunity for Machine Learning EngineersWe are sharing a specialised full-time consulting opportunity for machine learning engineers and research practitioners with hands-on experience training, evaluating, and experimenting with ML models end to end.This role supports the development of advanced agentic evaluation benchmarks for frontier AI systems. Selected professionals will transform real machine learning research ideas into rigorous multi-step tasks, implement and run experiments, analyse training behaviour, and evaluate where model-generated solutions fall short of technically correct results.Key ResponsibilitiesMachine Learning Task DesignTurn practical ML research ideas into well-defined, multi-step evaluation tasksDevelop assignments involving model training, experimental modifications, and performance analysisDefine clear technical requirements, expected outputs, and success criteriaEnsure tasks assess genuine implementation and experimental reasoning rather than superficial library usageExperiment Implementation & ExecutionImplement reference solutions using Python, scripts, and notebook environmentsConfigure and run model-training experiments from setup through final evaluationModify model components, training procedures, reward functions, or experimental parametersValidate code, dependencies, datasets, intermediate outputs, and final resultsDocument complete workflows so experiments can be reproduced independentlyModel Evaluation & AnalysisReview how frontier AI models approach complex machine learning tasksAssess implementation quality, experimental methodology, and technical conclusionsIdentify coding errors, unsupported assumptions, weak experimental controls, and misleading interpretationsDetermine whether reported improvements are supported by the observed resultsExplain clearly where and why a model-generated solution failsReinforcement Learning ExperimentsDevelop selected tasks involving reinforcement learning fundamentalsEvaluate reward-function changes, policy-training behaviour, and experimental outcomesAssess whether proposed modifications produce the intended training effectIdentify instability, unintended incentives, or incorrect interpretations of RL resultsResearch CollaborationWork closely with researchers, task authors, and fellow machine learning specialistsCompare evaluation decisions to maintain consistent and rigorous benchmark standardsRefine task instructions, reference solutions, and grading criteria based on testing outcomesShare recurring model failure patterns and opportunities for stronger benchmark coverageIdeal ProfileStrong candidates may have:At least 1 year of experience in machine learning research, research engineering, or a comparable technical roleHands-on experience training and evaluating ML models through complete experimental workflowsStrong understanding of experiment setup, execution, analysis, and reproducibilityFamiliarity with large language model capabilities, limitations, and evaluation techniquesWorking proficiency in Python and GitComfort using both scripting and notebook-based environmentsStrong technical writing, analytical reasoning, and attention to detailAbility to work independently through ambiguous, open-ended research problemsReliable availability for approximately 35 hours per weekEducational BackgroundA master's degree or PhD in machine learning, computer science, artificial intelligence, engineering, mathematics, or another relevant STEM discipline is highly relevantEquivalent practical experience in a research-intensive machine learning role may also be consideredAcademic or professional work involving model training, experimentation, or ML systems may strengthen an applicationPublications, open-source contributions, technical reports, or substantial research projects may also be valuableNice to HaveUnderstanding of reinforcement learning concepts, including reward functions and policy trainingExperience in AI training, model evaluation, or benchmark developmentBackground authoring technical tasks, reference solutions, or grading rubricsFamiliarity with agentic AI systems and multi-step model evaluationsExperience diagnosing model-training failures or unexpected experimental behaviourKnowledge of experimental design, ablation studies, and performance comparisonExperience reviewing code, notebooks, or research analyses prepared by other practitionersFamiliarity with reproducible ML environments and collaborative Git workflowsWhy This OpportunityApply practical machine learning research expertise to frontier AI evaluationDesign realistic tasks grounded in end-to-end model experimentationHelp improve how AI systems approach implementation, training, and analytical reasoningWork across Python, ML evaluation, reinforcement learning, and reproducible researchCollaborate closely with AI researchers and machine learning specialistsParticipate in a structured full-time remote role with competitive hourly compensationContract DetailsFull-time W-2 contingent employment opportunityFully remote within the United StatesExpected commitment of approximately 35 hours per weekCompetitive rates between $55–$85 per hour depending on expertise and project scopeIndividual tasks may require one to two days of focused implementation and experimental workWork may include task design, model training, experiment execution, notebook development, AI output evaluation, and technical reportingEngagement scope and duration may evolve according to project requirements and performanceThis opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy.