We are seeking a Technical Reviewer on behalf of a leading AI lab to evaluate and refine benchmarking pipelines for reinforcement learning (RL) environments and agentic AI systems. In this role, you’ll be responsible for reviewing environment design, terminal conditions, and evaluation protocols to ensure accuracy, reproducibility, and fairness in benchmarking. You’ll work closely with researchers and engineers to provide technical feedback that strengthens experimental rigor and system reliability. You’re a great fit if you: Have a background in reinforcement learning, computer science, or applied AI research . Are experienced with RL environments . Understand benchmarking methodologies, terminal conditions, and evaluation metrics for RL tasks. Are comfortable reading and reviewing codebases in Python (PyTorch/TensorFlow a plus). Have strong critical thinking skills and can provide structured technical feedback . Care deeply about experimental reproducibility, fairness, and standardization in agentic AI. Are detail-oriented and capable of reviewing both theoretical formulations and implementation details . Primary Goal of This Role To review, validate, and improve reinforcement learning environment benchmarking pipelines, ensuring that terminal conditions, evaluation metrics, and system behaviors are robust, reproducible, and aligned with agentic AI research goals. What You’ll Do Review RL environments and evaluate terminal conditions for correctness and consistency. Assess benchmarking pipelines for fairness, reproducibility, and alignment with research objectives. Provide structured technical feedback on code implementations and documentation. Collaborate with researchers to refine evaluation metrics and methodologies . Ensure reproducibility by validating results across different runs, seeds, and hardware setups . Document findings and recommend improvements for environment design and benchmarking standards . Why This Role Is Exciting You’ll directly influence the reliability of benchmarking in agentic AI research . You’ll work on cutting-edge RL environments that test the limits of intelligent agents. You’ll help establish standards for evaluation and reproducibility in a fast-moving field. You’ll collaborate with researchers shaping the future of agentic AI systems . Pay & Work Structure You’ll be classified as a full-time hourly contractor to Mercor. Paid weekly via Stripe Connect, based on hours logged. 40 hours/week commitment with flexible scheduling. Remote and flexible working style. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
We are hiring a Computational Physics Specialist to create terminal bench–style problems and tasks involving agents within the physics domain, as part of a hiring effort for its AI lab partner building advanced agent-based infrastructure. This role is ideal for individuals with a strong foundation in physics and a working knowledge of programming who are excited to design real-world simulations and problem-solving environments. You will be building terminal-based benchmarks where agents are tested on their ability to reason through and solve physics-based challenges using code, commands, and logic — all within a realistic terminal interface. You’re a great fit if you: Have a background in Physics, Applied Physics, or Engineering Physics. Are familiar with computational methods such as numerical integration, differential equations, Monte Carlo simulations, or physical modeling. Have coding experience in Python, MATLAB, C++, or similar languages, and are comfortable in a Linux terminal environment. Understand core physics concepts (mechanics, electromagnetism, thermodynamics, wave dynamics, etc.) and can translate them into structured problem-solving tasks. Are interested in AI agents and want to explore how they can interact with simulations, models, and physics solvers in terminal-based systems. Can design step-by-step challenges where quantitative reasoning, code-based exploration, and scientific accuracy are required. Primary Goal of This Role To design terminal bench–style tasks where autonomous agents are challenged to solve physics-based problems through code, simulations, and reasoning. These environments will test agent performance in areas like equation solving, unit conversion, modeling physical systems, and interpreting simulation outputs in a terminal context. What You’ll Do Build secure, sandboxed environments where agents interact with physics simulations or problem-solving tasks via the terminal. Design benchmarks that require agents to: Calculate physical quantities Run simple code-based simulations Solve real-world physics problems using appropriate equations and reasoning Collaborate with AI engineers to define task specifications, rewards, and difficulty levels. Create tasks that mimic real scientific computing workflows — e.g., modifying parameters, parsing output data, or debugging physical models. Develop tools for benchmark reproducibility, task evaluation, and automated resets. Scale challenges from basic mechanics problems to multi-step, interdisciplinary simulations. Why This Role Is Exciting Help shape a new class of agent benchmarks grounded in scientific reasoning. Apply physics knowledge in a creative and technically deep setting involving simulation, automation, and AI. Contribute to a high-autonomy team pushing the boundaries of agent performance in complex domains. Pay & Work Structure You’ll be classified as an hourly contractor to Mercor. Paid weekly via Stripe Connect, based on hours logged. Part-time (20 hrs/week) with fully remote, async flexibility — work from anywhere on your own schedule. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
W are seeking PhD holders, doctoral candidates, and outstanding Master’s graduates in biology and related disciplines to join a high-impact AI research initiative in partnership with a leading artificial intelligence lab . Key Domains Here is a list of domains. If you belong to or have expertise in one or more of them, feel free to apply — we’re excited to have you on the team! Molecular & Cellular Biology: Molecular Biology, Cell Biology, Biochemistry, Genetics, Genomics, Proteomics, Metabolomics Organismal & Evolutionary Biology: Evolutionary Biology, Developmental Biology, Ecology, Microbiology, Virology, Botany, Zoology Biomedical & Applied Life Sciences: Immunology, Neurobiology, Physiology, Biotechnology, Systems Biology, Bioinformatics, Computational Biology Interdisciplinary Biological Sciences: Biophysics, Structural Biology, Synthetic Biology, Environmental Biology Key Responsibilities Evaluate scientific correctness, conceptual rigor, and depth of LLM-generated responses across biology and biomedical domains. Review outputs involving experimental design, data interpretation, and theoretical frameworks in biological sciences. Identify factual inaccuracies, reasoning errors, and conceptual misunderstandings in model outputs. Benchmark model performance on advanced biological and interdisciplinary research problems. Work independently and asynchronously using proprietary evaluation tools. Requirements PhD (candidate/recipient) or Master’s degree in Biology, Molecular Biology, Biochemistry, Biotechnology, Bioinformatics, or a closely related field. Strong command of graduate-level biological concepts, experimental reasoning, and data interpretation. Excellent written communication and analytical abilities. Ability to work independently in a remote, asynchronous setting. Role Details Part-time (20 hours/week) Remote and asynchronous work environment Flexible schedule to accommodate global contributors Compensation Contractor position via Mercor $20–$30/hour , depending on expertise and domain depth Weekly payments through Stripe Connect
We are hiring on behalf of a leading AI research lab to bring on highly skilled Machine Learning Engineers with a proven record of building, training, and evaluating high-performance ML systems in real-world environments. In this role, you will design, implement, and curate high-quality machine learning datasets, tasks, and evaluation workflows that power the training and benchmarking of advanced AI systems. This position is ideal for engineers who have excelled in competitive machine learning settings such as Kaggle, possess deep modelling intuition, and can translate complex real-world problem statements into robust, well-structured ML pipelines and datasets. You will work closely with researchers and engineers to develop realistic ML problems, ensure dataset quality, and drive reproducible, high-impact experimentation. Candidates should have 3–5+ years of applied ML experience or a strong record in competitive ML, and must be based in India. Ideal applicants are proficient in Python, experienced in building reproducible pipelines, and familiar with benchmarking frameworks, scoring methodologies, and ML evaluation best practices. Responsibilities Frame unique ML problems for enhancing ML capabilities of LLMs. Design, build, and optimise machine learning models for classification, prediction, NLP, recommendation, or generative tasks. Run rapid experimentation cycles, evaluate model performance, and iterate continuously. Conduct advanced feature engineering and data preprocessing. Implement adversarial testing, model robustness checks, and bias evaluations. Fine-tune, evaluate, and deploy transformer-based models where necessary. Maintain clear documentation of datasets, experiments, and model decisions. Stay updated on the latest ML research, tools, and techniques to push modelling capabilities forward. Required Qualifications At least 3–5 years of full-time experience in machine learning model development Technical degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field Demonstrated competitive machine learning experience (Kaggle, DrivenData, or equivalent) Evidence of top-tier performance in ML competitions (Kaggle medals, finalist placements, leaderboard rankings) Strong proficiency in Python , PyTorch/TensorFlow , and modern ML/NLP frameworks Solid understanding of ML fundamentals: statistics, optimisation, model evaluation, architectures Experience with distributed training, ML pipelines, and experiment tracking Strong problem-solving skills and algorithmic thinking Experience working with cloud environments (AWS/GCP/Azure) Exceptional analytical, communication, and interpersonal skills Ability to clearly explain modelling decisions, tradeoffs, and evaluation results Fluency in English Preferred / Nice to Have Kaggle Grandmaster , Master , or multiple Gold Medals Experience creating benchmarks, evaluations, or ML challenge problems Background in generative models, LLMs, or multimodal learning Experience with large-scale distributed training Prior experience in AI research, ML platforms, or infrastructure teams Contributions to technical blogs, open-source projects, or research publications Prior mentorship or technical leadership experience Published research papers (conference or journal) Experience with LLM fine-tuning, vector databases, or generative AI workflows Familiarity with MLOps tools: Weights & Biases, MLflow, Airflow, Docker, etc. Experience optimising inference performance and deploying models at scale Why Join Gain exposure to cutting-edge AI research workflows, collaborating closely with data scientists, ML engineers, and research leaders shaping next-generation AI systems. Work on high-impact machine learning challenges while experimenting with advanced modelling strategies, new analytical methods, and competition-grade validation techniques. Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting, experimentation, tabular ML, and multimodal analytics. Flexible engagement options ( 30–40 hrs/week or full-time ) — ideal for ML engineers eager to apply Kaggle-level problem solving to real-world, production-grade AI systems. Fully remote and globally flexible — optimised for deep technical work, async collaboration, and high-output research environments. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
We are hiring a Data Scientist to help build advanced analytics and data-driven infrastructure for its AI lab partner focused on developing intelligent agent-based systems. This role is ideal for analytical thinkers who excel at turning large-scale data into actionable insights and enjoy working at the intersection of machine learning, experimentation, and real-world applications. You’ll be designing data pipelines, statistical models, and performance metrics that drive the next generation of autonomous systems. You’re a great fit if you: Have a strong background in data science, machine learning, or applied statistics . Are proficient in Python, SQL , and familiar with libraries such as Pandas, NumPy, Scikit-learn, and PyTorch/TensorFlow . Understand probabilistic modeling, statistical inference, and experimentation frameworks (A/B testing, causal inference) . Can collect, clean, and transform complex datasets into structured formats ready for modeling and analysis. Have experience designing and evaluating predictive models , using metrics like precision, recall, F1-score, and ROC-AUC. Are comfortable working with large-scale data systems (Snowflake, BigQuery, or similar). Are curious about AI agents , and how data can shape the reasoning, adaptability, and behavior of intelligent systems. Enjoy collaborating with cross-functional teams — from engineers to research scientists — to define meaningful KPIs and experiment setups. Primary Goal of This Role To design and implement robust data models, pipelines, and metrics that support experimentation, benchmarking, and continuous learning for agentic AI systems. The role focuses on building data-driven insights into how agents reason, perform, and improve over time across algorithmic and real-world tasks. What You’ll Do Develop data collection and preprocessing pipelines for structured and unstructured data from multiple agent simulations. Build and iterate on machine learning models for performance prediction, behavior clustering, and outcome optimization. Design and maintain dashboards and visualization tools for monitoring agent performance, benchmarks, and trends. Conduct statistical analyses to evaluate the efficacy of AI systems under various environments and constraints. Collaborate with engineers to design evaluation frameworks that measure reasoning quality, adaptability, and efficiency. Prototype data-driven tools and feedback loops to automatically improve model accuracy and agent behavior over time. Work closely with AI research teams to translate experimental results into scalable, production-grade insights. Why This Role Is Exciting Work at the forefront of AI agent intelligence and help define how data shapes their evolution. Blend machine learning, experimentation, and data engineering in one role. Collaborate with top-tier AI engineers on new agent benchmarks and feedback mechanisms . Contribute to a mission that merges algorithmic reasoning, real-world performance, and human-like decision-making . Pay & Work Structure You’ll be classified as an hourly contractor to Mercor. Paid weekly via Stripe Connect, based on hours logged. Part-time (20 hrs - 40 hrs/week) with fully remote, async flexibility — work from anywhere, on your own schedule. Weekly bonus of $500 - $1000 USD per 5 task created. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.