Mercor HIRING - Remote | Open Source Developers | Backend Engineer & Devops Engineer | Machine Learning Engineer

 

Mercor HIRING - Remote | Open Source Developers | Backend Engineer & Devops Engineer | Machine Learning Engineer

    Mercor is an AI-powered talent platform that connects highly skilled professionals (engineers, lawyers, doctors, etc.) with companies needing specialized expertise, primarily for training AI models, by using AI for screening, conducting AI interviews with avatars, and matching talent to complex data annotation and development roles, focusing on the top 10-20% of talent for high-value tasks in the AI economy.

75% of Mercor employees would recommend working there to a friend based on Glassdoor reviews. Employees also rated Mercor 3.7 out of 5 for work life balance, 3.8 for culture and values and 3.7 for career opportunities.

Open Source Developers

We’re looking for open-source contributors and experienced engineers who understand how to review, maintain, and troubleshoot live repositories.

Who You Are

  • An open-source developer or maintainer who has contributed to or reviewed code in live repositories
  • Comfortable reasoning about Git at a deep level
  • Adept at debugging repository states and fixing broken histories without data loss

Preferred Qualifications

  • 3+ years of software engineering experience in open-source, backend, or DevOps roles
  • Demonstrated history of contributions on GitHub, GitLab, or other OSS platforms
  • (Bonus) Experience in code review or AI/LLM model evaluation

Why Join

  • Turn your open-source experience into valuable, high-impact data
  • Fully remote, flexible work, with competitive compensation
  • We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
Backend Engineer and Devops Engineer - Microservices 

    Mercor is hiring on behalf of a leading AI lab for an experienced Backend & DevOps Engineer to design, build, and scale microservices-based infrastructure that powers next-generation AI systems. You’ll own both core backend services and infrastructure automation, ensuring distributed systems are fast, reliable, and secure. This position blends software engineering, DevOps, and system design, working closely with research and engineering teams to deliver production-grade, scalable AI infrastructure.

Responsibilities
  • Architect and develop high-performance, fault-tolerant microservices.
  • Build and maintain CI/CD pipelines, deployment workflows, and infrastructure-as-code.
  • Manage Kubernetes clusters, cloud infrastructure (AWS/GCP), and container orchestration.
  • Implement monitoring, observability, and security best practices.
  • Collaborate with backend and AI teams to optimize system performance and reliability.
  • Continuously improve automation, deployment speed, and operational efficiency.
Requirements
  • 3+ years of experience in backend engineering and/or DevOps roles.
  • Strong understanding of microservices architecture and API design.
  • Proficiency in Go, Python, Node.js, or Java.
  • Hands-on experience with Docker, Kubernetes, and cloud environments (AWS/GCP).
  • Familiarity with Terraform, Helm, ArgoCD, or GitHub Actions.
  • Experience with databases (PostgreSQL, MongoDB, Redis) and message queues (Kafka, RabbitMQ, NATS).
  • Solid grasp of monitoring stacks (Prometheus, Grafana, ELK) and CI/CD principles.
Bonus Points
  • Experience in AI/ML infrastructure or large-scale distributed systems.
  • Contributions to open-source DevOps or backend frameworks.
  • Knowledge of GitOps, serverless, or edge computing.
Pay & Work Structure
  • You’ll be classified as an hourly contractor to Mercor.
  • Paid weekly via Stripe Connect, based on hours logged.
  • Remote and flexible working style.
  • We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
Machine Learning Engineer
    Mercor is hiring a Machine Learning Engineer to help design, train, and deploy large-scale learning systems powering autonomous AI agents for its AI lab partner. This role is ideal for engineers passionate about building models that think, adapt, and perform complex tasks in real-world environments. You’ll be working at the intersection of ML research, systems engineering, and AI agent behavior — transforming ideas into robust, scalable learning pipelines.

You’re a great fit if you:
  • Have a strong background in machine learning, deep learning, or reinforcement learning.
  • Are proficient in Python and familiar with frameworks such as PyTorch, TensorFlow, or JAX.
  • Understand training infrastructure, including distributed training, GPUs/TPUs, and data pipeline optimization.
  • Can implement end-to-end ML systems, from preprocessing and feature extraction to training, evaluation, and deployment.
  • Are comfortable with MLOps tools (e.g., Weights & Biases, MLflow, Docker, Kubernetes, or Airflow).
  • Have experience designing custom architectures or adapting LLMs, diffusion models, or transformer-based systems.
  • Think critically about model performance, generalization, and bias, and can measure results through data-driven experimentation.
  • Are curious about AI agents and how models can simulate human-like reasoning, problem-solving, and collaboration.
Primary Goal of This Role
    To develop, optimize, and deploy machine learning systems that enhance agent performance, learning efficiency, and adaptability. You’ll design model architectures, training workflows, and evaluation pipelines that push the frontier of autonomous intelligence and real-time reasoning.

What You’ll Do
  • Design and implement scalable ML pipelines for model training, evaluation, and continuous improvement.
  • Build and fine-tune deep learning models for reasoning, code generation, and real-world decision-making.
  • Collaborate with data scientists to collect and preprocess training data, ensuring quality and representativeness.
  • Develop benchmarking tools that test models across reasoning, accuracy, and speed dimensions.
  • Implement reinforcement learning loops and self-improvement mechanisms for agent training.
  • Work with systems engineers to optimize inference speed, memory efficiency, and hardware utilization.
  • Maintain model reproducibility and version control, integrating with experiment tracking systems.
  • Contribute to cross-functional research efforts to improve learning strategies, fine-tuning methods, and generalization performance.
Why This Role Is Exciting
  • Build the core learning systems that power next-generation AI agents.
  • Combine ML research, engineering, and systems-level optimization in one role.
  • Work on uncharted challenges, designing models that can reason, plan, and adapt autonomously.
  • Collaborate with a world-class AI team redefining how autonomous systems learn and evolve.
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 per 5 task created.
  • We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

How To Apply For Mercor HIRING - Remote?

Eligible candidates apply this drive in online by the following the link ASAP.

To Apply Open Source Developer : Click Here

To Apply Backend Engineer & Devops Engineer : Click Here

To Apply Machine Learning Engineer : Click Here

For More Vacancies : Click Here

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