Monad Ecosystem Job Board

Fortytwo is a decentralized AI protocol on Monad that leverages idle consumer hardware for swarm inference. It enables Small Language Models to achieve advanced multi-step reasoning at lower costs, surpassing the performance and scalability of leading models.

Responsibilities:

• Deploy scalable, production-ready ML services with optimized infrastructure and auto-scaling Kubernetes clusters.

• Optimize GPU resources using MIG (Multi-Instance GPU) and NOS (Node Offloading System).

• Manage cloud storage (e.g., S3) to ensure high availability and performance.

• Integrate state-of-the-art ML techniques, such as LoRA and model merging, into workflows:

  • Work with SOTA ML codebases and adapt them to organizational needs.

  • Integrate LoRA (Low-Rank Adaptation) techniques and model merging workflows.

  • Deploy and manage large language models (LLM), small language models (SLM), and large multimodal models (LMM).

  • Serve ML models using technologies like Triton Inference Server.

  • Leverage solutions such as vLLM, TGI (Text Generation Inference), and other state-of-the-art serving frameworks.

  • Optimize models with ONNX and TensorRT for efficient deployment.

• Develop Retrieval-Augmented Generation (RAG) systems integrating spreadsheet, math, and compiler processors.

• Set up monitoring and logging solutions using Grafana, Prometheus, Loki, Elasticsearch, and OpenSearch.

• Write and maintain CI/CD pipelines using GitHub Actions for seamless deployment processes.

• Create Helm templates for rapid Kubernetes node deployment.

• Automate workflows using cron jobs and Airflow DAGs.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• Proficiency in Kubernetes, Helm, and containerization technologies.

• Experience with GPU optimization (MIG, NOS) and cloud platforms (AWS, GCP, Azure).

• Strong knowledge of monitoring tools (Grafana, Prometheus) and scripting languages (Python, Bash).

• Hands-on experience with CI/CD tools and workflow management systems.

• Familiarity with Triton Inference Server, ONNX, and TensorRT for model serving and optimization.

Preferred:

• 5+ years of experience in MLOps or ML engineering roles.

• Experience with advanced ML techniques, such as multi-sampling and dynamic temperatures.

• Knowledge of distributed training and large model fine-tuning.

• Proficiency in Go or Rust programming languages.

• Experience designing and implementing highly secure MLOps pipelines, including secure model deployment and data encryption.

Why Work with Us:

At Fortytwo, we are building a research-driven, decentralized AI infrastructure that prioritizes scalability, efficiency, and sustainability. Our approach moves beyond centralized AI constraints, applying globally scalable swarm intelligence to enhance LLM reasoning and problem-solving capabilities.

• Engage in meaningful AI research – Work on decentralized inference, multi-agent systems, and efficient model deployment with a team that values rigorous, first-principles thinking.

• Build scalable and sustainable AI – Design AI systems that reduce reliance on massive compute clusters, making advanced models more efficient, accessible, and cost-effective.

• Collaborate with a highly technical team – Join engineers and researchers who are deeply experienced, intellectually curious, and motivated by solving hard problems.

We’re looking for individuals who thrive in research-driven environments, value autonomy, and want to work on foundational AI challenges.