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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.
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.
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.
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.
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.