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Sciforium is an AI infrastructure company building next-generation multimodal AI models and a proprietary high-efficiency serving platform. Backed by multi-million-dollar funding and direct AMD sponsorship, the company is scaling rapidly to develop the full stack powering frontier AI models and real-time applications.
As an ML Engineer, you will operate at the intersection of production software engineering and core AI/ML, architecting, scaling, and optimizing end-to-end multimodal GenAI systems. You'll build production-grade solutions across serving, post-training, and agentic frameworks while driving deep technical optimizations and MLOps process improvements.
Key responsibilities include:
- Building and scaling agentic AI systems: Design and implement intelligent systems that reason, plan, and execute complex multi-step workflows. Develop architectures combining LLMs, retrieval systems, memory, tools, and feedback loops. Build orchestration frameworks for multi-agent and tool-based systems. Develop evaluation frameworks measuring accuracy, reliability, latency, and task completion.
- New model enablements, automated benchmarking, profiling, and roofline analysis: Rapidly benchmark, adapt, and integrate state-of-the-art open-weights models into production runtimes. Build automated MLOps tooling to profile deep learning workloads against theoretical hardware limits to drive optimization.
- Open-source leadership and knowledge sharing: Drive technical evangelism and elevate Sciforium's presence in the global AI ecosystem through community engagement. Actively contribute code, features, and optimizations to high-visibility open-source repositories. Author and publish deep-dive technical blogs, whitepapers, and architecture breakdowns.
Required qualifications: 5+ years of professional ML/AI software engineering with proven track record architecting and shipping performance-critical systems. BS, MS, or PhD in Computer Science, Computer Engineering, or related technical field (or equivalent). Strong knowledge of generative AI systems including LLMs, Transformers, Reinforcement Learning, RAG, and agentic patterns. Experience with distributed ML training frameworks (PyTorch, TensorFlow, JAX, Ray) and inference engines (TensorRT, vLLM, SGLang). Good understanding of deep learning architectures across multiple domains. Strong communication skills and ability to collaborate across multidisciplinary teams.
Nice-to-have: Production AI agent or autonomous systems experience, vector databases, retrieval systems, knowledge graphs, AI evaluation and benchmarking platforms, distributed serving frameworks, model performance optimization, GPU/TPU performance considerations, open-source contributions.
