About the roleAs an
ML Engineer at Sciforium, you will operate at the intersection of production software engineering and Core AI/ML to architect, scale, and optimize end-to-end multimodal GenAI systems. In this role, you will build production-grade solutions across Serving, Post-Training and Agentic frameworks. You will also be responsible for driving deep technical optimizations and MLOps process improvements.
What You'll Be Doing- Build and scale Agentic AI Systems:
Design and implement intelligent systems that can reason, plan, and execute complex multi-step workflows. Develop architectures that combine LLMs, retrieval systems, memory, tools, and feedback loops.Build orchestration frameworks for multi-agent and tool-based systems. Develop evaluation frameworks that measure accuracy, reliability, latency, and task completion. - New Model Enablements, Automated Benchmarking, Profiling & 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 & Knowledge Sharing: Drive technical evangelism and elevate Sciforium's presence in the global AI ecosystem through high-impact 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 showcasing the novel innovations and complex problem-solving happening at Sciforium.
Must-Haves- Experience: 5+ years of professional ML/AI software engineering experience with a proven track record of architecting and shipping performance-critical systems. Proven experience maintaining and developing model libraries or reusable ML components.
- Education: BS, MS, or PhD in Computer Science, Computer Engineering, or a related technical field (or equivalent practical experience).
- ML Systems: Strong knowledge of generative AI systems including Large Language Models, Transformers, Reinforcement Learning, RAG, and agentic patterns such as Chain-of-Thought, Tool Use, and Multi-Agent orchestration
- Machine Learning Expertise: Experience with one or more distributed ML training frameworks such as PyTorch, TensorFlow, or JAX, or Ray and inference engines like TensorRT, vLLM or SGLang. Good understanding of deep learning architectures across multiple domains (e.g., NLP, vision, speech, generative models).
- Communication: Ability to articulate complex technical trade-offs, write clear documentation, and collaborate smoothly across multidisciplinary engineering teams.
Nice-to-have- Experience building production AI agents or autonomous systems. Experience with reasoning frameworks, planning systems, memory architectures, and tool-use ecosystems. Track record of reducing operational complexity while increasing scalability and maintainability.
- Experience with vector databases, retrieval systems, knowledge graphs, or semantic search. Experience with AI evaluation, benchmarking, and observability platforms.
- Familiarity with distributed serving or large-scale inference frameworks (e.g., vLLM, TensorRT, FasterTransformer).
- Experience with model performance optimization and profiling.
- Familiarity with low-level performance considerations when running models on GPUs/TPUs.
- Contributions to open-source model repositories or ML frameworks.
Benefits include- Medical, dental, and vision insurance
- 401k plan
- Daily lunch, snacks, and beverages
- Flexible time off
- Competitive salary and equity
