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Salary: USD 150,000 - 230,000 / annual
Pantomath is hiring an Applied AI Engineer to build AI agents that solve complex enterprise data problems. You will combine applied research with hands-on engineering to develop new capabilities, evaluate what works, and bring promising approaches into production. The role spans models, agent systems, and product development.
Key Responsibilities:
- Build and ship AI agents that solve complex enterprise problems and deliver meaningful customer value
- Advance model and agent capabilities by exploring fine-tuning, post-training, and other adaptation techniques to improve performance on enterprise tasks
- Design experiments and evaluations, building datasets, benchmarks, and feedback loops to measure progress and understand failure patterns
- Develop services, integrations, and infrastructure that support reliable execution at scale
- Turn promising experiments into product capabilities, owning implementation, deployment, and ongoing improvement
- Make practical tradeoffs across quality, reliability, latency, cost, and security
- Partner with engineering, product, and customer-facing teams to identify valuable problems and pursue approaches that work
About Pantomath:
Pantomath is building the autonomous layer for enterprise Data Operations. The company's agents catch data incidents, trace them to their root cause across the stack, and resolve them with operational context from across your entire stack. Backed by a $30M Series B led by General Catalyst (alongside Sierra Ventures, Bowery Capital, Epic Ventures, and Hitachi Ventures), Pantomath is pioneering the transition of enterprises from passive data monitoring to real-time, autonomous incident resolution. The team includes seasoned startup founders, operators with prior successful exits, and veteran talent from Google, Meta, Stripe, Databricks, Microsoft, and Amazon.
Requirements:
- Substantial hands-on experience building and shipping AI systems, with significant contributions to code, experimentation, and production engineering
- Hands-on experience with post-training tools such as PyTorch, Hugging Face Transformers, TRL, and PEFT, including supervised fine-tuning, parameter-efficient adaptation (LoRA), and preference optimization or reinforcement learning
- Practical experience with LLMs, agent systems, and model evaluation
- Experience adapting or training models, with understanding of data quality, experimental design, and generalization
- Exposure to training and evaluation data pipelines, synthetic data generation, or human feedback systems
- Working knowledge of agent orchestration, tool use, retrieval, or inference optimization
- Comfort working with enterprise data platforms, distributed systems, or production AI infrastructure
- Familiarity with distributed GPU training and efficient inference using tools such as FSDP, DeepSpeed, or vLLM, alongside reproducible experimentation and model evaluation
- Strong software engineering fundamentals and proficiency in Python, TypeScript, or comparable languages
- Ability to read research, reproduce useful results, and assess whether an approach will translate to real-world performance
- Comfort owning ambiguous problems and moving between research exploration and product delivery
- Clear communication, technical judgment, and collaborative approach
