Applied AI Engineer

Pantomath•San Francisco, CA

2d•$150,000 - $230,000

About The Position

Pantomath is hiring an Applied AI Engineer, Agents to build AI agents that solve complex enterprise data problems. You'll 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: you'll explore emerging techniques, run rigorous experiments, and own the software that turns research into useful customer experiences. You'll have meaningful ownership of both the research and the engineering, with a direct path from an idea to a capability customers use. Pantomath is building the autonomous layer for enterprise Data Operations. Our 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. Across our entire company, Pantomath brings together seasoned startup founders, operators with prior successful exits, and veteran talent from market-defining technology companies including Google, Meta, Stripe, Databricks, Microsoft and Amazon. We combine the high-ownership, high-velocity culture of a scaling venture-backed startup with the deep execution rigor and track record of proven industry leaders.

Requirements

Experience building and shipping AI systems, with substantial hands-on 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 such as LoRA, and preference optimization or reinforcement learning.
Practical experience with LLMs, agent systems, and model evaluation.
Experience adapting or training models, with an understanding of data quality, experimental design, and generalization.
Exposure to training and evaluation data pipelines, synthetic data generation, or human feedback systems.
A 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.

Nice To Haves

Strong software engineering fundamentals and proficiency in Python, TypeScript, or comparable languages.
The 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 a collaborative approach.

Responsibilities

Build and ship AI agents. Develop agentic products that solve complex enterprise problems and deliver meaningful customer value.
Advance model and agent capabilities. Explore fine-tuning, post-training, and other adaptation techniques to improve performance on enterprise tasks.
Design experiments and evaluations. Build datasets, benchmarks, and feedback loops to measure progress and understand failure patterns.
Build the systems behind the agents. Develop services, integrations, and infrastructure that support reliable execution at scale.
Bring research into production. Turn promising experiments into product capabilities, owning implementation, deployment, and ongoing improvement.
Improve production performance. Make practical tradeoffs across quality, reliability, latency, cost, and security.
Shape the product and research direction. Partner with engineering, product, and customer-facing teams to identify valuable problems and pursue approaches that work.