Founding AI/ML Engineer

Higher Ground Labs; New York (NY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Lightning Rod Labs in a remote-first setting is building AI systems that predict real-world outcomes at scale. You will build platforms for training and serving prediction agents on multi-node GPU clusters, turning unstructured data into actionable learning signals.

We seek Python experts with LLM fine-tuning experience, a track record of shipping impactful systems, and a strong ownership mindset. You’ll operate with autonomy across cloud platforms and data pipelines.

Qualifications

• Excellent Python skills and experience fine‑tuning LLMs.
• Track record of shipping impactful systems.
• Ownership mindset and autonomy.
• Proof of skill — GitHub, portfolio, papers, or demos.
• Scale experience: millions of samples and large distributed GPU clusters.
• Technical range: rapid analysis in notebooks to robust pipelines.
• Data intuition: statistics, probability, data hygiene, ML best practices.
• Cloud fluency across modalities and platforms (e.g., GCP, Azure, AWS).

Responsibilities

• Build cutting-edge systems that teach AI real‑world reasoning at scale.
• Create and evolve infrastructure for training and serving prediction agents.
• Scale training and inference on large multi-node GPU clusters.
• Transform messy, unstructured streams into training signals.
• Design novel objectives, rewards, and calibration methods for causal factors.
• Experiment, optimize the training framework, and demonstrate state‑of‑the‑art results.
• Own delivery end‑to‑end from data collection to deployment.

Job description

Lightning Rod Labs trains AI to predict the future.

We train LLMs to predict real-world outcomes with scalable self-play on unstructured data. We make it easy for enterprises to train custom models from messy data, no extraction required.

We’ve demonstrated that small models (14B & 32B) trained with our method can outperform frontier models on prediction‑market benchmarks using generated data—with no human bottlenecks.

Messy data in; accurate predictions out.

What You’ll Do

You’ll build cutting-edge systems that teach AI real‑world reasoning, at scale.

• The worlds first platform for Prediction Agents. Create and evolve infrastructure for training and serving prediction agents.
• Scale. Orchestrate distributed training and inference on large multi-node GPU clusters.
• Transform messy data. Convert unstructured streams into training signals suitable for learning and evaluation.
• Learn causes, not just correlations. Design novel objectives, rewards, and calibration methods so models learn causal factors and output well‑calibrated probabilities.
• Experiment relentlessly. Optimize our training framework, tune specific models, and demonstrate state‑of‑the‑art results on benchmarks.
• Own delivery end‑to‑end. Ship from data collection to deployment.

What We Expect from You

• Excellent python chops & experience fine-tuning LLMs.
• Track record of shipping impactful systems.
• Ownership mindset and comfort operating with autonomy.
• Proof of skill — GitHub, portfolio, papers, or demos.
• Scale experience: you’ve worked beyond toy setups—millions of samples and large, distributed GPU clusters.
• Technical range: rapid analysis in a notebook to robust, scalable data and training pipelines.
• Tooling: Verl, Hugging Face, PyTorch, pandas, and standard training/data tooling.
• Data intuition: strong grasp of statistics, probability, data hygiene, and machine‑learning best practices.
• Cloud fluency: Comfortable across cloud platforms. We work with Modal, GCP, Azure, and AWS just to name a few.

What You’ll Get Out of It

• Cutting edge, for real. Help advance a new way to train LLMs for prediction from unstructured data using RL and self‑play, and build a framework that can eventually teach AI things humans don’t know.
• Meaningful impact. Work on critical use cases like defense, healthcare, and major institutions.
• Product ownership. Own major pieces of our stack—from design to shipped systems.
• Speed & autonomy. High‑trust, fast‑paced environment where you set direction, ship often, and see rapid real‑world impact.
• Tight‑knit team & company‑wide collaboration. Work closely with a small team and partner across the whole startup (product, engineering, and customer teams) to carry work from research to real‑world impact.

Location

Remote-first with a growing hub in Brooklyn / NYC (preferred); meaningful overlap with Eastern Time is required.