AI/ML Engineer

Ambient (Headquarters: Remote)

AI ML LLM Large Language Models Distributed Training Fine-tuning Inference Python PyTorch JAX DeepSpeed Distributed Systems Blockchain Proof of Work AI Engineer

Ambient is hiring AI/ML Engineers to build and optimize the artificial intelligence components at the heart of our Proof of Work L1 blockchain. You will work on implementing, testing, and deploying systems for distributed training, fine-tuning, and verified inference of our massive (>600B parameter) language model across a decentralized network.

Company Overview: Ambient is creating a cryptocurrency network where AI computation itself forms the basis of the proof of work. We're building an SVM-compatible L1 (a Solana fork) that replaces Proof of Stake with Proof of Work based on LLM tasks, aiming for Bitcoin-like economics coupled with AI productivity. Our network supports a single, massive open-source LLM and uses a hyper-optimized Proof of Logits (PoL) consensus for verified inference.

Role & Responsibilities:
- Implement and optimize algorithms for distributed LLM training, fine-tuning, and inference on decentralized hardware.
- Develop and maintain the software infrastructure supporting our AI workloads, potentially using Python and other relevant languages/frameworks.
- Work on the Proof of Logits (PoL) system for efficient and secure verification of AI computation.
- Collaborate with researchers to implement novel AI techniques and model improvements.
- Build monitoring, testing, and evaluation frameworks for our AI systems.
- Contribute to the integration of AI components with the underlying blockchain infrastructure.
- Participate in system design discussions and code reviews.

Technical Skills Required:
- Strong background in Machine Learning and Deep Learning, particularly NLP and Large Language Models.
- Proficiency in Python and ML frameworks (e.g., PyTorch, JAX, TensorFlow).
- Experience with or strong interest in distributed systems and parallel computing.
- Experience with distributed training frameworks (e.g., DeepSpeed, FSDP, Megatron-LM) is a major plus.
- Solid software engineering skills, including testing, debugging, and performance optimization.
- Ability to work effectively in a collaborative, fast-paced research and development environment.

Ideal Candidate:
- Hands-on experience training or fine-tuning large-scale neural networks.
- Enthusiasm for tackling complex technical challenges at the intersection of AI, distributed systems, and blockchain.
- Experience contributing to open-source AI/ML projects.
- Bachelor's or Master's degree in Computer Science, AI, or a related field, or equivalent practical experience.