Machine Learning Engineer
Job Description
Spearbit, founded in 2021 by former Ethereum Foundation Solidity engineers, is at the forefront of Web3 security. The company has developed Cantina, dubbed the "GitHub for Security," which serves as a comprehensive platform connecting security researchers with projects requiring expertise. The platform currently supports collaborative security reviews, security competitions, bug bounty programs, incident response, and AI code analysis.
As a Machine Learning Engineer at Spearbit, you'll be instrumental in advancing their AI capabilities within Cantina Code. The role focuses on leveraging advanced Large Language Models (LLMs) to enhance code security and collaboration. You'll be working on cutting-edge technologies, designing and implementing multi-agent pipelines for smart contract analysis, fine-tuning LLMs, and building sophisticated AI systems for code understanding.
The position offers an exciting opportunity to work at the intersection of AI and blockchain security. You'll be collaborating with security researchers to transform attack patterns into detection mechanisms, implementing AI solutions through FastAPI micro-services, and managing deployments on AWS. The role requires expertise in Python, ML systems, and LLM fine-tuning, along with the ability to understand and work with blockchain technologies, particularly Solidity.
Working at Spearbit means joining a talented team that's building the future of code security. The company offers competitive compensation including equity, comprehensive benefits, and the chance to work with high-profile clients in the blockchain industry. This role is perfect for someone passionate about applying AI to solve complex security challenges in the Web3 space.
Responsibilities For Machine Learning Engineer
- Design, prototype, and productionise multi-agent pipelines that collaborate to scan Solidity smart-contract repositories
- Build and fine-tune bespoke LLMs and smaller custom models for code understanding
- Encode and maintain agentic knowledge for autonomous improvement
- Implement concept-drift & data-drift monitors
- Expose capabilities through FastAPI micro-services and deploy them on AWS
- Collaborate with security researchers to turn novel attack patterns into detection heuristics
- Deploy and manage machine learning models and services on AWS
- Collaborate with product managers, data scientists, and engineers to integrate AI solutions
Requirements For Machine Learning Engineer
- Experience with Python and building ML systems in production
- Practical experience fine-tuning LLMs (LoRA, QLoRA, DPO/RLHF)
- Familiarity with agentic / autonomous-AI frameworks (LangGraph) and their evaluation challenges
- Solid grounding in software-2-software architectures: REST contracts, event-driven workflows, distributed tracing
- Comfort learning/reading Solidity and reasoning about smart-contract security primitives
Benefits For Machine Learning Engineer
- Competitive salary and performance-based compensation opportunities
- Opportunity to work in an early-stage startup with a talented and passionate team
- Exposure to high-profile clients in the blockchain and cryptocurrency industry
- Comprehensive health, dental and vision benefits
- 401k matching program
