Job description
Machine Learning Engineer
$130,000 to $220,000 + Equity + Benefits + PTO
Hybrid - Palo Alto, CA
Are you an AI/ML Engineer looking to leverage your technical expertise and interpersonal skills within a role where you will deliver highly innovative solutions to Fortune500 clients, whilst working with some of the brightest minds in the space?
This is an extraordinary opportunity to join a fast-growing startup with 9-figure funding, in a role where you will be working within a team at the very forefront of innovation within the enterprise legal tech sector, and benefiting from strong technical mentorship.
This company is creating AI-driven legal automation solutions designed to help enterprise legal teams optimize compliance, contract analysis, and risk assessment. By leveraging NLP and machine learning, their AI agents efficiently analyze legal documents, enforce regulatory compliance in marketing campaigns, and identify potential risks. This streamlines workflows, reducing manual effort and enabling legal professionals to focus on high-level strategy.
In this role, you will be responsible for the building, training, deployment and optimization of production-grade models, as well as the processing and analysis of large, complex datasets to extract actionable insights. You will work directly with clients, in addition to cross functional teams in order to translate business objectives into AI-powered strategies and deliverables.
This is a rare opportunity to get involved a financially stable start-up with a huge potential to disrupt the enterprise legal tech sector, working on highly innovative models whilst continuing to technically develop your skillset.
The Person:
* Technical skillset: Python, PyTorch, TensorFlow, MLFlow
* Experience building and deploying LLMs, RAGs, evaluation techniques, and machine learning frameworks
* Experience in diagnosing and resolving AI/ML model issues, including model drift, data quality challenges, and performance optimization
The Role:
* Work in a technical environment, as well as directly with clients as the technical point of contract
* Build, train, and deploy production-grade models, and optimize existing models
* Hybrid in Palo Alto, CA
