ML Engineer [IC3] at Sourcegraph

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Software, Enterprise SoftwareIndustries

Requirements

Candidates should possess a Bachelor's degree in Computer Science or a related field, along with at least 5 years of experience in Machine Learning engineering, specifically focusing on productionizing large-scale ML models. Demonstrated experience in industrial settings is essential, and a strong understanding of full-stack development is required.

Responsibilities

As an ML Engineer, you will be responsible for building and deploying Machine Learning models at scale, leveraging Sourcegraph's code intelligence data and platform. This includes developing and maintaining AI agents, automating repetitive tasks like vulnerability remediation and migrations, and contributing to innovation through areas such as bug triage and AI-driven code reviews. You will also play a key role in ensuring the seamless integration of these models into enterprise workflows and collaborating with the team to unlock the full potential of Sourcegraph’s code intelligence.

Skills

Machine Learning

ML Models

Full Stack

AI Agents

Automated Bug Triage

Code Reviews

Vulnerability Remediation

Migration Automation

Sourcegraph

Code intelligence platform for developers

About Sourcegraph

Sourcegraph provides a code intelligence platform designed to improve how developers work with their code. The platform features an AI coding assistant named Cody, which helps developers understand, navigate, and automate their codebases. Sourcegraph offers tools for code search, bug fixing, refactoring, and enhancing performance, all within a single interface. This makes it easier for developers to manage their code effectively. Unlike many competitors, Sourcegraph focuses on enhancing code security, speeding up developer onboarding, and promoting code reuse, making it particularly valuable for enterprises looking to improve engineering speed and software quality. The goal of Sourcegraph is to boost overall team efficiency and code health for organizations, and it is trusted by major companies in the tech industry.

San Francisco, CaliforniaHeadquarters

2013Year Founded

$216.9MTotal Funding

SERIES_DCompany Stage

Enterprise Software, AI & Machine LearningIndustries

51-200Employees

Benefits

Work fully remote

Unlimited PTO

Generous travel budgets

Competitive pay + equity

Medical, dental, & vision

Professional development

Office budget

Wellness budget

Family planning benefits

Risks

Anthropic's Claude AI model poses a competitive threat to Sourcegraph's AI tools like Cody.

A recent data breach at Sourcegraph may undermine customer trust and lead to scrutiny.

Advanced AI tools like Ironclad Contract AI could overshadow Sourcegraph if innovation lags.

Differentiation

Sourcegraph offers universal code search, enhancing developer productivity across multiple languages and tools.

The Cody AI assistant aids in code comprehension, navigation, and automation for developers.

Sourcegraph's Code Insights provides analytics for a comprehensive view of codebases.

Upsides

The rise of AI-driven code completion tools presents opportunities to enhance Cody's capabilities.

Remote-first development trends align with Sourcegraph's remote workplace model, boosting collaboration.

Growing focus on code security increases demand for Sourcegraph's enhanced security features.

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