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Full-Time

ML Engineer

Ic3

Confirmed live in the last 24 hours

Sourcegraph

Sourcegraph

51-200 employees

AI-powered code navigation and improvement platform

AI & Machine Learning
Enterprise Software

Compensation Overview

$185kAnnually

+ Equity + Perks + Benefits

Senior

San Francisco, CA, USA

Candidate must reside in San Francisco.

Category
Applied Machine Learning
Natural Language Processing (NLP)
AI & Machine Learning
Requirements
  • 5-8 years of industry experience
  • backend focused ML engineer who has worked on the entire ML lifecycle
  • deployed ML models to production to users and have developed feature pipelines
  • understand the nuances of ML for users to move metrics forward
Responsibilities
  • Start building a trusting relationship with your peers, and learning the company structure.
  • Be set up to do local development, and be actively prototyping.
  • Dive deep into how AI and ML is already used at Sourcegraph and identify ways to improve moving forward.
  • Develop simulated datasets using Gym style frameworks across a number of Cody use cases.
  • Experiment with changes to Cody prompts, context sources and evaluate the changes with offline experimentation datasets.
  • Ship a substantial new feature to end users.
  • Building out feature computation, storage, monitoring, analysis and serving systems for features required across our Cody LLM stack
  • Be contributing actively to the world’s best coding assistant.
  • Developing distributed training & experiment infrastructure over Code AI datasets, and scaling distributed backend services to reliably support high-QPS low latency use cases.
  • Be following all the relevant research, and conducting research of your own.
  • Be fully ramped up and owning key pieces of the assistant.
  • Be ramped up on other relevant parts of the Sourcegraph product.
  • Be helping design and build what might become the biggest dev accelerator in 20 years.
  • Owning a number of ML systems, and building core data and model metadata systems powering the end-to-end ML lifecycle.
  • Be developing a highly scalable, high-QPS inference service providing low latency performance using a mix of CPU and GPU hardware to most efficiently utilize resources.
  • Be driving the technical vision and owning a couple of major ML components, including their modeling and ML infra roadmap.

Sourcegraph is a Code AI platform that leverages AI to aid developers in understanding, navigating, and improving their code. Its technology enables codebase navigation, snippet retrieval, historical context, bug fixing, code refactoring, and performance enhancement, catering to over one million engineers and prominent companies such as Databricks, Plaid, Uber, Lyft, Reddit, GE, and Dropbox.

Company Stage

Series D

Total Funding

$232.1M

Headquarters

San Francisco, California

Founded

2013

Growth & Insights
Headcount

6 month growth

-2%

1 year growth

-2%

2 year growth

-16%
Simplify Jobs

Simplify's Take

What believers are saying

  • The $125 million Series D funding round and a $2.6 billion valuation indicate strong investor confidence and financial stability.
  • The launch of Cody 5.1 and its advanced features can significantly boost developer productivity by automating complex coding tasks.
  • Sourcegraph's continuous innovation, such as the release of Code Insights, positions it as a leader in the AI-assisted software engineering space.

What critics are saying

  • The recent data breach could undermine client trust and affect future business prospects.
  • The competitive landscape for AI-powered coding tools is intensifying, requiring Sourcegraph to continuously innovate to maintain its edge.

What makes Sourcegraph unique

  • Sourcegraph's Cody tool leverages generative AI to write and fix code, setting it apart from traditional code search platforms.
  • The introduction of Code Insights provides developers with a comprehensive analytics tool to better understand their codebase, a feature not commonly found in competing platforms.
  • Sourcegraph's ability to understand context across multiple repositories enhances its automation capabilities, making it a more robust solution for complex software engineering tasks.

Benefits

Work fully remote

Unlimited PTO

Generous travel budgets

Competitive pay + equity

Medical, dental, & vision

Professional development

Office budget

Wellness budget

Family planning benefits