Full-Time

Senior Software Engineer

ML Platform & Experience

Confirmed live in the last 24 hours

Datadog

Datadog

5,001-10,000 employees

Cloud monitoring and analytics solutions provider

Data & Analytics
Enterprise Software

Compensation Overview

$130k - $300kAnnually

+ Stock Equity (RSUs) + Employee Stock Purchase Plan (ESPP)

Senior

New York, NY, USA

Hybrid workplace; in-office presence required.

Category
Backend Engineering
FinTech Engineering
Software Engineering
Required Skills
Data Science
Requirements
  • You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or a related scientific field or equivalent experience
  • You are a software engineer at heart with 5+ years of professional experience in building distributed systems, data science applications, and/or machine learning engineering
  • You have proven ability to architect, build, and operate distributed systems to solve problems at a high scale
  • You have extensive experience executing projects that span data engineering, data science, and machine learning
  • You ship fast, route around blockers, and are comfortable working with ambiguity
  • You’re happy to jump into any part of the stack and do whatever’s needed to move a project forward
Responsibilities
  • Lead the design, development, and deployment of scalable tools and infrastructure to support the efforts of our data scientists (model training & serving infra, CI/CD, orchestration, deployment,...)
  • Create a development ecosystem that enables rapid experimentation and deployment of algorithms
  • Stay on the cutting edge of development in the MLOps domain and document best practices to share across teams
  • Drive the technical growth of Machine Learning as a distributed capability at Datadog, spanning all product areas, as well as leverage DD’s knowledge in observability to contribute back to the MLOps community

Datadog provides tools for monitoring and managing IT infrastructure in the cloud. Their services help organizations track various aspects of their operations, including application performance, security, and network activity. Datadog's products work by collecting data from different sources within an organization's IT environment, allowing users to analyze performance metrics and detect issues in real-time. This enables businesses to ensure their systems run smoothly and efficiently. Unlike many competitors, Datadog offers a wide range of monitoring solutions that cater to specific needs, such as cloud migration and IoT monitoring, all within a single platform. The company's goal is to help businesses optimize their IT operations and improve overall performance through effective monitoring and analytics.

Company Stage

IPO

Total Funding

$143.9M

Headquarters

New York City, New York

Founded

2010

Growth & Insights
Headcount

6 month growth

16%

1 year growth

36%

2 year growth

55%
Simplify Jobs

Simplify's Take

What believers are saying

  • Datadog's strategic partnerships, such as with JFrog, enhance its platform capabilities and expand its market reach.
  • The launch of new tools like Log Workspaces and Data Jobs Monitoring highlights Datadog's focus on improving user experience and operational efficiency.
  • Significant investments from entities like Riverview Trust Co and participation in funding rounds for AI startups indicate strong financial backing and growth potential.

What critics are saying

  • The competitive landscape in cloud monitoring and analytics is intense, with major players like AWS and Google Cloud posing significant threats.
  • Frequent introduction of new features may lead to complexity and potential integration issues for existing users.

What makes Datadog unique

  • Datadog's comprehensive suite of monitoring tools, including APM, security monitoring, and IoT monitoring, sets it apart from competitors who may offer more limited solutions.
  • Their scalable subscription-based pricing model allows them to cater to both small businesses and large enterprises, providing flexibility that many competitors lack.
  • The recent introduction of features like Live Debugger and Kubernetes Autoscaling demonstrates Datadog's commitment to continuous innovation and addressing real-time operational challenges.

Help us improve and share your feedback! Did you find this helpful?