1 year ago
Base Salary
$272k - $340k/yr
Responsibilities
- Own GenAI initiatives within APM and lead the design, development, and deployment of ML/AI-powered features across multiple teams.
- Guide long-term strategy and technical direction for GenAI workflows across APM and related products.
- Build and benchmark GenAI/ML models using state-of-the-art techniques.
- Collaborate with cross-functional teams to build automated investigation and triaging tools.
- Influence product direction by advocating for end users and applying a product mindset.
- Provide technical direction through ambiguity, scaling challenges, design reviews, technical talks, and working groups.
- Mentor engineers and contribute thought leadership to Datadog’s senior engineering community and company-wide initiatives.
Requirements
- BS, MS, or PhD in a scientific field or equivalent experience.
- 10+ years of relevant engineering experience, including 4+ years leading cross-team technical initiatives.
- Proven track record leading large-scale GenAI/ML initiatives in a product-driven environment.
- Highly proficient in model development, deployment, evaluation, and optimization, with hands-on experience building end-to-end ML systems.
- Ability to drive initiatives across cross-functional teams and solve ambiguous challenges.
- Strong product mindset, communication skills, mentoring ability, and commitment to cultivating a high-performing engineering culture.
Benefits
- Hybrid workplace.
- Competitive global benefits, including healthcare, dental, parental planning, mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.
- Continuous professional development.
- Competitive salary and equity package, with possible variable compensation.
Categories
About Datadog
Datadog is the essential monitoring platform for cloud applications. We bring together data from servers, containers, databases, and third-party services to make your stack entirely observable. These capabilities help DevOps teams avoid downtime, resolve performance issues, and ensure customers are getting the best user experience.