Lead multiple engineering teams building AI-powered features for Datadog's observability platform, including ML models, agents, and natural language interfaces. Requires deep AI/ML expertise, experience shipping production AI products, and strong people management skills.
234k – 300k/yr
HybridEngineering Management
About the role
What You'll Do
Lead and grow multiple teams of engineers, applied scientists, and managers working on AI-powered features across the Datadog platform
Set technical vision and strategy for your area in partnership with Product
Ship AI products end-to-end, from data pipelines and model training through evaluation, deployment, and production operations
Coach and develop senior engineers and managers, building a culture of high performance, psychological safety, and clear feedback
Drive collaboration across Applied AI, product, and partner engineering teams to deliver on Datadog's AI priorities
Build evaluation and quality practices for AI systems, offline benchmarks, online metrics, and continuous improvement loops
Own operational excellence for your teams' systems: reliability, on-call, incident response, and technical quality
Hire and plan for organizational growth as the AI group scales
Help shape Datadog's broader AI roadmap and promote best practices in ML engineering and career development across the organization
Who You Are
An experienced technical leader with a strong background in AI, machine learning, or data science
Proven track record building and shipping ML or AI-powered products in production
Experience leading and mentoring multiple teams, including senior engineers and engineering managers
Deep technical expertise in one or more areas: large language models, agentic systems, anomaly detection, time-series modeling, NLP, retrieval-augmented generation, or ML infrastructure
Comfortable partnering with Product to set vision and strategy, and turning ambiguous problems into clear plans
A strong people manager who can attract, develop, and retain top AI and engineering talent
BS/MS/PhD in Machine Learning, Computer Science, Engineering, or related field, or equivalent professional experience
Benefits and Growth
New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
Continuous professional development, product training, and career pathing
An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
Free, global Spring Health benefits for employees and dependents age 6+
Leads engineering team building metadata foundations, catalog integrations, and unified data pipelines for Snowflake's interoperable Lakehouse platform supporting Iceberg tables and open ecosystems. Drives innovation in distributed systems and multi-cloud architectures for data mobility.
236k – 339k/yr
HybridEngineering Management
Sales Manager
TabsNew York, NY
The Sales Manager will lead and develop a team of Account Executives, drive revenue performance, and shape go-to-market strategies. This role involves coaching AEs, managing pipelines, and collaborating cross-functionally to achieve sales excellence.
230k – 315k/yr
On-site5+ YOEEngineering Management
Engineering Manager - Forecasting & Scheduling
AssembledUnited States
Assembled is seeking an Engineering Manager for their Forecasting & Scheduling team to lead the development of algorithms and user experiences for workforce management in the AI era. This role involves setting technical direction, making product decisions, and partnering with cross-functional teams to deliver impactful solutions.
230k – 270k/yr
Remote5+ YOEEngineering Management
Engineering Manager - Forecasting & Scheduling
AssembledSan Francisco, CA
Lead the Forecasting & Scheduling team, setting technical direction for algorithms and UX that optimize staffing of human agents and AI. Partner with PM, design, ML engineers, and customers while balancing people leadership and deep technical problem solving.
230k – 270k/yr
On-siteEngineering Management
Machine Learning Manager, Notifications Relevance
RedditUnited States
Lead a team of ML engineers building notifications relevance systems at Reddit, focusing on recommender systems for personalization and user re-engagement. Requires 5+ years in large-scale ML and 2+ years managing engineering teams.