Jobs / United States / Datadog INC
Manager I, Engineering - Anomaly Detection
Datadog INC · 🇺🇸 New York, New York, USA
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 64 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 14 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Datadog INC paid sponsored hires in similar roles4 certified filings for “Manager I, Engineering” (Computer and Information Systems Managers) in NY: $240k–$286k, median $255k. Most were filed at wage level III (50%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Datadog INC
The US Department of Labor certified 64 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 14 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Datadog INC →
About the role
The Anomaly Detection team’s mission is to build scalable, cost effective systems that can automatically detect anomalies in our customers’ system telemetry. The ultimate goal is to be able to intelligently find problems in system telemetry proactively without the need for our customers to set up or configure specific alarms or alerts. This is a chance to work on a team that sits at the intersection of distributed systems and machine learning and applying those techniques at massive scale to serve a real world customer need. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: • Lead and develop an existing engineering team of 8; building trust, setting technical direction, and establishing a high bar for ownership and execution • Own the roadmap and execution for our key signal finding systems • Work closely with product management and applied scientists to build an effective detection system and the right evaluation mechanisms to ensure the quality of our signals • Build and operate both traditional machine learning and LLM based systems in production at scale • Drive the team's AI-native development practices, setting high standards for safety, validation, and increasing agent autonomy over time Who You Are: • Experienced engineering manager with a track record of shipping robust and scalable distributed systems with AI and/or ML components. Distributed systems experience is a must-have • Experience with Java, Python or Go, and experience working in a platform-focused team • Strong cross-team collaboration and stakeholder management skills • Comfortable making significant contributions to product strategy in an ambiguous environment rather than executing a pre-defined roadmap • Direct experience building and training ML models in production environments is a plus • Familiarity utilizing streaming technology (e.g. Flink, Kafka) is a plus Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. Benefits and Growth: • New hire stock equity (RSUs) and employee stock purchase plan (ESPP) • Continuous professional development, product training, and career pathing • Intradepartmental mentor and buddy program for in-house networking • An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups) • Free, global mental health benefits for employees and dependents age 6+ • Competitive global benefits Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan. The reasonably estimated yearly salary for this role at Datadog is: $192,000 — $240,000 USD About Datadog: Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security