1

Ai Research Jobs (NOW HIRING)

Mission Design, train, ship, iterate on, and innovate on the AI brains behind The Path's AI Therapist. Combine research, data science, and engineering to create models, orchestration, and evaluation ...

We're hiring Research Engineers to join teams across Meta working at the intersection of frontier AI and real-world product impact. You'll be embedded directly in Facebook's ecosystem, helping ...

AI research scientist

San Francisco, CA · On-site

$234K - $349K/yr

As an AI research scientist, you'll be at the center of that work. You'll drive a high-impact research agenda focused on large language models, agentic reasoning, and the system-level capabilities ...

AI Research Engineer

Seattle, WA · On-site

$190K - $280K/yr

About the Role We are seeking an experienced research engineer to join our team. As a critical member of our interdisciplinary group, you will collaborate closely with AI scientists, engineers, and ...

We're hiring Research Engineers to join teams across Meta working at the intersection of frontier AI and real-world product impact. You'll be embedded d.

We're hiring Research Engineers to join teams across Meta working at the intersection of frontier AI and real-world product impact. You'll be embedded directly in Facebook's ecosystem, helping ...

Work with the Fundamental AI Research Team to produce high impact AI research. Responsibilities: * Lead, collaborate, and execute on research that pushes forward the state of the art in large ...

We're hiring Research Engineers to join teams across Meta working at the intersection of frontier AI and real-world product impact. You'll be embedded d.

next page

Showing results 1-20

Ai Research information

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

To thrive as an AI Researcher, you need a strong background in mathematics, computer science, and machine learning, typically supported by an advanced degree (Master’s or PhD) in a related field. Proficiency with programming languages such as Python, deep learning frameworks like TensorFlow or PyTorch, and experience with data analysis tools are essential. Creativity, critical thinking, and effective collaboration are vital soft skills for innovating and communicating complex ideas. These skills ensure researchers can develop novel AI solutions, contribute to cutting-edge advancements, and work effectively within interdisciplinary teams.

What is the difference between Ai Research vs Data Scientist?

AspectAi ResearchData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or related fieldsDegree in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, R&D departments, academiaBusiness environments, tech companies, analytics teams
Industry UsageDeveloping new AI algorithms, models, and theoriesAnalyzing data, building predictive models, generating insights

While both roles involve working with data and algorithms, Ai Research focuses on developing new AI methods and theories, often in research settings. Data Scientists apply these techniques to solve practical business problems through data analysis and modeling. The roles overlap in skills and tools but differ mainly in their objectives and work environments.

What is AI research?

AI research is the scientific study and development of algorithms, models, and systems that enable computers to perform tasks that typically require human intelligence. This includes areas such as machine learning, natural language processing, computer vision, robotics, and reasoning. AI researchers work to advance the field by creating new methods, improving existing technologies, and exploring ethical and societal impacts of artificial intelligence. Their work is crucial in driving innovation across industries, from healthcare to autonomous vehicles.

What are some common challenges faced by AI researchers when working on real-world projects?

AI researchers often encounter challenges such as dealing with insufficient or biased data, ensuring model interpretability, and balancing research innovation with project deadlines. Collaborating closely with data engineers, software developers, and domain experts is essential to address these hurdles and translate theoretical models into practical solutions. Additionally, aligning research outcomes with business goals while keeping up with the rapidly evolving AI landscape can be demanding, but it also provides valuable opportunities for continuous learning and career development.
More about Ai Research jobs
What cities are hiring for Ai Research jobs? Cities with the most Ai Research job openings:
What are the most commonly searched types of Ai Research jobs? The most popular types of Ai Research jobs are:
What states have the most Ai Research jobs? States with the most job openings for Ai Research jobs include:
Infographic showing various Ai Research job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution.
AI Research Scientist - Datadog AI Research (DAIR)

AI Research Scientist - Datadog AI Research (DAIR)

Datadog

New York, NY

Other

Re-posted 21 hours ago


Datadog rating

9.6

Company rating: 9.6 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

1st of 451 rated business services


Job description

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products.

Building on our track record of AI-powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security.

We are focused on two research areas:

  1. World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents.
  2. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost.

What You'll Do:

  • Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability
  • Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure
  • Design and build simulated environments and RL training loops for on-policy agent training and evaluation
  • Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products
  • Stay at the forefront of foundation models, world models, and RL-based agent research
  • Contribute to research publications, present at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks

Who You Are:

  • You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience)
  • You have extensive experience designing and implementing deep learning models and agents, with a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch)
  • You have a track record of impactful publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR)
  • You are familiar with efficient training, post-training, and inference techniques for large foundation models
  • You can explain complex models and research findings to both technical and non-technical audiences

Bonus Points (any of the following):

  • Experience bridging research and real-world product applications, especially with large foundation models, world models, or RL-trained agents
  • Passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
  • Experience writing production data pipelines and applications
  • Hands-on experience with GPU programming and optimization, including CUDA

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:

  • Competitive global benefits 
  • New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
  • Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris
  • Opportunity to attend and present at conferences and meetups
  • Intra-departmental mentor and buddy program for in-house networking
  • An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

About Datadog: 

Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers' entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

Equal Opportunity at Datadog:

Datadog is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

Your Privacy:

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog's Applicant and Candidate Privacy Notice.


What Datadog employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom