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Ai Research Engineer Jobs (NOW HIRING)

About Chai Discovery Chai is a research lab working on AI to unlock biology. Our models design new ... We are changing how biologists develop drugs, just as language models are changing how engineers ...

AI Research Engineer- Speech 1

Redmond, WA · On-site

$229K/yr

About Job AI Engineer: Speech/Audio Centific AI Research About Centific AI Research Centific AI Research is at the forefront of developing cutting-edge AI solutions that bridge the gap between ...

Senior/Staff AI Research Engineer

San Jose, CA · On-site +1

$180K - $350K/yr

Hume AI is seeking talented researchers and engineers interested in working with our AI research team to build state-of-the-art speech-language models (SLMs). Our new SLM training method ...

Iterate on AI model development; starting from the data needed for training, architecture, input/output representations, evaluation, and deployment. * Collaborate with other researcher engineers to ...

Iterate on AI model development; starting from the data needed for training, architecture, input/output representations, evaluation, and deployment. * Collaborate with other researcher engineers to ...

Senior AI Research Engineer

San Jose, CA · On-site

$160K - $180K/yr

Iterate on AI model development; starting from the data needed for training, architecture, input/output representations, evaluation, and deployment. * Collaborate with other researcher engineers to ...

Staff AI Research Engineer

San Jose, CA · On-site

$190K - $240K/yr

Iterate on AI model development; starting from the data needed for training, architecture, input/output representations, evaluation, and deployment. * Collaborate with other researcher engineers to ...

The Research Engineer will play a crucial role in building and training AI agents for vulnerability discovery and exploitation. Responsibilities : • Build State-of-the-Art AI Agentic pipelines ...

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Ai Research Engineer information

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$37K

$106K

$142.5K

How much do ai research engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for ai research engineer in the United States is $106,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $104,000.00 per year, depending on experience, location, and employer.

What does an AI Research Engineer do?

An AI Research Engineer designs, develops, and tests artificial intelligence models and algorithms. They work on advancing the state-of-the-art in machine learning, deep learning, and related fields, often collaborating with data scientists and software engineers. Their responsibilities typically include experimenting with new approaches, implementing prototypes, publishing research findings, and helping to integrate AI solutions into products or services. The role requires strong programming skills, a deep understanding of mathematics and statistics, and the ability to keep up with rapid advancements in AI technology.

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

To thrive as an AI Research Engineer, you need strong expertise in mathematics, machine learning algorithms, programming (especially Python), and typically a graduate degree in computer science or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience using large datasets, and sometimes knowledge of cloud computing platforms are commonly required. Creativity, problem-solving abilities, and effective collaboration are crucial soft skills that distinguish top performers in this role. These skills and qualities are essential for developing innovative AI models, solving complex research problems, and contributing impactful solutions in a rapidly evolving field.

What are some common challenges AI Research Engineers face when transitioning research models into production environments?

AI Research Engineers often encounter challenges when moving models from research to production, such as ensuring scalability, optimizing for real-world data variability, and maintaining model performance under resource constraints. Additionally, integrating research models with existing systems and workflows can require close collaboration with software engineers and data engineers. Addressing issues like reproducibility, monitoring, and model retraining is crucial for long-term success in production settings. Proactive communication and a strong understanding of both research and engineering principles help overcome these hurdles.

What is the difference between Ai Research Engineer vs Data Scientist?

AspectAi Research EngineerData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with machine learning frameworksDegree in Statistics, Computer Science, or related fields; strong analytical skills
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics teams, data-driven companies, consulting firms
Employer & Industry UsageTech companies, research institutions, AI startupsFinance, healthcare, marketing, e-commerce
Common Search & Comparison IntentUnderstanding roles in AI research and developmentAnalyzing data to inform business decisions

While both roles involve working with data and algorithms, Ai Research Engineers focus on developing new AI models and advancing AI technology, often in research settings. Data Scientists analyze and interpret complex data to help organizations make strategic decisions. The roles overlap in skills like programming and machine learning, but their primary goals and work environments differ.

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AI Research Engineer - Datadog AI Research (DAIR)

AI Research Engineer - Datadog AI Research (DAIR)

Datadog

New York, NY • On-site

Full-time

Medical, Dental, Retirement, PTO

Posted 19 days ago


Job description

As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production.
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:
  • Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling
  • Implement models, run experiments at scale, and profile for reliability, performance, and cost
  • Build simulation environments and replay infrastructure for agent training and evaluation
  • Orchestrate distributed training and distributed RL with Ray, including scheduling, scaling, and failure recovery
  • Establish rigorous automated benchmarks and regression tests for world model predictions, agent performance, and simulation fidelity
  • Collaborate with Research Scientists, Product, and Engineering to integrate capabilities into Datadog's products and to harden prototypes into reliable services
  • Contribute to research publications at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and produce high-quality code, documentation, and open-source artifacts

Who You Are:
  • You have depth in distributed computing, RL Infra, and ML systems for training and inference at scale; experience with Ray, Slurm, or similar frameworks is a plus
  • You are proficient in Python, familiar with a systems language (e.g., Rust, C++, or Go), and comfortable with modern cloud and data infrastructure
  • You have practical experience implementing and operating ML training and inference systems (e.g., PyTorch or JAX), including containerization, orchestration, and GPU acceleration
  • You have practical experience with large-scale model training and fine-tuning, including frameworks like Megatron-LM, DeepSpeed, SkyRL, VeRL, or TorchTitan, and techniques such as SFT, RLVR, RLHF, and efficient inference (quantization, speculative decoding)
  • You can explain design and performance trade-offs clearly to both technical and non-technical audiences
  • You have experience supporting or contributing to research publications

Bonus Points (any of the following):
  • You have strong software engineering skills with experience in domains such as observability, SRE, or security
  • You have experience bridging research prototypes and real-world product applications, especially with large foundation models, world models, or RL-trained agents
  • You have a passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
  • You have hands-on experience with GPU programming and optimization, including CUDA
  • You have experience writing production data pipelines and applications
  • You have experience building simulation or sandbox environments for agent training

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.
#LI-Hybrid
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:
$140,000-$400,000 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 into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.
Equal Opportunity at Datadog:
Datadog 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 other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.
Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.
Privacy and AI Guidelines:
Any information you submit to Datadog as part of your application will be processed in accordance with Datadog's Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.