As part of Datadog’s GTM Enablement department, the Technical Solutions (TS) Enablement team helps share knowledge, provide ongoing education, create training materials, and maintain processes for our internal product experts including sales engineers, technical support engineers, technical customer enablement, and technical account managers. Our team supports a top-tier product that truly solves customer problems. We share technical and product expertise with the technical solutions team through demos, e-learning, live training, documentation, and ongoing support.

Datadog continues to grow at a rapid rate and with this exciting growth, our onboarding and ongoing training is also quickly increasing in scale and complexity. Technical Solutions Field Enablement Managers play a key part in fostering a highly engaging, positive, and professional learning environment for our technical learners. This position is responsible for providing onboarding and ongoing enablement training to our TS teams in the APJ region. We are looking for a structured communicator with a knack for simplifying the complex to develop and deliver the training content, curriculum, and programs that enable our technical solutions people to be successful in their jobs. This position is based in our Tokyo office, offering the opportunity to collaborate with local and global teams, while working at the forefront of cloud monitoring and observability.

Datadog is a monitoring service for hybrid cloud applications, assisting organizations in improving agility, increasing efficiency, and providing end-to-end visibility across the application and organization. These capabilities are provided on a SaaS-based data analytics platform that enables DevOps and other teams to accelerate go-to-market efforts, ensure application uptime, and successfully complete digital transformation initiatives.

At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Implement and facilitate effective in-person and virtual onboarding and technical training programs that will improve the performance and productivity of the regional technical solutions teams.
• Regionalize training programs by incorporating APJ- or Japan-specific deals, customers, competitors, etc. into the content and talk tracks, and translate into Japanese as needed.
• Partner with the Curriculum team to give feedback, and pilot new programs in development, co-building when appropriate.
• Work closely with first line technical solutions leaders to identify, develop, and deliver ad hoc training as needed.
• Work closely with technical solutions teams to observe and assess their processes, empathize with the realities of troubleshooting and demonstrating Datadog products, evaluate the impact of enablement programs and provide coaching to individual contributors, supporting TS leadership.
• Utilize understanding of Datadog products, technical solutions team performance metrics, and key relationships with cross functional partners to contribute to enablement strategy.
  
  

Who You Are:

• 3-5 years in learning and development or technical enablement, in a training or teaching role working with technical concepts 
• Experience presenting day-long or multi-day trainings
• Demonstrated history of coaching and mentoring colleagues around key role elements 
• Experience developing dynamic and engaging trainings for various audiences
• A self-starter, tech-savvy professional who can easily understand a company’s business requirements and explain Datadog’s value and technical details to C-level executives, a technical guru and everyone in between 
• Outstanding verbal and written communicator; you make the complex simple
• General knowledge of cloud technologies
• Fluency in English and Japanese

Bonus Points:

• Experience at a high growth Software-as-a-Service or technology company
• Experienced with AWS, Azure or GCP
• Knowledge of DevOps
• Experience enabling support or sales engineers with a structured methodology

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:

• High income earning opportunities based on individual performance
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Sales training in MEDDIC and Command of the Message
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global Spring Health benefits for employees and dependents age 6+
• Competitive global benefits
• Giving programs

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

#LI-Hybrid

As part of Datadog’s GTM Enablement department, the Technical Solutions (TS) Enablement team helps share knowledge, provide ongoing education, create training materials, and maintain processes for our internal product experts including sales engineers, technical support engineers, technical customer enablement, and technical account managers. Our team supports a top-tier product that truly solves customer problems. We share technical and product expertise with the technical solutions team through demos, e-learning, live training, documentation, and ongoing support.

Datadog continues to grow at a rapid rate and with this exciting growth, our onboarding and ongoing training is also quickly increasing in scale and complexity. Technical Solutions Field Enablement Managers play a key part in fostering a highly engaging, positive, and professional learning environment for our technical learners. This position is responsible for providing onboarding and ongoing enablement training to our TS teams in the APJ region. We are looking for a structured communicator with a knack for simplifying the complex to develop and deliver the training content, curriculum, and programs that enable our technical solutions people to be successful in their jobs. This position is based in our Singapore office, offering the opportunity to collaborate with local and global teams, while working at the forefront of cloud monitoring and observability.

Datadog is a monitoring service for hybrid cloud applications, assisting organizations in improving agility, increasing efficiency, and providing end-to-end visibility across the application and organization. These capabilities are provided on a SaaS-based data analytics platform that enables DevOps and other teams to accelerate go-to-market efforts, ensure application uptime, and successfully complete digital transformation initiatives.

At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Implement and facilitate effective in-person and virtual onboarding and technical training programs that will improve the performance and productivity of the regional technical solutions teams.
• Regionalize training programs by incorporating APJ- or Japan-specific deals, customers, competitors, etc. into the content and talk tracks, and translate into Japanese as needed.
• Partner with the Curriculum team to give feedback, and pilot new programs in development, co-building when appropriate.
• Work closely with first line technical solutions leaders to identify, develop, and deliver ad hoc training as needed.
• Work closely with technical solutions teams to observe and assess their processes, empathize with the realities of troubleshooting and demonstrating Datadog products, evaluate the impact of enablement programs and provide coaching to individual contributors, supporting TS leadership.
• Utilize understanding of Datadog products, technical solutions team performance metrics, and key relationships with cross functional partners to contribute to enablement strategy.
  
  

Who You Are:

• 3-5 years in learning and development or technical enablement, in a training or teaching role working with technical concepts 
• Experience presenting day-long or multi-day trainings
• Demonstrated history of coaching and mentoring colleagues around key role elements 
• Experience developing dynamic and engaging trainings for various audiences
• A self-starter, tech-savvy professional who can easily understand a company’s business requirements and explain Datadog’s value and technical details to C-level executives, a technical guru and everyone in between 
• Outstanding verbal and written communicator; you make the complex simple
• General knowledge of cloud technologies
• Fluency in English and Japanese

Bonus Points:

• Experience at a high growth Software-as-a-Service or technology company
• Experienced with AWS, Azure or GCP
• Knowledge of DevOps
• Experience enabling support or sales engineers with a structured methodology

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:

• High income earning opportunities based on individual performance
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Sales training in MEDDIC and Command of the Message
• Intradepartmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Access to Inclusion Talks, our Internal panel discussions
• Free, global Spring Health benefits for employees and dependents age 6+
• Competitive global benefits
• Giving programs

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

#LI-Hybrid

Member of Technical Staff, Post-Training

Location: SF Bay Area or Tokyo, Japan
Type: Full-time

About Radical Numerics

Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures for million-token context windows, enabling work toward AI-designed whole genomes and gene-editing tools. 

We believe the next generation of biological foundation models will require not only new and improved pretraining recipes, but also innovation on post-training: the work that turns a powerful base model into a system that is useful, steerable, robust, and scientifically productive. This role sits at that interface between fundamental research and practical engineering.

About the Role

As a Member of Technical Staff, Post-Training at Radical Numerics, you will develop the training and evaluation loops that shape biological world models after pretraining. You will work on the methods, data, and infrastructure required to improve model behavior on real scientific tasks: reasoning over long biological context, following complex objectives, making useful predictions, and interacting reliably with downstream tools and workflows.

This is a hands-on role for someone who wants to both build systems and deepen understanding. You should be excited to run careful experiments, question whether the metrics reflect reality, and translate empirical findings into better recipes, datasets, and productively used models.

What You’ll Do

• Develop and tune post-training recipes. Design and iterate on post-training stages, datasets, reward signals, and hyperparameters for biological world models. Study how choices in data mixtures, objective design, curriculum, and training schedules affect model behavior.
• Build evaluations that actually matter. Collaborate with the science team to develop and refine evaluation suites for biological reasoning, scientific usefulness, long-context behavior, robustness, and model reliability, and to identify when existing benchmarks stop being informative and should be replaced with better ones.
• Debug model behavior end-to-end. Investigate failure modes in training runs and model outputs, distinguish between signal and noise, and trace problems back to data, optimization, evaluation design, or systems issues.
• Work on preference- and feedback-driven learning. Explore methods such as preference modeling, reward modeling, synthetic feedback, or related post-training approaches that improve how models respond to scientific tasks and constraints.
• Improve data for post-training. Help define, curate, or generate high-quality post-training datasets, including expert-informed data, synthetic data, and task-specific examples grounded in biological workflows.
• Study scaling in post-training. Measure how performance changes with dataset size, recipe complexity, compute budget, and model family. Use those results to guide what we scale next and what new directions are worth exploring.
• Collaborate across research and engineering. Work closely with colleagues in training systems, architecture, and biology-facing research to ensure post-training methods are grounded in the realities of large-scale experimentation and downstream scientific use.

What We’re Looking For

• Strong track record in ML research or engineering, especially in frontier-model training, post-training, alignment, evaluation, data quality, or related areas.
• Proficiency in building production-quality software and research infrastructure, ideally in Python and PyTorch, with comfort debugging large-scale training workflows.
• Ability to design careful experiments, interpret ambiguous results, and separate real effects from artifacts, bugs, or benchmark overfitting.
• Excellent written and verbal communication skills, especially the ability to explain technical findings clearly across research, engineering, and scientific collaborators.
• Curiosity, rigor, and a bias toward iteration: you like improving systems by repeatedly tightening the loop between hypotheses, experiments, and insight.

Nice to Have

• Experience with RLHF, RLAIF, preference optimization, reward modeling, rejection sampling, or other post-training methods for large models.
• Experience designing or operating evaluation frameworks for model quality, reliability, safety, or scientific task performance.
• Familiarity with synthetic data generation, annotation workflows, or expert-in-the-loop data collection.
• Background in applied math, systems, computational biology, or another quantitative scientific field.
• Contributions to open-source ML systems, model tooling, or research infrastructure.

Why Radical Numerics

• Help build the post-training stack for multimodal biological world models that could materially improve how we detect, understand, and respond to problems in health and biology. 
• Work in an environment that combines distributed systems, model architecture, and numerics research with real biological applications. 
• Join a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and research institutes. 
• Competitive compensation, comprehensive benefits, and support for continual learning. 

Member of Technical Staff, Pre-Training Science

Location: SF Bay Area or Tokyo, Japan
Type: Full-time

About Radical Numerics

Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures that unlocked million-token context windows, enabling work toward AI-designed whole genomes and real gene-editing tools. 

We believe that biological foundation models will require advances not only in systems and scale, but also in the science of pretraining itself: how models learn from diverse biological data, what objectives produce useful representations, and how training recipes evolve as models and datasets grow. This role is focused on that core scientific agenda.

About the Role

As a Member of Technical Staff, Pre-Training Science at Radical Numerics, you will work on the science of how biological world models learn during large-scale training. You will develop new pretraining methods, study scaling behavior, and design training recipes that improve efficiency, generalization, and downstream scientific usefulness.

This role blends research and engineering. You should be excited to move fluidly between theory and implementation: reading technical literature, proposing new hypotheses, running large-scale experiments, and writing high-performance code that turns ideas into measurable progress. 

What You’ll Do

• Research and develop new pretraining methodologies. Explore how biological world models learn from multi-modal data (eg, sequence, structure, and image data), and develop new objectives, training strategies, or architectural ideas that improve representation quality and downstream performance.
• Study scaling behavior. Investigate how training dynamics change with model size, data composition, context length, and compute budget. Use empirical results to inform scaling protocols and future research priorities.
• Design data curricula and sampling strategies. Build and refine mixtures, curricula, and sampling policies that improve learning efficiency, generalization, and robustness across biological modalities and tasks.
• Work on architecture, algorithms, and optimization. Evaluate ideas in model design, optimization, long-context learning, and training stability that make large-scale biological pretraining more effective.
• Run large-scale experiments rigorously. Design, execute, and analyze experiments with strong empirical discipline. Distinguish real effects from bugs, noise, or benchmark artifacts, and convert findings into better training recipes.
• Collaborate closely with infrastructure and data teams. Work across the stack to ensure large-scale experiments are reproducible, efficient, and instrumented well enough to support fast scientific iteration.
• Define evaluations for pretraining progress. Build and improve evaluation suites that measure representation quality, long-context behavior, transfer to downstream biological tasks, and scientific utility.

What We’re Looking For

• Strong track record in ML research or engineering, especially in large-scale model training, pretraining, representation learning, optimization, scaling laws, or related areas.
• Ability to design, run, and analyze experiments thoughtfully, with strong research judgment and empirical rigor. 
• Proficiency in Python and modern deep learning tooling such as PyTorch, plus comfort debugging distributed or high-performance training systems at scale. 
• Experience working in distributed or high-performance computing environments.
• Excellent written and verbal communication skills, especially the ability to explain complex technical findings clearly across engineering, research, and scientific collaborators.
• Intellectual curiosity and a bias toward experimentation, iteration, and continuous improvement. 

Nice to Have

• Experience training or analyzing frontier or foundation models.
• Strong grasp of probability, statistics, optimization, and ML fundamentals.
• Familiarity with curriculum learning, data selection, active learning, or data-quality methods for large-scale training.
• Experience designing or maintaining evaluation frameworks for large models.
• Contributions to open-source ML systems, datasets, or research tooling.
• Background in applied math, systems, computational biology, physics, mathematics, or another strongly quantitative field. 

Why Radical Numerics

• Help build the multimodal biological world models needed for rapid detection, response, and countermeasures across global health. 
• Work on fundamental questions in pretraining science while staying close to real scientific applications in biology.
• Join a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and research institutes. 
• Competitive compensation, comprehensive benefits, and support for continual learning.