About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

Open to overseas candidates/Visa Support

This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

In this role, you’ll further improve bidding strategies, audience targeting and creative optimization by deep-diving RTB ecosystem including SSP/DSP/MMP to design the strategy, enhancing user signals or applying advanced deep learning model

What You’ll Work On

• Deep-dive the RTB ecosystem to identify the potential direction and decide the bidding strategy
• Leverage advance deep learning model to improve bidding/audience model performance
• Co-work with teams to improve real-time/long-term user interaction signal collection and to further improve model performance

What We’re Looking For

• Master’s or PhD’s degree in Computer Science, Machine Learning, Statistics, or related field.
• 3+ years of industry experience on RTB ecosystem
• Proficiency in Python and SQL experience with modern ML frameworks and deep learning model (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills.

#LI-AK1

THE ROLE

The Field Solution Architect (FSA) is the regional business and technical leader for a specific workload, responsible for driving strategy, execution, and measurable business impact and growth for that workload across the assigned geo.  Acting as a Workload Business Owner, the FSA combines deep technical expertise with cross-functional leadership to grow the pipeline, accelerate bookings, and strengthen Everpure’s ecosystem.

The FSA partners closely with district and regional Sales, Sales Engineering, Global Partner Organization, and Marketing leadership to shape and execute go-to-market strategies for their assigned workload. Through annual planning cycles, quarterly sales reviews (QSRs), and ongoing GTM alignment, the FSA advises leadership on market opportunity, customer priorities, and competitive dynamics. By adding workload-specific insights into regional and district strategies, the FSA ensures growth opportunities are clearly represented, prioritized, and translated into actionable plans that drive pipeline creation, bookings, and sustained adoption.

The role requires strong technical depth in Artificial Intelligence, Analytics and/or high performance computing along with a solid understanding of the surrounding ecosystem. Curious and proactive, the FSA drives enablement, knowledge sharing, and new opportunity development to expand Everpure’s impact across the region.

WHAT YOU'LL DO

• Key Responsibilities

  1. Workload Ownership and Business Growth

  Own the business success of your workload across APJ. Operate with a business development mindset and partner with regional GTM leadership to drive structured annual planning, and alignment across sales, SE, marketing and partner organizations.

  Activities:

  • Develop and execute a regional “Workload Business Plan” with measurable goals and actions.
    
    
    
  • Conduct TAM and whitespace analysis to identify growth opportunities.
    
    
    
  • Collaborate with Sales, SE, Marketing, and Partner Leadership to track and execute regional GTM plays for your workload.
    
    
    

   

  • Maintain alignment with HQ and product teams to refine technical messaging (“Why Everpure / Why Now”).
    
    
    
  • Track progress and report quarterly insights and outcomes to leadership.
    
    
    

  2. Strategic Account Engagement

  Take ownership for integrating your solution strategy into the top strategic account Technical Account Plans, ensuring clear actions that drive expansion and sustained workload growth.The FSA acts as a trusted advisor and workload strategist, partnering with account teams, alliance partners, to uncover insights that shape Everpure’s positioning and accelerate workload adoption, pipeline generation, bookings growth.

  • Investigate workload presence within the top-strategic accounts. Understand what the customer is currently using, which vendors and technologies are involved, and where Everpure can add differentiated value.
  • Lead or co-lead strategic pipeline creation customer meetings, workshops, architecture reviews, and workload deep dive sessions
    
    
    
  • Collaborate with alliance and ecosystem partners to gather technical and commercial intelligence that informs competitive strategy and solution fit.
    
    
    
  • Co-develop account-specific workload strategies with AE/SE teams, aligning Everpure’s strengths with the customer’s business priorities.
    
    
    
  • Drive workload-specific opportunity identification, cross-sell, and upsell within key accounts.
    
    
    
  • Lead or support executive briefings (EBCs), roadmap discussions, and innovation workshops to influence long-term architecture decisions.
    
    
    

  3.  Domain Mastery of the Solution

  Be the regional Subject Matter Expert (SME) for the assigned workload demonstrating mastery of both the technology and the sales motion. Align workload strategies and architectures with Everpure Storage’s unique strengths to drive differentiation, technical wins, and measurable customer outcomes.

  • Demonstrate 501-level technical expertise and maintain up-to-date knowledge of the solution area.
    
    
    
  • Proactively drive technical wins in key opportunities and complex deals.
    
    
    
  • Lead architecture reviews, workshops, and technical demonstrations.
    
    
    
  • Provide competitive intelligence, product insights, and updates to the field.
    
    
    
  • Align workload positioning and architectures with Everpure’s platform strengths: Evergreen, simplicity, efficiency, and Enterprise Data Cloud (EDC).
    
    
    
  • Mentor SEs to independently position, design, and size solutions confidently.
    
    
    
  • Maintain readiness of demo environments and FSA lab assets.
    
    
    
    

  4. Ecosystem, Evangelism and Enablement

  Act as the voice and advocate of your workload across Everpure, partners, and the broader ecosystem. Build a strong technical community that amplifies Everpure’s message, strengthens partner capability, and drives mutual success across alliances and the channel.

  • Collaborate with alliance partners, ISVs, and GSIs to identify complementary technologies that strengthen Everpure’s workload positioning.
    
    
    
  • Leverage alliance and partner intelligence to uncover where workloads are deployed today and translate findings into actionable insights for account teams.
    
    
    
  • Lead quarterly enablement sessions and workload-focused workshops for partners, SEs, and sales teams to scale capability and readiness.
    
    
    
  • Represent Everpure at industry events, alliance webinars, and technical forums to promote Everpure’s differentiated workload strategy.
    
    
    
  • Publish technical content: blogs, demos, and videos to reinforce thought leadership and solution relevance.
    
    
    
  • Channel field intelligence, competitive insights, and feature requests to HQ via structured feedback loops and the RFE process.
  1. Operational Excellence

   

  The FSA proactively engages on key opportunities to drive technical wins and contribute to solution design in strategic deals, participating in regional deal reviews when aligned to their solution domain. Operational excellence is measured through consistent execution, data hygiene, and active field leadership that directly supports technical wins and workload growth.

  The role is accountable for maintaining clear, timely, and accurate SFDC updates, including opportunity details and specialist notes, for all engagements. These activities are measured and reviewed as part of the FSA’s overall performance to ensure sustained pipeline quality, execution discipline, and scalable success.

  Activities:

  • Maintain accurate, complete, and timely updates in SFDC for all engaged opportunities, including clear Specialist Notes, to ensure visibility, forecasting accuracy, and execution alignment.
    
    
    
  • Capture and document customer win stories tied to the solution domain on a quarterly basis, highlighting business value, technical differentiation, and repeatable success patterns.

WHAT YOU BRING 

• • Must be fluent in Japanese and proficient in English.
  • Advanced degree in computer science, engineering or mathematics
  • 10+ years’ experience with AI/ML systems across various aspects including LLMs, distributed training and inference optimization, data modelling, HPC, and/or cloud Infrastructure with special emphasis in areas of Compute, Storage, and Networking
  • Proficiency with PyTorch, Ray, Kubernetes, and GPU clusters (NVIDIA/AMD)
    Highly Motivated/Independent and self-starting
  • An outside the box thinker who can think innovatively
  • The ability to quickly dig in to learn and adapt to new technologies
  • Produce solution collateral for customers in the form of presentations, short papers, demos, Proof of concepts and
  • Create public content such as blogs, webinars or podcasts to drive thought leadership
  • Willingness to promote a culture of collaboration by partnering with solution stakeholders across the organization – eg. sales, alliance, marketing
  • A strong desire to mentor and coach team members throughout the organization.
  • Strong problem-solving skills
  • Must be able to communicate effectively and in a constructive manner with management, peers, coworkers as well as customers, partners and key industry stakeholders

   Preferred Qualifications

  • Experienced selling complicated technical solutions to customers.
  • Experience with high-performance storage technologies and data platforms

    (e.g.Kafka, Spark).

#LI-ONSITE

The Role 

As a ML Engineer within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalization, comfort, and collaboration.

You’ll design and deliver ML-driven behaviours that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.

What You’ll Be Working On

• Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.
• Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.
• Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.
• Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.
• Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.
• Collaborate cross-functionally across various teams to ensure integration and iteration velocity.
• Mentor senior engineers and shape the long-term technical direction across Autonomy.
  
  

About you  

Essential 

• Extensive and proven track record of shipping deep learning systems to production.
• Expert in deep learning (esp. sequential models, control, planning, or perception).
• Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.
• Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.
• Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.

Desirable 

• Prior work in autonomous driving, imitation learning, or trajectory prediction.
• Familiarity with personalization, human behavior modelling, or driver intent inference.
• Experience integrating ML systems into production hardware or multi-agent simulation.
  
  

This is a full-time role based in our office in Japan.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.  

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

Open to overseas candidates/Visa Support

This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing, and personalized content recommendation, while ensuring system robustness and scalability in a dynamic market environment.

What You’ll Work On

• Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes.
• Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization.
• Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions.
• Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning).

What We’re Looking For

• Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred.
• 4+ years of industry experience in ad tech, with a focus on performance optimization.
• Proven experience in applied machine learning, especially in CTR prediction, recommendation systems.
• Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills—able to lead end-to-end projects across product, data, and engineering.
• Bonus: Experience working on high-throughput, low-latency real-time systems (e.g., RTB engines, stream inference).

#LI-AK1

About Appier 

Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers businesses to create value through cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of “Making AI Easy by Making Software Intelligent,” Appier helps businesses turn AI into ROI through its Ad Cloud, Personalization Cloud, and Data Cloud—each powered by Agentic AI that enables autonomous, adaptive, and real-time decision-making. Today, Appier operates 17 offices across APAC, the US, and EMEA, and is listed on the Tokyo Stock Exchange. Learn more at www.appier.com.

About the role

AI is reshaping how brands connect with consumers — and at Appier, we’re at the forefront. Our Playable Ads team builds AI-powered ad experiences — from interactive playable formats to video ads — that drive dramatically higher engagement and conversion for the world’s leading apps and games. We’re looking for a Senior Machine Learning Scientist to push the boundaries of what’s possible — applying cutting-edge ML, generative AI, and optimization techniques to create smarter, more compelling ad creatives at scale.

If you’re excited about turning the latest advances in AI into products that reach millions of users, and you thrive at the intersection of research and real-world impact, we’d love to talk.

What You’ll Work On

• Apply ML and generative AI techniques to problems such as creative optimization, user behavior prediction, engagement modeling, and dynamic content generation for various creative formats.
• Own the end-to-end ML lifecycle — from problem framing and experimentation through to production deployment and continuous improvement.
• Collaborate closely with product, engineering, and design teams to translate business goals into well-defined ML solutions.
• Conduct offline and online evaluation, A/B testing, and performance monitoring to continuously improve model quality and business impact.
• Stay current with the latest research in areas like LLMs, multimodal models, reinforcement learning, and recommendation systems, and proactively propose innovative applications to the team.
• Mentor fellow scientists and contribute to the team’s technical standards and culture.

What We’re Looking For

[Minimum qualifications]

• Master’s or Ph.D. in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or a related field.
• Solid understanding of modern ML models and algorithms, with hands-on experience in at least one area such as prediction/optimization, generative models, recommendation systems, computer vision, or reinforcement learning.
• Proficient in Python and ML frameworks such as PyTorch or TensorFlow.
• Practical experience taking ML models from prototype to production, including pipeline development and performance monitoring.
• Strong analytical and problem-solving skills with the ability to diagnose model behavior and improve training pipelines.
• Clear communicator who can explain technical ideas to both technical and non-technical stakeholders and enjoys working in a fast-paced, collaborative environment.
• Proficient in using LLM-powered development tools (e.g., GitHub Copilot, Cursor, ChatGPT) to accelerate productivity.
• Ability to balance cutting-edge research with time-to-market constraints, delivering iterative PoCs that validate product hypotheses and translate into production-ready solutions.

[Preferred qualifications]

• Publications in top AI/ML conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, KDD, RecSys).
• Experience in the advertising technology or MarTech domain.
• Contributions to open-source projects or active involvement in research communities.

#LI-AK1

Member of Technical Staff, AI Bio

Location: SF Bay Area
Type: Full-time

About Radical Numerics

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

About the Role

We are seeking research scientists and engineers working at the intersection of machine learning and biological modeling to develop frontier AI architectures for biological problems.

In this role, you will extend and adapt large model backbones—such as sequence and multimodal foundation models—to enable tasks across genomics, protein biology, and cellular systems. This includes designing post-training pipelines, domain adaptation strategies, and evaluation frameworks that enable state-of-the-art  ML frameworks  to reason over biological data.  You likely know the inner workings of frontier bio models such as AlphaFold, AlphaGenome, ESM, Evo, and thought about ways to improve, evaluate or apply them in novel ways.

You will collaborate with computational biologists to systems architecture researchers to translate advances in large-scale machine learning into capabilities for modeling biological systems, ranging from genome interpretation and regulatory modeling to multimodal cellular prediction and biological design.

What You'll Do

• Adapt frontier AI models to biological tasks through fine-tuning, post-training, adapters, and architectural modifications.
• Design and run experiments applying large models to problems in genomics, regulatory biology, protein biology, or cellular systems.
• Develop evaluation pipelines and benchmarks for biological tasks such as variant interpretation, gene regulation modeling, protein function prediction, and multimodal cellular modeling — and drive these capabilities toward grounded downstream biological impact.
• Design biologically meaningful data representations and modeling schemes across sequence, molecular, and multimodal data modalities.
• Analyze model behavior and run ablations to understand model reasoning and failure modes in biological contexts.
• Explore modern mechanistic interpretability pipelines and methods for biological discovery.
• Collaborate with model architecture teams to integrate biological capabilities into next-generation foundation models.
• Prototype new approaches for biological prediction and design using foundation models.

What We're Looking For

• Strong background and intuition in machine learning and deep learning across large-scale generative architectures, from autoregressive LLMs to diffusion models. 
• Experience adapting large models to new domains through fine-tuning, post-training, adapters, or architecture modifications.
• Experience applying ML models to biological data and challenging prediction tasks.
• Familiarity with molecular biology and biological data modalities, particularly genomics, gene regulation, protein biology, or cellular systems.
• Ability to design evaluation tasks and benchmarks that measure biological model capability beyond simple accuracy metrics, with a critical eye toward aligning computational outputs with actionable downstream applications.
• Strong Python and ML tooling experience (PyTorch or JAX, experiment management, distributed training).
• Ability to interpret biological datasets and translate biological questions into machine learning experiments.
• Mid-to-senior level experience building and deploying ML systems in research or production environments.

Nice to Have

• Experience with genomic or molecular sequence models (e.g., Evo, HyenaDNA, AlphaFold-style models, AlphaGenome-style tasks, virtual cell models).
• Background in ML for structural biology, or (bio)chemistry.
• Familiarity with multimodal biological modeling, including transcriptomics, epigenomics, chromatin accessibility, or spatial biology.
• Experience building robust evaluation suites for large AI systems.
• Experience scaling ML experiments across large GPU clusters.
• Research publications in ML for biology, chemistry, or related areas.

Why Radical Numerics

• We believe biology is the most impactful and consequential application of AI.  
• Join peers that are mission driven, and dedicated to creating radically innovative tech that will change the world and human health for the better.
• Work on frontier AI systems in a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and national research institutes.

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. 

Member of Technical Staff, Infrastructure & Training Systems

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 biological world models will require not only strong research ideas, but exceptional training and inference systems: infrastructure that makes large-scale experimentation efficient, reproducible, and robust enough to support rapid scientific iteration. This role is focused on building that foundation.

About the Role

As a Member of Technical Staff, Infrastructure & Training Systems at Radical Numerics, you will design and build the systems that make large-scale model training possible across research and deployment workflows. You will work on distributed training, performance optimization, reusable internal frameworks, and the tooling that helps researchers move quickly without sacrificing reliability.

This role is ideal for someone who combines deep systems instincts with an interest in modern machine learning. You should care about how every layer of the stack affects research velocity: kernel performance, communication overhead, fault tolerance, observability, reproducibility, and the ergonomics of the training loop itself. 

What You’ll Do

• Design and scale distributed training systems. Build and optimize distributed training infrastructure for large-scale biological world models across large distributed compute systems, with a focus on performance, stability, and scalability. 
• Maximize throughput and hardware efficiency. Develop performance optimizations across the stack, including communication patterns, memory efficiency, custom kernels, compilation paths, and systems instrumentation, to ensure training compute is used effectively. 
• Build reusable training frameworks. Develop internal libraries, abstractions, and workflows that improve reproducibility, reliability, and scalability across new model architectures and training recipes. 
• Improve reliability under rapid iteration. Establish standards and mechanisms for robustness, maintainability, debugging, and safe deployment of fast-moving research infrastructure. That includes fault tolerance, checkpointing, monitoring, experiment hygiene, and incident analysis. 
• Collaborate across research and engineering. Partner closely with model researchers, training scientists, and data/infrastructure engineers to identify bottlenecks, unblock experiments, and design systems that support new scientific directions rather than constrain them. 
• Support new architectures and training paradigms. Adapt infrastructure to the needs of multimodal models, long-context training, and evolving model architectures, so the systems stack remains a research multiplier as model requirements change.

What We’re Looking For

• Strong engineering track record in distributed systems, high-performance ML infrastructure, training systems, or closely related areas.
• Proficiency in building performant, maintainable software in Python, PyTorch, Triton, CUDA, and C++. 
• Strong understanding of modern deep learning frameworks and their systems internals. 
• Ability to debug complex, multi-layered systems involving distributed training, memory/performance regressions, and reliability issues in large codebases. 
• Comfort working in a highly collaborative environment with researchers, engineers, and domain experts, with a bias toward initiative and execution. 
• Excellent written and verbal communication skills bridging technical and scientific domains. 

Nice to Have

• Experience with large-scale distributed training for frontier or foundation models.
• Contributions to open-source ML systems or infrastructure such as PyTorch, Torchtitan or Megatron-LM. 
• Familiarity with ML runtimes, compilers, numerics, communication libraries, and custom kernel development.
• Experience improving researcher productivity through infrastructure design, developer tooling, or workflow improvements. 
• Background in applied math, systems, computational biology, or related quantitative sciences. 

Why Radical Numerics

• Help build the computational foundation for multimodal biological world models aimed at rapid detection, response, and countermeasures across global health. 
• Work on systems problems at the frontier of distributed training, architecture, and numerics, in service of 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. 

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

Open to overseas candidates/Visa Support

This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

Our engineering team has a solid technical background, which would be a great environment for a skilled engineer.

In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing, and personalized content recommendation, while ensuring system robustness and scalability in a dynamic market environment.

What You’ll Work On

• Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes.
• Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization.
• Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions.
• Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning).

What We’re Looking For

• Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred.
• 5+ years of industry experience in ad tech, with a focus on performance optimization.
• Proven experience in applied machine learning, especially in CTR prediction, recommendation systems.
• Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills—able to lead end-to-end projects across product, data, and engineering.
• Bonus: Experience working on high-throughput, low-latency real-time systems (e.g., RTB engines, stream inference).

Language

Fluent in English or Chinese.

#LI-AK1

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing, and personalized content recommendation, while ensuring system robustness and scalability in a dynamic market environment.

What You’ll Work On

• Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes.
• Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization.
• Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions.
• Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning).

What We’re Looking For

• Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred.
• 4+ years of industry experience in ad tech, with a focus on performance optimization.
• Proven experience in applied machine learning, especially in CTR prediction, recommendation systems.
• Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills—able to lead end-to-end projects across product, data, and engineering.
• Bonus: Experience working on high-throughput, low-latency real-time systems (e.g., RTB engines, stream inference).

#LI-AK1

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.
In this role, you’ll build identify graph to enrich unified customer profile and also identify high purchase value users to drive ROAS by machine learning model.

What You’ll Work On

• Leverage scientific methods to build identify graph and identify high-value users
• Drive online experiments to continuously improve ROAS
• Own the project independently.
• Partner with product stakeholder, backend and frontend to provide measurement solution for our customers.

What We’re Looking For

• Master’s or PhD’s degree in Computer Science, Machine Learning, Statistics, or related field.
• 3+ years of industry experience on identify graph, ID mapping and cross-device mapping in digital Ads related fields, and generate mapping and profile under large-scale data.
• Proficiency in Python and SQL experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills.

#LI-AK1

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

Open to overseas candidates/Visa Support

This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.
In this role, you'll measure ads incrementality on different type of traffic, creative, users, and also improve campaign efficiency on combination having good incrementality by machine learning model and AI automation.

What You’ll Work On

• Leverage scientific methods to improve ads measurement.
• Drive online experiments to continuously improve ads effectiveness.
• Own the project independently.
• Partner with product stakeholder, backend and frontend to provide measurement solution for our customers.

What We’re Looking For

• Master’s or PhD’s degree in Computer Science, Machine Learning, Statistics, Econometrics, or related field.
• 3+ years of industry experience on causal inference, incrementality, marketing mix modeling or other measurement solutions on digital Ads related fields, and understand the assumption and difference across methods.
• Proficiency in Python and SQL experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills.

Language

Fluent in English or Chinese.

#LI-AK1