MLSys 2026 Career Opportunities

Team Description:

The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business.

This is an individual contributor (IC) role driving strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One. As a well-respected IC leader, you will guide and mentor a team of applied scientists and their managers without being a direct people leader. You will be expected to be an external leader representing Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.

In this role, you will:

Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.

Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.

Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.

Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.

Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

The Ideal Candidate:

You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.

Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.

A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.

Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.

Has a deep understanding of the foundations of AI methodologies.

Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.

An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.

Experience in delivering libraries, platform level code or solution level code to existing products.

A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.

Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Key Responsibilities:

Partner with a cross-functional team of scientists, machine learning engineers, software engineers, and product managers to deliver AI-powered platforms and solutions that change how customers interact with their money.

Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.

About Unconventional

Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation - a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale.

At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient. We're doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.

The Role

As a Member of Technical Staff, Language & Reasoning Models, you will drive the development of foundational language and reasoning models that fundamentally leverage the dynamics of our novel silicon. Your goal is to map the behaviors of modern language models directly onto the physics of our hardware.

You will sit at the intersection of NLP/reasoning research and hardware codesign, proving that high-fidelity, large-scale language understanding and generation can be achieved natively on an unconventional computing substrate.

What You'll Do

• Model Development: Design, train, and scale next-generation language and reasoning architectures (such as transformers, state space models, diffusion/flow models, and deep equilibrium models) specifically tailored for unconventional compute.
• Physics-Informed Architecture: Rethink standard sequence modeling to exploit the continuous-time dynamics of silicon, moving away from layers of inefficient digital abstraction. 
• Evaluation & Scaling: Establish the training recipes, loss functions, and evaluation metrics needed to reach the frontier of language comprehension, logical reasoning, and generation speed while maintaining the massive energy efficiency of our platform.
• Extreme Codesign: Collaborate with hardware designers and theorists, and system builders to co-design the model architecture alongside the underlying physical compute primitives.  

Minimum Qualifications

• Education: An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.
• Experience:  Deep, hands-on expertise in the theory, architecture, and training of modern foundation models (transformers, SSMs, text diffusion/flow, etc.).
• Systems Fluency: Hands-on, battle-tested experience dealing with model scaling. You have successfully designed and executed full-scale, distributed training runs for large language or reasoning models, managing the complexities of massive compute clusters.
• Software Development: You are fluent in modern deep learning frameworks (PyTorch or JAX) and have a proven track record of writing clean, scalable training code for large language models.

Preferred Qualifications (Nice to Have)

• Unconventional Experience: As a bonus, you may have experience working with hardware-in-the-loop training, mixed-signal hardware, quantization, or physics-informed neural networks

Why Join Us?

• The Mission: Redefine computing for the next 50 years by solving the fundamental energy limitation of AI at a global scale.
• The Impact: Shape the company's future as a foundational team member. Enjoy massive ownership and an outsized opportunity to drive change.
• The Perks: A comprehensive package including best-in-class health benefits, 401k matching, truly unlimited PTO, and complimentary meals in our Palo Alto office.

About Unconventional

Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation - a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale.

At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient. We're doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.

The Role

As a Member of Technical Staff, AI Systems, you will develop state-of-the-art architectural components, write their bespoke implementations for our unconventional software framework, and map them efficiently down to the physical silicon. You are critical to preparing our software stack for upcoming tapeouts by acting as the bridge between model architecture and physical compute.

What You'll Do

• AI Architectural Modeling: Co-design and evaluate next-generation AI models (e.g, transformers, diffusion, flow, and energy-based models).

• You will collaborate closely across the team to combine, modify, and implement core modeling components, including both conventional (e.g., attention, normalization, Mixture-of-Experts, FFNs) and unconventional components.

• You will ensure that they function optimally across our novel compute substrates.

• Performance Modeling & Scaling: Establish and test scaling laws specific to our novel hardware. Develop rigorous performance models to evaluate compute vs. memory trade-offs

• Advanced Mapping & Partitioning: Drive the partitioning and mapping of complex AI models down to hardware. Apply and invent advanced optimization strategies from first principles, including custom quantization schemes, sparsity/pruning, and distillation to fit the physical constraints of our substrates.

• GPU Optimization & Kernel Development: Develop and optimize GPU kernels using low-level programming models like CUDA, Triton, or CUTLASS. Profile and debug complex ML codebases to resolve performance bottlenecks (training and inference).

• Cross-Functional Collaboration: Act as a translator, discussing algorithmic trade-offs with theorists and converting model requirements into concrete specifications for infrastructure and hardware engineering teams.

Minimum Qualifications

• Education: An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.

• Experience: Deep, practical understanding of the modern AI/ML stack and optimized compilation and execution of algorithms on modern GPU systems.

• Proven experience in profiling, identifying, and resolving performance bottlenecks in complex ML codebases.

• Systems Fluency: Demonstrated ability to map state-of-the-art AI model architectures (e.g., Transformers, Mixture of Experts, diffusion models) to system performance implications and apply advanced efficiency techniques such as sparsity, quantization, and distillation.

• Software Development: Deep experience with PyTorch, including its internals, torch.compile, and distributed data parallel (DDP) / fully sharded data parallel (FSDP) libraries.

Preferred Qualifications (Nice to Have)

• Unconventional Co-Design: A forward-looking perspective on co-designing algorithms for unconventional computing paradigms that map closely to the physics of underlying systems.

• Next-Gen Efficiency: Theoretical or research experience in advanced approximation/compression techniques beyond standard quantization.

Team Description:

The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact.

In this role, you will:

Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.

Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.

Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.

Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.

Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

The Ideal Candidate:

You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good.

Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production.

You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven.

You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss.

You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown.

Team Description:

The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business.

This is an individual contributor (IC) role driving strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One. As a well-respected IC leader, you will guide and mentor a team of applied scientists and their managers without being a direct people leader. You will be expected to be an external leader representing Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.

In this role, you will:

Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money.

Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.

Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.

Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.

Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

The Ideal Candidate:

You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.

Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.

A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.

Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.

Has a deep understanding of the foundations of AI methodologies.

Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.

An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.

Experience in delivering libraries, platform level code or solution level code to existing products.

A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.

Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Location Santa Clara, California USA or Toronto, Canada

Description At Lemurian Labs, we’re on a mission to bring the power of AI to everyone—without leaving a massive environmental footprint. We care deeply about the impact AI has on our society and planet, and we’re building a rock-solid foundation for its future, ensuring AI grows sustainably and responsibly. Because let’s face it, what good is innovation if it doesn’t help the world?

We are building a high-performance, portable compiler that lets developers “build once, deploy anywhere.” Yes, anywhere. We’re talking about seamless cross-platform compatibility, so you can train your models in the cloud, deploy them to the edge, and everything in between—all while optimizing for resource efficiency and scalability.

If the idea of sustainably scaling AI motivates you and you’re excited about making AI development both powerful and accessible, then we’d love to have you. Join us at Lemurian Labs, where you can have fun building the future—without leaving a mess behind.

The Role We're looking for a Senior ML Performance Engineer to architect and lead our Performance Testing Platform from the ground up. You'll be the technical authority on how we measure, validate, and optimize the performance of large language models (Llama 3.2 70B, DeepSeek, and others) before and after compiler optimization on modern GPU architectures.

This is a high-impact role where you'll directly influence our product quality and our customers' success. You'll work at the intersection of ML systems, GPU architecture, and performance engineering—building the infrastructure that proves our compiler delivers real value.

Here is what you will do: Design and build a comprehensive performance testing platform for evaluating LLM inference workloads across GPU clusters Define and implement the benchmarking methodology, metrics, and test suites that measure latency, throughput, memory utilization, power consumption, and model accuracy Establish baseline performance for unoptimized models (Llama 3.2 70B, DeepSeek, etc.) and validate post-optimization improvements Develop automated testing pipelines for continuous performance validation across compiler releases and model updates Investigate performance bottlenecks using profiling tools (ROCm profilers, GPU traces, system-level monitoring) and work with the compiler team to drive optimizations Create dashboards and reporting that provide clear visibility into performance trends, regressions, and wins Collaborate cross-functionally with compiler engineers, ML engineers, and DevOps to ensure performance testing is integrated into our development workflow Document best practices for performance testing and optimization of ML workloads on GPU hardware

Essential Skills and Experience: BS degree in computer science, computer engineering, electrical engineering, or equivalent practical experience 7+ years of experience in performance engineering, benchmarking, or systems engineering roles Deep understanding of ML inference workloads, particularly transformer-based models and LLMs Hands-on experience with GPU programming and optimization (CUDA, ROCm, or similar) Strong programming skills in Python and C/C++ Proven track record of building performance testing infrastructure or benchmarking platforms from scratch Experience with ML frameworks (PyTorch, TensorFlow, ONNX Runtime, vLLM, TensorRT-LLM, etc.) Proficiency with profiling and debugging tools for GPU workloads Strong analytical skills with the ability to design experiments, analyze results, and communicate findings clearly Experience with CI/CD systems and test automation frameworks

Inception creates the world’s fastest, most efficient AI models. Our Mercury model is the world’s fastest reasoning LLM and first commercially available diffusion LLM, delivering 5x greater speed and efficiency than today’s LLMs, with best-in-class quality.

We are the AI researchers and engineers behind such breakthrough AI technologies as diffusion models, flash attention, and DPO.

The Role We seek experienced engineers and scientists to shape how we collect, process, and curate the datasets that power our models. You'll combine engineering expertise with research insight to build scalable data pipelines, develop synthetic data generation techniques, and ensure our models are trained on high-quality, diverse data.

Key Responsibilities - Develop data mixes for training LLMs, including by leveraging open-source datasets, synthetically generated data, and curated human feedback. - Design and implement data pipelines for processing petabyte-scale datasets. - Build systems for web crawling, data ingestion, and real-time data processing to support model training. - Develop tools and frameworks for efficient data storage, retrieval, and versioning across distributed systems. - Create evaluation frameworks to measure data diversity, quality, and representativeness. - Ensure data collection adheres to privacy regulations.

Qualifications - BS/MS/PhD in Computer Science, Machine Learning, or a related field (or equivalent experience). - 3+ years of experience building data processing pipelines at scale, particularly with AI/ML applications. - Strong proficiency in Python and experience with data processing frameworks (Apache Spark, Beam, Airflow). - Familiarity with synthetic data generation techniques and data augmentation strategies. - Familiarity with web scraping, crawling technologies, and Common Crawl datasets. - Solid understanding of machine learning fundamentals and experience with ML frameworks (PyTorch, TensorFlow). - Experience with SQL and NoSQL databases for managing structured and unstructured data.

Preferred Skills - Experience with large language models and understanding of tokenization, embeddings, and model architectures. - Experience managing human annotation workflows and quality control processes. - Experience with vector databases and embedding-based retrieval systems. - Knowledge of data privacy regulations and ethical AI practices. - Experience with distributed computing and large-scale data storage systems (HDFS, S3, BigQuery).

Why Join Inception - Work with World-Class Talent: Collaborate with the inventors of diffusion models and leading AI researchers - Shape Foundational Technology: Your decisions will influence how the next generation of AI products are built and used - Immediate Impact: Join at the ground floor where your contributions directly shape product direction and company trajectory

Perks & Benefits - Competitive salary and equity in a rapidly growing startup - Flexible vacation and paid time off (PTO) - Health, dental, and vision insurance - Catered meals (breakfast, lunch, & dinner) - Commuter subsidies - A collaborative and inclusive culture

About the job

Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.

We build the industry's best data agents to help customers make more, better, and faster data-driven decisions—achieved by enriching the customer knowledge layer, automating data preparation, providing tailored agent harnesses, and leveraging the advanced capabilities of BigQuery and its ecosystem.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving channel behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

Lead the technical strategy and architectural design of the core reasoning engine that translates natural language into reliable SQL insights, ensuring the platform scales to support complex enterprise data exploration. Drive cross-functional collaboration with AI/ML, UX, and Product teams to define the "agentic" future of BigQuery, bridging the gap between raw data and business-ready answers. Establish and maintain engineering excellence by setting the bar for performance, reliability, and observability of production-critical agent services across the BigQuery ecosystem. Mentor and influence a broad group of engineers, identifying and refining ambiguous, high-impact problems into tractable projects that advance our data-centric AI capabilities.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

In this role, you will be advancing fundamental capabilities of AI to drive significant benefits to humanity. You will pioneer AI research in Singapore, focused on delivering the most performant, efficient and capable generative AI models.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.

Responsibilities

Abstract out key problems, design elegant and deep solutions for these problems through theoretical or empirical insights. Prototype, profile and benchmark solutions to showcase effectiveness. Lead and collaborate with research teams located across the globe. Drive and grow collaborations with product teams to land product innovations. Collaborate with hardware architects/infrastructure teams to inform design and algorithm decisions.