MLSys 2026 Career Opportunities

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. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together. The US base salary range for this full-time position is $174,000-$252,000 + 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

Write and test product or system development code. Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency). Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality. Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.

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.

Location Santa Clara, California US 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.

Here is what you will do: - Design, develop, maintain and improve our heterogeneous AI compiler. - Design and implement new capabilities in our compiler based on our novel compiler architecture. - Propose improvements to and expansions of our novel compiler architecture with respect to new advancements in machine learning model architectures and hardware. - Use the latest techniques in parallelization and partitioning to automate generation and exploit highly optimized kernels. - Generate and use performance data to identify opportunities and drive improvements. - Work with our product team to understand the evolving needs of ML engineers and drive improvements in architecture.

Essential Skills and Experience: - BS degree in computer science, computer engineering, electrical engineering, or equivalent practical experience - 4+ years of experience working with compilers. - Very strong knowledge of compiler algorithms and data structures. - Experience and interest in low level code generation, object file manipulation and target specific optimizations - 4+ years of experience with C/C++ - Strong written and oral communication, and able to write clear and concise documentation - Team first attitude - Detail oriented

Preferred Skills and Experience: - Masters or PhD degree in computer science, computer engineering, electrical engineering, or equivalent practical experience. - Knowledge of traditional compiler techniques; instruction selection, register allocation and traditional analysis like dominance, def-use et al. - Knowledge of calling conventions and APIs, linking and relocations. - Working knowledge of LLVM. - Experience with loop optimizations (vectorization, unrolling, fusion, parallelization, etc). - Experience with machine learning workloads and their demands on hardware.

Salary depends on experience and geographical location.

This salary range may be inclusive of several career levels and will be narrowed during the interview process based on a number of factors, such as the candidate’s experience, knowledge, skills, and abilities, as well as internal equity among our team.

Additional benefits for this role may include: equity, company bonus opportunities, medical, dental, and vision benefits; retirement savings plan; and supplemental wellness benefits.

Lemurian Labs ensures equal employment opportunity without discrimination or harassment based on race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital or domestic/civil partnership status, genetic information, citizenship status, veteran status, or any other characteristic protected by law.

EOE

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 a people manager role that will lead teams to drive strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One. As a well-respected people leader, you will guide and mentor a team of applied scientists. 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.

Engage in high impact applied research to take the latest AI developments

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.

The D. E. Shaw group seeks exceptional software engineers with expertise in applied AI, AI agents, and agentic systems to join the firm. This role offers the chance to work directly with a variety of groups at the firm on innovative, greenfield projects that transform how teams operate—leveraging quantitative and programming skills to design, build, and deploy AI solutions that drive efficiency, enhance analytical capabilities, and accelerate decision-making across the firm.

What you'll do day-to-day

You’ll join a dynamic team, with the potential to: - Collaborate directly with internal groups and end users across various functions to build bespoke AI agents and applications tailored to nuanced, real-world business needs. - Lead and contribute to greenfield AI projects, taking ownership from concept through production and helping shape internal AI strategy and adoption. - Experiment with emerging AI tools and model capabilities, rapidly prototyping and integrating them across platforms to enhance usability, scalability, and effectiveness. - Scale the adoption of AI tools firmwide by developing best practices, frameworks, and reusable components that drive innovation and productivity. - Build foundational AI components, such as agent frameworks, reusable “skills,” and large-scale retrieval systems, to support AI tools and applications. - Design, develop, and maintain shared AI infrastructure and agentic applications, ensuring firmwide data integration and enhancing software development efficiency.

Who we're looking for

• A bachelor’s degree in any field is required, along with an extensive background in software development, and hands-on experience building and scaling AI solutions at the product, system, or company level.
• Solid understanding of AI technologies and an interest in developing advanced AI applications and frameworks.
• Demonstrated ability to thrive in technical or entrepreneurial environments, along with the capability to solve complex challenges and lead projects from inception to deployment.
• A record of strong academic or professional achievement, with analytical depth and creativity in AI-related projects.
• We welcome outstanding candidates at all experience levels who are excited to work in a collegial, collaborative, and fast-paced environment.
• The expected annual base salary for this position is $225,000 to $275,000. Our compensation and benefits package includes substantial variable compensation in the form of a year-end bonus, guaranteed in the first year of hire, a sign-on bonus, and benefits including medical and prescription drug coverage, 401(k) contribution matching, wellness reimbursement, family building benefits, and a charitable gift match program.

Location: San Francisco · On-site

ABOUT THE COMPANY

We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-site

ABOUT THE ROLE

You'll be researching making models efficient: quantization, speculative decoding, sparse and structured attention, distillation, mixture-of-experts inference, and the training-time techniques that make those methods possible. The work spans algorithm design, careful evaluation, and pushing methods to where they actually run.

This is a senior research role with a clear engineering edge. You'll spend time at the intersection of model architecture and inference performance, designing methods that move accuracy/latency/cost trade-offs in our favor (then partnering with engineers to make those wins real in production).

WHAT YOU'LL DO

• Research and develop quantization methods: post-training quantization, quantization-aware training, mixed-precision regimes, low-bit-width arithmetic
• Design and evaluate speculative decoding approaches: draft models, tree attention, parallel speculation, lookahead decoding
• Investigate training-time efficiency methods that compose well with inference: distillation, sparse attention, mixture-of-experts, low-rank adaptation, pruning
• Run controlled experiments at production scale; characterize what works on real workloads, not just toy benchmarks
• Co-design methods with the inference engineering team: push results to where they actually run, not stop at the paper
• Read deeply across the efficient ML / efficient inference literature; translate the most useful ideas into our stack
• Publish when the work warrants it; share findings internally
• Partner with model and training researchers so efficiency choices align with model architecture and post-training decisions

WHAT WE'RE LOOKING FOR

• Strong track record of ML research on efficiency methods: quantization, speculative decoding, distillation, MoE, sparse attention, or adjacent
• 5+ years of hands-on research experience
• Deep familiarity with both training and inference performance characteristics
• Fluent in PyTorch, Jax or equivalent; comfortable working at the kernel and serving-framework level when methods require it
• Track record of moving efficiency research from prototype to production
• Strong statistical expertise: you'd notice a flawed comparison before someone else points it out
• Strong written communication
• Published research at NeurIPS, ICML, ICLR, MLSys, or comparable venues

NICE TO HAVE

• PhD in ML, systems, or related field
• Open-source contributions to quantization, speculative-decoding, or efficient-inference libraries
• Experience with hardware-aware optimization and accelerator-specific tooling
• Background in numerical methods, low-precision arithmetic, or approximate computation

THIS ROLE IS PROBABLY NOT FOR YOU IF

• You want to focus on pretraining large models from scratch (that's a different role)
• You prefer abstract algorithmic research without hands-on implementation
• You want a fixed benchmark with stable targets (our targets shift with what our models actually need to do)

P.S. We’re also hosting a small private dinner during MLSys for people interested in agents, recursive self-improvement, and AI infrastructure. Apply to join us here: https://luma.com/u6yt1gri

About the Role

We are seeking a Member of Technical Staff, ML Kernels to design, optimize, and benchmark high-performance compute kernels for modern ML workloads. This role is for a deeply technical engineer who enjoys working close to hardware — writing CUDA kernels, investigating performance artifacts, building benchmarks, and serving as a go-to expert on accelerator behavior.

You will partner closely with research, systems, and infrastructure teams to unlock efficiency gains across GPUs today and other accelerators (e.g., TPU, Trainium) as we expand our hardware partnerships.

This role will be performed onsite in Santa Clara, CA or Boston, MA.

Essential Duties & Responsibilities

• Design, implement, and optimize high-performance ML kernels targeting GPUs (CUDA), with an emphasis on throughput, latency, and memory efficiency.
• Profile, benchmark, and analyze performance across hardware configurations, identifying bottlenecks.
• Debug low-level performance issues involving memory hierarchy, scheduling, synchronization, and numerical formats.
• Build and maintain benchmarking tools to compare performance across GPUs and other accelerators.
• Advise internal teams on GPU and accelerator performance characteristics, tradeoffs, and best practices.
• Explore and prototype support for alternative accelerator platforms (e.g., TPU, Trainium) as needs evolve.
• Collaborate with ML researchers and systems engineers to translate algorithmic needs into efficient kernel implementations.

Qualifications

• Strong experience writing and optimizing CUDA kernels or equivalent low-level accelerator code.
• Deep understanding of GPU architecture, including memory systems, parallel execution, and performance tradeoffs.
• Experience with profiling and benchmarking tools (e.g., Nsight Systems/Compute, nvprof).
• Proficiency in C++ and low-level performance-oriented programming.
• Ability to independently investigate ambiguous performance issues and drive them to resolution.

Preferred Qualifications

• Experience with ML framework internals (e.g., PyTorch, TensorFlow, XLA) and custom operator development.
• Prior work with non-GPU accelerators such as TPU, Trainium, or IPU.
• Familiarity with mixed-precision compute (e.g., FP16, BF16, FP8).
• Contributions to open-source performance, systems, or ML infrastructure projects.

Compensation & Benefits

• Competitive base salary, performance-based bonus, and early stage equity grant
• Comprehensive health, dental, vision, and life insurance
• Relocation assistance and visa sponsorship
• Daily lunch stipend, 401k match, and more
• Sunny offices in Santa Clara, CA and Boston, MA

The Opportunity

• Impact: We are tackling a fundamental challenge at the infrastructure layer: unlocking greater AI capability while dramatically improving efficiency. The work we do here compounds across state-of-the-art AI models, systems, and real-world applications.
• Timing: Joining now means real ownership of the company and meaningful influence over product direction and execution. You'll work from first principles, move quickly from insight to execution, and see your contributions directly reflected in what we build.
• Culture: You'll work alongside a group of people who care deeply about rigor, clarity, and impact. We value thoughtful disagreement, fast learning, and intellectual fearlessness. This is a place where strong ideas shine, curiosity is encouraged, and growth is a daily practice.