MLSys 2026 Career Opportunities

Overview:

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.

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.

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 high quality 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.

The D. E. Shaw group seeks a machine learning researcher to creatively apply their knowledge of ML and software engineering to design and build computational architectures for high-performance, large-scale knowledge discovery in financial data. In this dynamic role, the engineer will leverage cutting-edge ML research to turn new ideas into proof-of-concept implementations, solve tough low-level engineering problems, and set up infrastructure for broader, longer-term impact. This position will play a key role in improving the efficiency, scalability, and reliability of the firm’s ML efforts, and will directly impact the firm’s systematic research through ML engineering contributions, all within a collaborative and engaging environment.

What you'll do day-to-day

• Rapidly prototype, implement, and evaluate state-of-the-art machine learning techniques.
• Drive the computational agenda for ongoing and future ML projects.
• Tackle complex engineering problems across software and hardware layers, setting technical direction and anticipating architectural needs.
• Deploy ML models into real-world systems where they have direct, measurable impact on decision-making and trading.
• Create compelling proof-of-concept systems, demonstrate them internally, and collaborate with others for development.
• Partner with researchers to design and implement efficient training workflows, enabling rapid experimentation with deep learning models.

Who we're looking for

• Bachelor’s degree or higher is required.
• Proven track record of collaborating with researchers to translate ML ideas into high-performance solutions.
• Experience driving computational and architectural innovation by rapidly prototyping and demonstrating novel ML ideas within a high-performance environment.
• Interest in staying current with ML research and swift application of new techniques.
• Expertise in performance optimization, low-level engineering, GPU programming and libraries (e.g., Pytorch, JAX, CUDA, XLA, Triton, or PTX).
• Demonstrated ability to quickly solve complex computational problems, create inspiring technical demos, and transition work to broader teams.
• Proactive approach in driving agendas and anticipating engineering bottlenecks in large systems.
• Proficiency in modern ML frameworks, facility with deep learning tooling, and a solid understanding of hardware and architectural challenges.
• The expected annual base salary for this position is $275,000 to $350,000. 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.

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, you will be a foundational member of our small, multi-disciplinary R&D team. We are looking for 'first principles' thinkers who are excited to tackle the hardest, most ambiguous technical challenges at the intersection of AI, physics, and computer architecture. You will be responsible for driving invention, prototyping, and validation of the core components of our novel computing platform.

Your work will be fluid and could span from theoretical modeling and simulation to algorithm development, hardware/software co-design, or experimental validation in collaboration with other team members. We're hiring exceptional problem-solvers who can navigate deep uncertainty and help chart our technical roadmap.

What We're Looking For   - Exceptional technical ability in a quantitative field (e.g., Physics, Computer Science, Electrical Engineering, Applied Math, or a related discipline). - An MS/PhD or equivalent research/project experience is strongly preferred. - A "0-to-1" mindset. You have a demonstrated history of tackling complex, ambiguous R&D problems, often from a blank slate. - Deep curiosity. You are comfortable diving into new domains, whether it's semiconductor physics, machine learning theory, or systems-level design. - A creative and unconventional approach to problem-solving.

Core Technical Competencies

• Analytic Foundations: Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. Strong candidates are able to leverage the properties of (or impose constraints on) such systems to develop analytic insights.
• Practical Eye: The ability to leverage analytic insights to build practical tools – metrics, algorithmic optimizations, and automated analyses – that can be used to study dynamical systems. Strong candidates are comfortable navigating the tradeoff space between theoretic purity and practicality to realize useful tooling.
• Programming Proficiency: Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. Experience with libraries geared toward analysis, such as computer algebra libraries (e.g., sympy) also recommended.
• ML/AI Familiarity: Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. Experience with using high-level ML model frameworks, such as PyTorch and JAX.

Bonus Points (Nice to Have)

• Compute model technical staff may focus primarily on the above skillsets, or may be cross-disciplinary with hardware or AI/ML algorithms expertise.
• Collaboration & Communication
• Cross-Functional Leadership: Excellent ability to translate complex technical concepts for diverse teams. You will act as a translator, discussing algorithmic/model trade-offs with ML/AI teams and eliciting hardware constraints and features from hardware engineering teams.

Why Join Us?

• The Mission: Tackle a fundamental problem that could redefine computing for the next 50 years.
• The Impact: Be a foundational member of a world-class team with an outsized opportunity for ownership and impact.

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 to architect and scale the core infrastructure behind distributed training pipelines and petabyte-scale data catalogs. You'll work directly with researchers to accelerate experiments, develop new datasets, improve infrastructure efficiency, and enable key insights across our data assets.

Key Responsibilities - Design, build, and operate scalable, fault-tolerant infrastructure for LLM research: distributed compute, data orchestration, and storage across modalities. - Develop high-throughput systems for data ingestion, processing, and transformation — including training data catalogs, deduplication, quality checks, and search. - Build systems for web crawling, data ingestion, and real-time data processing to support model training operations. - Develop tools and frameworks for efficient data storage, retrieval, and versioning across distributed systems. - 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

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're looking for engineers and scientists to design, optimize, and maintain the core systems that enable scalable, efficient training of LLM. Your goal is to make experimentation and training at Inception fast and reliable so our team can focus on science, not system bottlenecks.

Key Responsibilities - Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes. - Develop high-performance optimizations to maximize throughput and efficiency. - Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.

Qualifications - BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience). - Understanding of ML frameworks (PyTorch, TensorFlow) from a systems perspective. - Strong engineering skills — ability to contribute performant, maintainable code and debug in complex codebases. - Proficiency in Python and at least one systems programming language (C++/Rust/Go). - Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.

Preferred Skills - Experience building and maintaining large-scale language models with tens of billions of parameters or more. - Experience with ML workflow orchestration tools (Kubeflow, Airflow). - Background in performance optimization and profiling of ML systems (Prometheus, Grafana, OpenTelemetry). - Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM.

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

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

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're looking for engineers and scientists to design, optimize, and maintain the compute foundations that power large-scale language model training and inference. You will develop high-performance ML kernels, enable efficient low-precision arithmetic, and improve the distributed compute stack that makes training and serving large models possible.

Key Responsibilities - Design and implement custom ML kernels (CUDA, CuTe, Triton) for core dLLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU architectures. - Design compute primitives to reduce memory bandwidth bottlenecks and improve kernel efficiency. - Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.

Qualifications - BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience). - Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks. - Understanding of ML frameworks (PyTorch, TensorFlow) from a systems perspective. - Background in performance optimization and profiling of ML systems. - Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (XLA, TVM). - Familiarity with distributed training techniques (data parallel, model parallel, pipeline parallel). - Proficiency in Python and at least one systems programming language (C++/Rust/Go). - Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.

Preferred Skills - Experience building and maintaining large-scale language models with tens of billions of parameters or more. - Experience with distributed systems and cloud computing platforms (AWS/GCP/Azure). - Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM. - Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA.

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

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

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.