Staff Web Engineer Jobs

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you! Requirements: 5+ years of experience writing high-quality, high-performance code. Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT). Familiarity with Nvidia GPU architecture and CUDA. Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc). Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Responsibilities and Tasks: Drive best-in-class product maturity which includes bits per wafers (yield), product grades (Repair Density and DPM) at the fastest speed and cycle of learning. In depth knowledge of processes across different loops or functions (FEOL, MOL, CELL, BEOL) to identify best optimum process integration solution to achieve the goal, knowledge on product and circuit design will be plus. Collaborate with YE, Device, RDA, Metrology team to resolve top yield issues, develop inline visibility to enable fast yield ramp and minimize excursion. Identify best known method and business process from DRAM/NAND sites and drive for alignment and standardize across network. Travel to Micron sites as necessary for face to face collaboration. About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all . With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations tha

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search systems, datasets, and models work together to create the best possible experience for our users. As our product and agent capabilities evolve, we need evaluation systems that are fast, reliable, production-faithful, and actionable. In this role, you will build and improve the technical foundations that support Answer Quality across Perplexity. This includes our shared evaluation infrastructure and the platform used to replay and analyze agent traces. You will work closely with data scientists, engineers, and product teams to identify quality problems, measure their impact, and turn evaluation findings into product improvements. Responsibilities Build shared evaluation infrastructure that helps teams run reliable evals, analyze results, and make product and model decisions Develop the platform for replaying and analyzing agent traces to reproduce production behavior and diagnose failures Build and operate scalable systems for processing, storing, and monitoring interaction, trace, and evaluation data Partner with data scientists, engineers, and product teams to turn answer-quality problems into evaluations, analyses, and product improvements Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality Qualifications 4+ years of software, data, or machine learning engineering experience shipping and operating production systems Strong proficiency in Python and SQL, with solid fundamentals in system design, data modeling, and distributed systems Experience building big-data systems, including distributed compute, large-scale storage, and high-volume pipelines Demonstrated ownership of ambiguous technical projects from initial design through production operation Ability to work effectively with

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource mana

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource manag

We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us. What you will work on Examples of real work the team does: New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving. Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. Who we're looking for Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. You understand modern LLM architectures and are able to bring them up reliably in a production environment. You've built and operated production distributed systems under real load - ideally performance-critical ones. Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. Good if you touched any of ML compilers and framework internals: PyTorch internals, torch.compile, cus

We are looking for an AI Inference Engineer to join our growing team. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL. Responsibilities: New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving. Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents. Who we're looking for: Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus. You understand modern LLM architectures and are able to bring them up reliably in a production environment. You've built and operated production distributed systems under real load - ideally performance-critical ones. Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels. You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday. Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you. Nice-to-have: ML compilers and framework internals: PyTorch internals, torch.compile, custom operators. Distribut

About Ema Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. Role Overview As Chief of Staff to the CEO, you will be the strategic nerve center of the entire company — a true force-multiplier to Surojit Chatterjee (ex CPO Coinbase, VP Products at Google). Reporting directly to the CEO and based in Mountain View, CA (full co-location required), you will own the translation of the CEO's vision across Sales, Product, and the full organizational agenda — serving as connective tissue between the CEO and every critical function: EPD, GTM, Finance, HR, Legal, and the Board. This is a senior operating role for someone who has already done it — led strategy at scale, driven GTM at a high-growth technology company, and earned their stripes as an Engagement Manager or Associate Principal at a top-tier consulting firm (McKinsey, BCG, Bain) before stepping into industry. You are not here to learn the ropes; you are here to pull them. Ema is backed by top-tier investors (Accel, Section 32, and Prosus) and is building the Universal AI Employee for enterprises worldwide. This is a high-visibility, high-impact role at one of the most consequential AI companies in the world. Key Responsibilities CEO Leverage & Executive Operations Serve as Surojit's strat

About the Role: The Machine Learning team at Tubi drives the innovation behind personalized user experiences. With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding and ads optimization that shape the future of streaming. We are seeking a highly skilled Staff Machine Learning Engineer to contribute to transformative projects in video personalization. In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy. As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions. This is a hybrid role in our Toronto office. What You'll Do: Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences. Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement Your Background: 8+ years of industry experience building production Machine Learning systems MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks Proficiency in building and deploying full-stack machine le

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Position Summary The Distinguished Member of Technical Staff (DMTS) in Process Development for Advanced Thin Film serves as a recognized expert both within and outside of Micron. This position is pivotal in shaping the technical vision for NAND structural and electrical film processes, with influence that spans top-level organizational boundaries. The DMTS leads programs that impact the entire engineering group, drives breakthrough innovations, and plays a key role in influencing industry-wide and cross-disciplinary teams. Additionally, the DMTS<sp

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Position Overview Acts as a technical authority in 3D NAND Advanced thin film development, setting strategic direction and leading multi-functional initiatives. Drives department-wide impact by architecting solutions, accelerating innovation adoption, and influencing technical standards. Engages in global projects and mentors engineers to elevate technical excellence. Key Responsibilities Define technical strategy and lead multi-functional projects in structural and electric film development. <span

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Position Overview The Member of Technical Staff (MTS) in Process Development for Advanced Thin Film plays a critical role in advancing thin film process development for NAND memory technology. This position is responsible for performing experiments, conducting material characterization, and providing support for process integration and fixing. As a key contributor, the MTS collaborates with senior engineers and Multi-functional teams to promote manufacturability, continuous improvement, and ongoing technology advancement within the organization. Key Responsibilities</span

About Hark Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory. We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world. To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems. About the Role The Omni team at Hark is building the next generation of AI experiences beyond text, enabling models to understand and generate content across multiple modalities, including text, audio. Our goal is to create seamless, real-time multimodal intelligence that powers intuitive and immersive user experiences. As part of the Omni team, you will drive the development of advanced speech and audio capabilities within multimodal foundation models. You will work across the full stack—from data and modeling to training, evaluation, and real-time serving—pushing the boundaries of speech intelligence and human-computer interaction. Responsibilities Drive research and development to advance speech and audio capabilities in multimodal models, including speech recognition, synthesis, and understanding. Develop and improve large-scale speech and audio data pipelines, including data collection, filtering, alignment, and synthetic data generation. Design and implement state-of-the-art models for speech and audio, including end-to-end multimodal architectures and real-time systems. Build evaluation frameworks and internal benchmarks to measure speech quality, latency, robustness, and overall user experience. Optimize models and systems for real-time pe

About Hark Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory. We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world. To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems. About the Role The Omni team at Hark is building the next generation of AI experiences beyond text, enabling models to understand and generate content across multiple modalities, including text, audio, and vision. Our goal is to create seamless, real-time multimodal intelligence that powers intuitive and immersive user experiences. As part of the Omni team, you will focus on developing large-scale pretraining systems and foundation models. This includes working across the full stack—from data curation and large-scale training infrastructure to model architecture and optimization. You will play a key role in advancing the core capabilities of our models through pretraining at scale. Responsibilities Drive research and development in large-scale LLM and multimodal pretraining, focusing on improving model capability through better data, scaling, and architecture. Develop and optimize data pipelines for pretraining, including large-scale data curation, filtering, deduplication, and synthetic data generation. Design and implement efficient training strategies for foundation models, including distributed training, scaling laws, and optimization techniques. Build and improve pretraining infrastructure, including training systems, data pipelines, and comp

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Join Micron's world-class team as a Global Facilities North Asia Regional Chemical Engineer! This outstanding opportunity allows you to lead business process alignment in design, construction project composition, management, schedule, cost control, testing & commissioning, and operational and maintenance programs for chemical & slurry systems across our manufacturing sites. Your role will be pivotal in ensuring our facilities run flawlessly, maintaining our competitive edge in the industry. Key Responsibilities Project Design and Construct and develop regional greenfield projects aligned with global standards. Collaborate with central sustainability, cost, construction, and line planning teams. Offer engineering assistance for site projects and sustainability efforts. Review and approve site capacity expansion and capital expenditures. Make decisions based on business needs for Facilities Reliability Improvement project capital expenditures. Reliability Support site system improvement projects, including system reliability improvements, quality enhancement, and equipment end-of-life replacements. Coordinate regional efforts and identify strategic program alignment opportunities (e.g., BCP, Critical Spec, Quality Defense Line). Offer engineering assistance for significant SIPP evaluations. Share lessons learned from regional events and influence the development of glob