Machine Learning Engineer Radar Jobs

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. As a Design Validation Product Engineer on High Bandwidth Memory (HBM) technology you will be tasked with developing next-generation HBM products. Product Engineers are a key interface between Design, Manufacturing, Test, and Quality internal departments as well as external customers. We take ownership of HBM products; starting at the Design phase, through initial bring-up, qualification, and into volume production. The seniority level offered will be based on a combination of experience and education. In HBM HIG or equivalent experience (High Bandwidth Memory, Heterogeneous Integration Group), we innovate and integrate end-to-end groundbreaking front-end and backend processes with groundbreaking design, debugging various tests, and qualification techniques to develop the lowest power per bit solutions to improve customer experience in the field of ML (Machine Learning) and AI. The success of a sophisticated product such as HBM relies vastly on vertical integration and the various engineering working in unison. To provide greater detail, our HBM technology pertains to stacking numbers of DRAM chips along with a logic chip within one package through an assembly technology called TSV (Through Silicon Via). This greatly increases the memory density in a package, while allowing very high-speed signal transmission. Furthermore, "high bandwidth"; is an outstanding memory design area where custom gate-level design and RTL style logic design are blended into the same product, and most of the DDR or LPDDR design is based on the gate-level design on

Job Details: Job Description: We are seeking a Senior Middleware Development Engineer to join our communication runtimes team to develop and contribute software features and optimizations to multiple communication libraries such as Intel SHMEM (Shared Memory Access), Intel MPI (Message Passing Interface), MPICH (Message Passing Interface Chameleon) and Intel oneCCL (Collective Communications Library). In this role, you will have an opportunity to build expertise with the latest Intel GPUs and CPUs used in the data center, work directly with scientists and engineers working on the Aurora supercomputer at the Argonne National Labs, and make meaningful contributions that improve scientific computing and machine learning. You will need to understand multiple levels of the communications stack, be self-driven, have high motivation to learn and want to use your outstanding analytical skills to make an impact and must have excellent communication skills. Your roles and responsibilities will include but not limited to: Design, develop, and maintain features and optimizations for Intel SHMEM, Intel MPI, MPICH, and oneCCL libraries Collaborate with cross-functional teams to define software requirements and specifications Optimize software to meet performance requirements, ensuring low latency, high bandwidth and high reliability Qualifications: Minimum qualifications listed below would be obtained through a combination of industry relevant job experience, internship experience and / or schoolwork/classes/research. The preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Minimum Qualifications: Master’s d

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. Location Singapore, Fab10 Department Fab10 High-Volume Manufacturing Photo Process and Equipment Engineering Project Title Automation of Non-Zero-Offset Control for High-Volume Manufacturing Photo Processes Project Description Non-Zero-Offset is a key photo-process control parameter used in overlay-performance management and yield protection. The current workflow includes data preparation, Non-Zero-Offset generation, engineering review, validation, and post-implementation monitoring. As the number of process vintages increases, a scalable analytical approach is needed to improve workflow consistency and efficiency. The intern will undertake a structured project to develop and evaluate an automated, data-driven Non-Zero-Offset framework using Python, statistical analysis, machine learning, and visualization. The project will provide practical exposure to photo-process engineering, inline metrology, data modeling, workflow automation, and AI-Enabled engineering analysis. Objective of the Project Develop an understanding of the end-to-end Non-Zero-Offset workflow, including wafer selection, inline recipe criteria, metrology requirements, generation logic, and validation.

About ElevenLabs ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $22B - multiples of 11, always. We have expanded from voice into three main platforms: ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. How we work High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. Excellence everywhere: Everything we do should match the quality of our AI models. Global team: We prioritize your talent, not your location. What we offer Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. Growth paths: Joining ElevenLabs means joining a

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. 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. As a Physical Verification Engineer, you will be a key technical contributor within the Heterogeneous Integration Group (HIG), responsible for defining, executing, and driving sign-off quality physical verification flows for next‑generation HBM logic die and memory‑centric SoCs. You will work across physical design, design rule development, EDA tool teams, foundry interfaces, and product engineering teams to deliver industry‑leading, tape-out-ready silicon under aggressive schedule, quality, and reliability constraints. This role combines full-chip physical verification ownership — including DRC, LVS, ERC, and DFM — with deep collaboration across implementation and silicon bring‑up, ensuring design integrity from layout through first silicon. Responsibilities will include, but are not limited to: Implement physical verification sign‑off for full‑chip and hierarchical designs, including Design Rule Check (DRC), Layout Versus Schematic (LVS), Electrical Rule Check (ERC), Physical Electrical Rule Check (PERC), antenna checking, and Design for Manufacturability (DFM). Implement and

About the Team: OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. In this role you will: As a Hardware Test Engineer, you will work on Machine Learning/AI hardware system projects to craft the solutions for current and future data center deployments. You will bring a strong understanding of hardware system testing, excellent project management skills, and the ability to collaborate across multiple teams to ensure efficient lab operations. You will be responsible for designing, implementing, and executing comprehensive test plans that ensure the reliability, performance, and scalability of our supercomputing hardware systems. You will develop detailed test plans and methodologies tailored to hardware components, including processors, memory modules, custom accelerators and interconnects. You will collaborate with hardware design, manufacturing, firmware teams and vendors to identify, analyze, and resolve issues affecting hardware, power, thermal and high-speed interconnects. You will perform in-depth debugging on the hardware system Excellent analytical skills to diagnose hardware issues, troubleshoot problems, and propose solutions. Ability to interpret complex test data, identify trends, and draw meaningful conclusions. High-speed links, with a focus on SerDes (Serializer/Deserializer) technology to assess signal integrity, error rates, and overall link performance. You will collaborate with the lab manager to maintain the equipment and hardware systems, including oscilloscopes, thermal test chambers, liquid cooling systems, and other mea

About the job Build the framework support that helps AI developers unlock Graphcore hardware. You will help Graphcore accelerators work seamlessly with state-of-the-art ML frameworks, including Triton and PyTorch. Reporting to a Team Lead in Frameworks, you will design, implement, optimise and support critical software. Your work will shape how researchers and engineers use Graphcore hardware for demanding machine learning workloads. You will improve performance, quality and usability across a complex software stack. You will join our Triton team, delivering new features, reviewing code, writing technical documentation and working with upstream communities. You will also help coordinate open-ended technical work across teams. This role offers deep technical ownership and visible impact as the ML software landscape evolves. You will keep learning and may contribute across other framework teams. The team and culture The Frameworks team connects Graphcore hardware with the tools ML engineers and researchers rely on. We work across PyTorch, Triton, JAX and TensorFlow, focusing on usability and performance. Work happens through agile practices, close technical discussion and clear ownership. Engineers are trusted to speak up, solve hard problems and move work forward with pace. Decisions are shaped by evidence, code quality and business outcomes. You will work across Graphcore and with open-source communities to deliver software developers can trust. What we’re looking for · Strong software engineering skills, with the judgement to manage quality, complexity and technical debt. · Experience developing in Python and C++. · Experience in compiler development. · Ability to manage complex technical tasks with cross-team dependencies. · Clear communication skills, with a proactive approach to collaboration and technical leadership. · Interest in AI, ML frameworks, performance optimisation or computationally intensive engineering. Benefits · Flexible working: Balance your work

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview At Arpalus, recently acquired by Instacart, we build AI-powered computer vision and AR technology that turns a phone camera into a precise sensing tool - real-time object detection, scanning, and spatial understanding shipped as a mobile product. Our tech runs on real devices, in real environments, under messy real-world conditions. We're a small, fast-moving team and we're looking for a Principal Computer Vision Engineer who already works that way too. You'll join our core AI team and help craft best-in-class perception systems thatpower our real-time retail analytics. You'll solve genuinely hard problems in computer vision, bridging the gap between cutting-edge research and highly optimized production pipelines. You'll work hands-on, architecting solutions that seamlessly blend classic computer vision techniques with moder

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Are you the person on your team who builds the agent everyone else ends up using? We're looking for an AI Engineer to join our Training Product team and do that at Baseten. You'll build AI-driven product features for the customers training and post-training frontier models on our platform, and you'll raise the ceiling on how Baseten itself uses AI internally, turning manual workflows into agentic ones that make every other team faster. You'll work directly with our research engineers to scope and build products, taking ideas from a research loop that already works internally to something customers can run themselves. This is a hands-on role with real autonomy. You'll pick the problems worth solving, build the harnesses, execution flows, and guardrails that make AI systems reliable, and own the results. If you've been shipping agents and want that to be the job, let's talk. EXAMPLE INITIATIVES Take a look at these blog posts written by members of our team: Baseten Training: an autoresearch substrate Introducing Baseten Loops Harnesses are everything. Here's how to optimize yours. Building with NVIDIA Nemotron 3 Ultra and LangChain Deep Agents Code on Baseten RESPONSIBILITIES Build and ship agentic product experiences, including chat-style and assistant-like interfaces, from prototype to GA. Design the harnesses, execution flows, and guardrails that make AI systems reliable in production. Build internal automation

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role The Observability, Monitoring, and Integrations team manages observability, monitoring, and detection for systems that support customer purchasing and use of GitLab. As a Staff Backend Engineer on the Fulfillment Workflow Monitoring (Catch All) team, you'll set the technical direction for the telemetry, detection, and reconciliation tooling that identifies billing, data, and event anomalies across CustomersDot, Salesforce, and Zuora before they can affect revenue or the customer experience. This is a greenfield team. You'll help build it from the ground up, shaping its operating rhythm, incident response

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role The ML Infrastructure team is responsible for building & improving the core infrastructure for autonomy teams at Nuro. In this role, you will work closely with teams across Nuro, to design, build and deploy core infrastructure components in machine learning model life cycle, to push the autonomous future forward. You will have an opportunity to work across the full stack of machine learning solutions - from designing robust and scalable model & data pipelines to building to deploying the optimized models on Nuro’s fleet of self-driving robots! About the Work Design and develop ML workflow pipelines to train, optimize, validate, and deploy Nuro autonomy models. Develop and maintain continuous testing and monitoring systems for core ML infrastructure components. Develop observability to track ML model lifecycles from data generation to on-road validation. Maintain an in-house ML inference platform to serv

Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role The ML Infrastructure team is responsible for building & improving the core infrastructure for autonomy teams at Nuro. In this role, you will work closely with teams across Nuro, to design, build and deploy core infrastructure components in machine learning model life cycle, to push the autonomous future forward. You will have an opportunity to work across the full stack of machine learning solutions - from designing robust and scalable model & data pipelines to building to deploying the optimized models on Nuro’s fleet of self-driving robots! About the Work Design and develop ML workflow pipelines to train, optimize, validate, and deploy Nuro autonomy models. Develop and maintain continuous testing and monitoring systems for core ML infrastructure components. Develop observability to track ML model lifecycles from data generation to on-road validation. Maintain an in-house ML inference platform to serv

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Baseten is seeking talented and experienced Software Engineers to join our Observability team within the Infrastructure organization. As an early member of the Observability Team, you will be pivotal in building and shaping the observability experience for our internal and external customers. By joining this team, you’ll have a direct impact on the reliability and operational excellence of Baseten's product systems. As Baseten scales its infrastructure across different cloud providers and diverse hardware, the volume and complexity of operational data is growing by orders of magnitude. This team is responsible for building high-throughput ingest pipelines, cost-efficient storage, and agentic diagnostic tools to ensure that we can detect, diagnose, and resolve issues in minutes rather than hours, even as the systems they operate become more complex. RESPONSIBILITIES Design and build scalable telemetry ingest and storage pipelines for metrics, logs, and traces across Baseten’s multi-cloud infrastructure Own and evolve core observability platforms, driving migrations and architectural improvements that improve reliability, reduce cost, and scale with organizational growth Build instrumentation libraries, SDKs, and integrations that make it easy for engineering teams to emit high-quality telemetry from their services Drive alerting and SLO infrastructure that enables teams to define, monitor, and respond to reliabil

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. We’re looking for a Staff Forward Deployed Engineer who’s excited to build with the engineers using the AI computers Tenstorrent makes. You will create continuity between customers, engineering, and AI inference service products. This is an engineering role first: you contribute production code, operate deployments, and you can explain a trade-off to customer leadership as clearly as to core engineering teams. This is a high-autonomy role with direct customer impact. This role is remote, based out of North America, with preference near one of our main hubs: Santa Clara, CA; Austin, TX; or Toronto, ON. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are You understand how accelerator compute, memory, and networking topology constrain AI workloads, and don't treat hardware as a black box. You're an early adopter of AI for your work from coding to building agentic workflows that multiply your impact. You work directly with customers to understand their challenges and provide effective solutions. You are comfortable debugging across the full inference stack: from failing requests, through the serving layer, down to OOMs or kernel dispatch if n

RDQ127R59 Summary As a Senior Applied ML Engineer on the Applied AI team at Databricks, you will use machine learning, scheduling, and optimization algorithms to maximize the efficiency and performance of our infrastructure. Your work will span the entire stack—from cluster management down to query compilation. You will solve complex, high-impact engineering problems to deliver highly optimized, cost-effective workloads for our customers. Impact You Will Have Accelerate Serverless Growth: Drive the scaling and efficiency of Databricks serverless compute products through advanced optimization techniques. Build Systems: Design end-to-end ML4Sys solutions from the ground up within a lean team of domain experts to support Shape Strategy: Define the roadmap for applied ML investments by collaborating with engineering and product leaders across Databricks. Drive Deployment: Architect, train, and deploy state-of-the-art models that directly improve product performance and cost efficiency. Scale Infrastructure: Build robust ML pipelines, data processing layers, model serving components, and production monitoring systems to help scale Innovate: Research and implement novel modeling techniques tailored specifically to computer systems and distributed environments. Minimum Qualifications Education: Background in Computer Science and Master's degree in Machine Learning, Data Science, or a related computational field (AI, Bioinformatics, EE, Physics, etc). ML Experience: Strong background in building, training, and deploying machine learning models in production. Infrastructure Knowledge: Practical familiarity with cloud computing, distributed systems, and modern data processing frameworks. Core Coding: Proficiency in Python, Scala, or Java. Preferred Skills Advanced Education: PhD in AI, Data Science, or a related technical discipline. Industry Experience: 4+ years of machine learning engineering experience in high-velocity, high-growth environment. Systems Domain: