Software Engineer Ml Infrastructure Jobs

HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world’s most innovative companies to build strong engineering teams ready for what’s next. Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills. People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards. About the role Hiring is one of the most consequential decisions a company makes. 3,000+ enterprises rely on HackerRank to get it right. We are now reinventing how that works for the agentic era. The AI/ML systems that power this platform are not auxiliary features. They are the product. Open Problems The agentic era is reshaping every layer of the hiring stack. These are some of the core problems you'll be working across, none of them fully solved. Chakra: Building an autonomous AI interviewer that conducts, adapts to, and evaluates technical interviews end to end. Integrity: Detecting fraud and suspicious behavior across multiple signal types. The ways candidates game assessments change frequently, and the models need to keep up. Evaluation: Measuring technical skill in a world where AI writes the code. The old proxies no longer hold and the new ones have not been defined yet. Your focus will shift across these depending on where the highest-leverage work is at any given time. What you will do Research, Design, and Develop advanced Deep Learning models and novel Neural architectures. Explore architectures beyond Transformers, including State Space Models (SSMs), Liquid Neural Networks (LNNs), and Hybrid approache

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. A CAD Engineer is a development/support role focusing on semi-conductor / EDA design flows/tools. At Micron Technology, Inc., you will be working in a fast-paced, collaborative, production support role evaluating, developing, improving, and debugging both in-house and commercial EDA tools and flows for the design, physical layout, and verification of CMOS integrated circuits. Responsibilities and Tasks include, but not limited to: Serves as a subject matter of expert within a technical discipline Defines and leads large projects Provide input to team members and managers on technical or innovation Guide the ML/AI implementation on CAD flows Providing training, documentation, and support to end-users on new tools and methods Collaborating closely with design and process teams Develop and apply CAD software engineering methods, theories, and research techniques in support of the organization's product development. Assignments include crafting and developing CAD software, assessing architecture and hardware limitations, and defining and implementing CAD software engineering applications. Successful candidates will have the following: Coursework or experience in EE, CE or CS including VLSI circuit design and software Expertise in IC design CAD tools (schematics, layouts, simulation, and verification tools) is required Exposure to CMOS circuit design and implementation including area, speed, and power tradeoffs Excellent programming fundamentals are required In-d

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: Large teams of agents are starting to solve problems no single agent can, from rewriting major codebases to formalizing landmark mathematics. Our team studies how these teams scale: what happens to performance, cost and coordination as the number of agents, the compute budget and the length of the task grow, and what has to change to keep getting returns from that scale. We build the platform and evaluations Anthropic uses to run and measure large agent teams, and other research teams build on them. This role lives at the boundary between research and engineering. It is a generalist role on a small team: you'll design and run large experiments, build the systems they run on, and get to the bottom of surprising results. We often need to go from a vague question to a running experiment quickly. Responsibilities: Design, run and interpret large-scale experiments on agent teams, reasoning rigorously about what the data does and doesn't show Investigate how performance and efficiency change as team size, compute and task horizon grow, and find the bottlenecks that limit them Build and scale the systems that run very large agent teams reliably, and debug the failures that only appear at scale Design evaluations for long-horizon problems, and keep their results trustworthy Build the tooling and metrics that let researchers see what a large agent team is doing and why Partner with research teams across Anthropic so they can run their own experiments on the platform, and communicate findings clearly You may be a good fit if you: Have significant software engineering, ML or research engineering experience Have owned something su

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We're hiring Cyber Evaluations Engineers to build and run the evaluations that measure cyber-relevant capabilities and safeguard robustness in our models. You'll design new evals, run per-release robustness testing, and dig into data on jailbreaks and prompt bypasses to understand where our safeguards hold up and where they don't. You'll also design many of the probes that detect cyber abuse in production and help shape the overall detection architecture alongside the policy team. Key responsibilities Design and run capability, uplift, and safety evaluations to assess cyber-relevant risk in new models Execute per-release safeguard-robustness testing ahead of major model launches Analyze evaluation results and communicate findings clearly to the team and to stakeholders Design, prototype, and tune detection probes for cyber misuse Work with the cyber policy team to turn policy lines into a layered, robust abuse-detection architecture, and measure its precision and coverage over time Build and maintain internal tooling used to run and score evaluations Collaborate with policy and engineering partners to translate eval findings into safeguard improvements Minimum qualifications Experience building or running evaluations, benchmarks, or test suites for software or ML systems, including delivering results on short, fixed timelines Hands-on cybersecurity experience (e.g., CTF participation, vulnerability research, exploit development, or security research) Proficiency in Python Strong ability to communicate evaluation results with multiple cross-functional stakeholders or potential policy stakeholders Preferred qualific

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. Our IP delivery timelines are set as much by flow maturity as by design work. This role develops, deploys, and owns the RTL-to-GDSII methodology the IP physical design team runs on, so a new block, node, or customer variant starts from a working flow instead of a cold start. This role is hybrid, based out of Toronto, ON; Austin, TX, or Belgrade, Serbia. 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 A physical design or CAD methodology engineer who has built flows that production teams depend on daily. Automation-minded, happiest when you are removing manual steps and making PPA exploration repeatable. Building with AI as part of how you develop flows, and opinionated about where LLMs and ML-driven optimization genuinely help versus where they do not. An effective partner to design teams and EDA vendors, and a clear writer who documents flows well enough that others can run them without you. What We Need An Engineer with 5+ years developing and supporting physical design methodology or CAD flows in production use. Expertise with industry-standard tools (FusionCompiler/ICC2, Innovus/Genus, PrimeTime, RedHawk) and scripting languages (T

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Frontier Agent Engineer (Applied AI) , you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in. What You'll Build Frontier AI Systems Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use. Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterprise data

About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX or London, UK (Hybrid) About the Role You’ll help define how machine learning models run across Cloudflare’s global network, from frontier open LLMs and real-time voice models to customer-deployed models served on heterogeneous GPUs and next-generation accelerators. You’ll work with systems engineers, product teams, hardware partners, and AI/ML engineers to bring models into production with low latency, strong reliability

Why RoboForce RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are hiring a Lead AI Native Engineer to build RoboForce’s vertical agents and shared agent foundation. Reporting to the co-founder, you will turn frontier models into systems for strategy and engineering. This is a hands-on technical leadership role: you will write code, set architecture, and enable agent development—not lead People programs or organizational transformation. Responsibilities Build vertical agents. Own end-to-end agents for high-value workflows across research, software, hardware, data, and operations—from problem definition through production use. Establish the agent foundation. Build reusable primitives for models, tools, orchestration, context, memory, retrieval, permissions, human approval, and long-running execution. Create the context layer. Connect agents to trusted data through APIs, pipelines, MCP servers, and integrations with clear provenance and access control. Own evaluation and reliability. Build benchmarks, regression tests, tracing, monitoring, and failure-analysis loops across quality, latency, cost, security, and resilience. Advance frontier agent usage. Evaluate new models, coding agents, SDKs, and patterns, then turn useful capabilities into maintainable systems rather than demos. Support strategic initiatives. Help company leadership apply agents and analytical systems to market and customer intelligence, partnerships, fundraising, diligence, scenario analysis, and executive decisions. Provide technical leadership. Set architecture and engineering standards, review designs and code, and create reusable patterns for the technical team. Requirements 5+ years in software engineering, applied AI, ML systems, or a related field, with strong zero-to-one technical judgment. Exper

Title: Senior AI QA Engineer Location: Bengaluru (Bangalore) Opportunity: As a Senior AI QA Engineer for our Precision Patient Care Pipeline , you will go beyond traditional functional testing. You will be responsible for building the framework that ensures our clinical insights are accurate, safe, and reliable. This role requires a unique blend of high-level software testing and data engineering to validate complex, non-deterministic medical outputs using a hybrid of automated grading methodologies . Key Responsibilities: Architect Multi-Layered Validation Frameworks: Design and implement structured testing strategies that combine deterministic checks, semantic similarity metrics, and model-based evaluations. Automated Model Grading: Develop systems to evaluate clinical pipeline outputs for faithfulness, safety, and hallucination detection using various automated scoring techniques (e.g., BERTScore, ROUGE, or custom heuristics). Vibe-Driven Development: Leverage agentic AI tools to rapidly prototype complex test harnesses, "red-team" clinical logic, and build internal validation utilities at high velocity. Data Pipeline Integrity: Execute integration and regression tests for data-heavy backend processes, ensuring medical data remains consistent from ingestion to insight generation. Collaborative Strategy: Work closely with Data Scientists and Product Managers to define "Ground Truth" datasets and clinical evaluation rubrics. Root Cause Analysis: Deep-dive into complex system failures to identify whether issues stem from code logic, data drift, or model behavior. Requirements: 6+ years of technical experience in Quality Assurance, with a strong focus on system architecture and backend data validation. Advanced Python Proficiency: Expert-level skills in Python for building custom test scripts and working within AI/ML ecosystems. AI/ML Validation Experience: Proven experience testing model outputs using diverse metrics (e.g., Semantic Similarity, NLP metrics, an

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. Tenstorrent is building next-generation AI compute. The Kernel Development and Optimization team develops the performance-critical kernels that unlock the full capability of our hardware across ML and HPC workloads. This role is hybrid based out of Belgrade, Serbia. 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 A strong C++ systems engineer with experience writing performance-critical or low-level software. Comfortable reasoning about concurrency, synchronization, latency hiding, and compute versus memory trade-offs. Data-driven in your approach, using profiling and benchmarking results to guide optimization decisions. Effective at debugging complex runtime or kernel-level issues in large codebases. Structured thinker who can break down ambiguous performance problems into measurable experiments. What We Need Engineers who can design, implement, and optimize GPU-style kernels such as matrix multiplication, attention primitives, and data-movement operations. Clear ownership of performance, from identifying bottlenecks to delivering measurable throughput improvements. Contribution to host-side orchestration code and parallelizati

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. As a C++ Machine Learning Engineer on the AI Models team at Tenstorrent, you’ll work on the training framework behind our most advanced models. You’ll write high-performance C++ code, shape how new layers and operators are implemented, and help models scale across our custom silicon. If you enjoy building the guts of ML systems and seeing them run fast, this role is for you. This role is hybrid, based out of Warsaw or Gdansk, Poland. We also consider remote candidates within Poland on a case-by-case status. 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 Strong in C++ and low-level systems programming, especially in performance-critical code. Comfortable thinking in tensors, memory layout, and compiler graphs. Familiar with PyTorch and curious about how frameworks map to hardware. A builder who enjoys solving technical puzzles and digging into the details. What We Need Extend and optimize our ML training framework with new ops, layers, and training features. Debug and tune model performance on Tenstorrent chips. Work with compiler and kernel teams to make sure models compile and run as expected. Support integration of real-world models

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. About the Role As a Senior Design Verification Engineer, you will develop and support the tools, automation, and flows used to design, verify, and tape out advanced memory products. You will work closely with design, layout, verification, and process teams to improve engineering productivity and design quality in one or more areas, including layout automation, physical verification, circuit simulation, and digital implementation. Responsibilities Develop, enhance, and debug CAD tools, automation, and flows for custom IC design, layout, simulation, and verification. Contribute to one or more functional areas: layout automation, physical verification, circuit simulation, or digital implementation. Integrate, evaluate, and qualify in-house and commercial EDA tools in production environments. Partner with design, layout, verification, and process teams to resolve workflow and tool-integration challenges. Identify gaps in current tools and flows, and evaluate EDA industry offerings to improve quality, performance, and efficiency. Provide training, documentation, and production support to end users. Apply AI/ML and agentic AI to CAD automation, debugging, and user support. Develop clean, scalable software and mentor engineers to support their technical growth. Lead technical projects and contribute to CAD methodology roadmaps. Minimum Qualifications Bachel

Altana is the network for trusted trade. Our AI-powered product network empowers governments and businesses to build a more resilient and secure global economy while keeping trade flowing. The Opportunity at Altana We're looking for a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, our agentic platform for global trade. You'll work on a continuous stream of public and proprietary trade data (shipments, customs filings, trade documents), and build the models and AI agents that turn it into real-time decisions on risk, compliance and enforcement for governments and the world's largest companies. Your Responsibilities Drive the technical direction for agentic systems. Define the architecture and roadmap for the models and agentic systems that process trade data as it arrives (shipments, customs declarations, invoices, bills of lading) and turn it into risk, classification and compliance decisions in real time. Build the pipeline from ingestion to decision. Lead the design of reliable, cost-efficient systems for ingestion, document understanding, entity resolution, inference and routing across models, at the scale of global trade. Adapt models to trade. Use analyst feedback, production outcomes and agent traces to fine-tune and post-train models (SFT, DPO, RL with domain graders) where it measurably beats prompting, and keep every model improving over time. Design the observability and feedback stack. Build the evals, tracing and feedback loops that decide what ships and catch regressions in production. Lead beyond your team. Mentor senior engineers, raise the technical bar across Engineering, and help shape how we hire and level ML talent. Shape the product and the business. Work with software engineers, data scientists, product managers and customers to generate new business ideas and turn ambiguous problems into shipped capabilities. About You BS/MS in Computer Science, Machine Learning, or a related field, or equivalent experi

Altana is the network for trusted trade. Our AI-powered product network empowers governments and businesses to build a more resilient and secure global economy while keeping trade flowing. The Opportunity at Altana We're looking for a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, our agentic platform for global trade. You'll work on a continuous stream of public and proprietary trade data (shipments, customs filings, trade documents), and build the models and AI agents that turn it into real-time decisions on risk, compliance and enforcement for governments and the world's largest companies. Your Responsibilities Drive the technical direction for agentic systems. Define the architecture and roadmap for the models and agentic systems that process trade data as it arrives (shipments, customs declarations, invoices, bills of lading) and turn it into risk, classification and compliance decisions in real time. Build the pipeline from ingestion to decision. Lead the design of reliable, cost-efficient systems for ingestion, document understanding, entity resolution, inference and routing across models, at the scale of global trade. Adapt models to trade. Use analyst feedback, production outcomes and agent traces to fine-tune and post-train models (SFT, DPO, RL with domain graders) where it measurably beats prompting, and keep every model improving over time. Design the observability and feedback stack. Build the evals, tracing and feedback loops that decide what ships and catch regressions in production. Lead beyond your team. Mentor senior engineers, raise the technical bar across Engineering, and help shape how we hire and level ML talent. Shape the product and the business. Work with software engineers, data scientists, product managers and customers to generate new business ideas and turn ambiguous problems into shipped capabilities. About You BS/MS in Computer Science, Machine Learning, or a related field, or equivalent experience 8+

Who we are At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences. Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands. . Hiring and how we work We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions! Also, while we are a remote-first company, you may be asked to report in person on an ad-hoc basis for team gatherings, functional off-sites or customer meetings. . See yourself at Twilio Join the team as our next Staff Machine Learning Engineer on Twilio’s Conversation Memory team. About the job In this role, you will build the foundational AI/ML capabilities that power intelligent engagement experiences for Twilio customers - letting any application, human or AI agent, or channel know who a customer is, what they have done, and how best to reach them. This role requires a builder and curious mindset, someone who can bridge the gap across theoretical research, production-grade experimentation and enterprise quality software. You will lead rapid research cycles to solve complex problems regarding contextual intelligence and large scale ML problems ensuring every interaction feels seamless and personalised. Responsibilities In this role, you’ll: Build out new functionality within a fast moving product seeing rapid adoption. Take a nebulous idea, design a rigorous experiment, and execute it within weeks. Conduct deep-dive research