Вакансии

Location: San Francisco; Toronto; New York; Montreal; Seattle Workplace: Hybrid 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 https://www.baseten.co/blog/announcing-our-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 Voice is becoming the internet’s next interface, but a production-grade Voice AI system is "hard to build" https://greylock.com/greymatter/voice-agents-easy-to-use-hard-to-build/. You’ll join a small founding team of Baseten Voice AI, focused on bringing state-of-the-art open source models into production for Voice AI customers across productivity, customer service, clinical conversation, creator tools, education, and more. You’ll make a meaningful impact on people’s daily lives and help reshape these industries. This is a high-impact, high-ownership role. You will be the primary owner of Baseten Voice AI - our in-house inference stack to power Voice AI models - from product roadmap through engineering implementation. You’ll partner closely with Forward Deployed Engineers, Model Performance Engineers, and sister engineering teams to push the boundaries of Voice AI. EXAMPLE INITIATIVES - Develop world-class model serving stack for state-of-the-art open-source voice models - reduce end-to-end and tail latency (p95/p99), increase throughput, and improve GPU efficiency via profiling, runtime tuning, and server-level optimizations. - Build large-scale, real-time infrastructure for multi-model voice agents - orchestrate STT, TTS, and agent components with streaming I/O to meet customer SLOs. - Design tight training and inference iteration loops for voice model customization - enable fast evaluation, safe rollout, and rapid experimentation for custom voice model development. - Past projects: - Baseten leads Coval’s voice AI benchmark https://www.baseten.co/blog/baseten-leads-covals-voice-ai-benchmark/ - How Notion achieved 2-3x lower latency with AI Meeting Notes https://www.baseten.co/resources/customers/how-notion-acheived-2-3x-lower-latency-with-ai-meeting-notes/ - Cost-efficient, high-performance TTS with Qwen3-TTS https://www.baseten.co/blog/cost-efficient-high-performance-qwen3-tts/ - Fine-tuning Qwen3-TTS for high-quality voice cloning https://www.baseten.co/blog/fine-tuning-qwen3-tts-for-high-quality-voice-cloning/ - The fastest Whisper — with streaming and diarization https://www.baseten.co/blog/the-fastest-whisper-transcription-with-streaming-and-diarization/ - NVIDIA Nemotron 3 Diarization: real-time speaker labels at a cent per audio hour https://www.baseten.co/blog/nvidia-nemotron-3-diarization/ RESPONSIBILITIES - Own and lead Voice AI product areas end-to-end - from architecture and system design through implementation, rollout, and long-term production operations. - Design, build, and operate real-time, large-scale, high-performance model serving systems for STT, TTS, and voice agent workloads for mission-critical customer deployments - Drive cross-team collaboration with sister engineering teams to solve full-stack technical problems, align on priorities, and coordinate end-to-end delivery across the product surface area - Mentor teammates through code reviews, design docs, and technical leadership. REQUIREMENTS - Bachelor's degree or higher in Computer Science or related field - Proven track record owning production-grade real-time, large-scale systems where tail latency (p99) matters. - Proficient coding abilities in one or more popular programming or scripting languages; Python proficiency is a plus. - Good taste in product, particularly developer-oriented tools - Interest in ML/AI infrastructure and willingness to learn - Strong collaboration and communication skills - Comfortable using AI coding assistants (e.g., Claude Code, Codex, Cursor) as a daily productivity multiplier — as an AI-native company, we see this as a must-have skill. NICE TO HAVE - Experience implementing pipeline-level model runtime optimizations such as dynamic batching, async scheduling, or decode-side throughput improvements. - Experience building developer platforms: SDKs, CLIs, APIs, and self-serve workflows for ML or infrastructure products. - Experience with containerization and orchestration technologies (Docker, Kubernetes), service meshes, or distributed scheduling. - Familiarity with speech/audio ML models (STT, TTS, speech-to-speech) - Familiarity with model-serving runtimes (vLLM, TensorRT, ONNX). - Familiarity with systems-level performance profiling across host-device boundaries (e.g. PyTorch Profiler), diagnosing GPU utilization issues - Exposure to customer-facing engineering: pre-sales prototyping, technical discovery, or working directly with customers to ship solutions. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: Remote - Bay Area MinIO is the data and memory foundation for enterprise AI. Built for the speed, scale, and economics that AI and analytics demand, AIStor and MemKV unify every layer of the data stack, from agentic and inference context memory to tables and objects across core, edge, and cloud. Trusted by 77% of the Fortune 100, MinIO is redefining how AI factories, intelligent applications, and autonomous agents secure, persist, and unlock the full value of their data. The Field Architect role at MinIO is a senior customer-facing technical leadership position focused on defining, validating, and guiding AIStor architectures for AI, analytics, cloud-native applications, and large-scale data infrastructure. You will serve as a trusted technical advisor to customers, partners, and internal teams, translating business and application requirements into robust technical designs that deliver measurable outcomes. You will collaborate closely with the broader MinIO team to ensure customers are successful through technical discovery, architecture design, proof-of-value execution, production onboarding, performance validation, and long-term expansion. Success in this role requires deep technical curiosity, hands-on architecture skills, strong communication, and the ability to connect AIStor’s capabilities to the data-intensive workloads that matter most to our customers. What You Will Do: Technical Leadership & Architecture Technical Architecture and Discovery: Lead technical discovery with customers and partners to understand their business goals, application requirements, data architecture, infrastructure constraints, and success criteria. Define scalable AIStor architectures that map directly to those requirements. AI and Analytics Workload Design: Define and validate AIStor architectures for AI/ML training and inference, analytics platforms, data lakehouse environments, high-performance data pipelines, large object workloads, and modern application data services. Object Storage and S3 Expertise: Serve as a subject matter expert in object storage, S3-compatible architectures, distributed systems, metadata, replication, erasure coding, security, performance tuning, and large-scale namespace design. Customer Engagement & Advisory Customer Technical Advisor: Build trusted relationships with customer technical stakeholders, including infrastructure, platform, analytics, AI, security, application, and operations teams. Guide customers toward informed architecture decisions and proactively identify opportunities to improve performance, resilience, scale, and operational efficiency. Proof-of-Value Execution: Lead technical evaluations, system configurations, performance testing, interoperability validation, and proof-of-value engagements that demonstrate AIStor’s value in real customer environments. Customer Success Beyond the Win: Partner with Customer Engineering, Product Management, and Engineering teams to help customers move from evaluation to production and from initial deployment to broader adoption across additional workloads, teams, and use cases. Ecosystem, Enablement & Field Operations Cloud-Native and Kubernetes Guidance: Provide technical leadership on Kubernetes, containers, automation, and cloud-native deployment patterns, including hybrid cloud, private cloud, public cloud, and edge infrastructure models. Solution Integration and Ecosystem Collaboration: Work with technology partners, reseller partners, and customer teams to support complete solutions involving analytics engines, AI platforms, Kubernetes distributions, data protection tools, identity systems, observability platforms, and public cloud services. Technical Presentations and Enablement: Build and deliver compelling architecture presentations, whiteboard sessions, demonstrations, best practices, workshops, and technical enablement sessions for customers, partners, and internal teams. RFP, RFI, and Technical Response Support: Contribute to or lead technical portions of RFPs, RFIs, RFQs, security reviews, architecture questionnaires, and competitive evaluations. Product Intelligence & Market Expertise Product and Engineering Feedback: Advocate for customer requirements internally by capturing field feedback, competitive insights, workload patterns, integration needs, and enhancement opportunities. Work with Product and Engineering teams to help inform roadmap priorities and improve customer outcomes. Competitive and Market Expertise: Stay current on object storage, data lakehouse, AI infrastructure, analytics, Kubernetes, cloud, and competitive storage trends. Help internal teams and customers understand where AIStor is differentiated. Your Skills and Experience: Strong background in enterprise storage, object storage, distributed systems, cloud infrastructure, data platforms, or high-performance data-intensive applications. Strong understanding of object storage concepts and S3-compatible architectures; experience with HTTP, REST APIs, JSON, security models, replication, data protection, and scale-out system design is highly valuable. 10+ years of experience in a senior customer-facing technical role such as Field Architect, Solutions Architect, Sales Engineer, Systems Engineer, or Technical Consultant. Hands-on experience with Linux, networking, storage systems, virtualization, containers, Kubernetes, and modern data center infrastructure. Experience designing or supporting AI, machine learning, analytics, data lakehouse, or large-scale data pipeline environments. Familiarity with ecosystem technologies such as Databricks, Starburst, Spark, Trino, Iceberg, Delta Lake, Kafka, PyTorch, TensorFlow, or other AI and analytics frameworks is a strong plus. Experience with public cloud and hybrid cloud environments, including AWS, Azure, GCP, OCI, or private cloud platforms. Ability to conduct technical discovery, design architectures, build demonstrations, guide proof-of-value execution, analyze performance, and communicate tradeoffs clearly. Comfortable working with heterogeneous enterprise environments involving compute, storage, networking, security, identity, observability, backup, disaster recovery, and application teams. Strong verbal, written, whiteboarding, and presentation skills, with the ability to communicate complex technical concepts to both technical and executive audiences. Ability to work collaboratively across customers, partners, Sales, Product, Engineering, and Customer Engineering teams. Strong customer-success orientation, intellectual curiosity, sense of ownership, and ability to prioritize effectively in a fast-moving environment. Willingness to travel as needed to support customers, partners, field events, and internal team activities. Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field; equivalent practical experience will also be considered. What We Offer: Health Care Plan (Medical, Dental & Vision) 401K with 3% Contribution Pre-IPO Stock Options At least 12 Public Holidays Flexible Time Off Pay Transparency MinIO determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. The salary range for this position is $200,000 - $225,000 base with commission. Actual offer will be based on your qualifications. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience. Equal Opportunity Policy (EEO) MinIO is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.

Location: London, UK 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 The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £370,000 — £630,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Remote-Friendly (Travel-Required) | San Francisco, CA | New York City, NY 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 The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Ukraine; Spain; Poland; Portugal; Romania About Us At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed. Now we are looking for a Senior ML Operations Engineer to join us as part of our team. About the role As a Senior ML Operations Engineer, you will support teams building and deploying AI-driven solutions, helping them overcome common challenges in the ML development, deployment, and support lifecycle. This role offers autonomy in decision-making, encouraging a proactive approach to solving complex issues and finding optimal solutions for scalable systems. About the project PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials and many more. Team composition 1 Engineering Manager, 4 Senior ML Ops Engineers Work Environment The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology. As a qualified expert, You will Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation. Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment. Provide mentorship, technical guidance, and perform code reviews for team members. Design and deliver on projects end-to-end with little to no guidance. Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC. Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities. Support the vision and values of the company through role modeling and encouraging desired behaviors. Participate in various cross-functional company initiatives and projects as requested. Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value. Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance. We’re looking for you if you have Degree in Computer Science, Information Systems, Machine Learning, or a similar field preferred (or equivalent practical experience). 5+ years of hands-on software development experience with Python (Java experience with strong Python proficiency also considered). 4+ years of experience designing and building distributed software systems and architectures. 3+ years of hands-on experience deploying and operating Machine Learning services in production. Experience supporting ML lifecycle operations including post-deployment monitoring and maintenance. Experience in cloud-native stack, with a practical understanding of containerization technologies such as Kubernetes and Docker. Demonstrated experience with SQL and NoSQL database design and implementation. Ability to decompose complex problems into iterative, well-defined solutions. Strong problem-solving abilities with focus on building scalable, efficient, and maintainable systems. Strong communication and collaboration skills, with the ability to engage effectively with internal customers across various cultures and regions. Ability to be a team player who can also work independently. Experience working across multiple development teams is a plus. Bonus points Experience with cloud platforms (AWS, Google Cloud Platform, or Azure). Proficiency in GitOps practices and CI/CD pipeline development and management. Observability and monitoring: Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems. LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock). LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar). Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector). RAG Systems: Experience building Retrieval-Augmented Generation systems and evaluating both retrieval quality and generation performance. Cloud-native experience with services like Amazon SageMaker, Google Vertex AI, or Azure ML Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka. ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow. API development with FastAPI or similar frameworks. Java programming experience is a plus. What’s in it for You Reveal great tech solutions Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling clients growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients. Experience an agile and flexible working environment Work fully remote around the globe, or in a hybrid work model from our office hubs which are equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. Empower yourself with a stable workload and a stable income, focusing on lasting cooperation and high-performance culture. Embrace the opportunity for personal and professional growth Take advantage of performance review and career path. Elevate your skills with mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more. Feel cared about Be among like-minded people and feel supported with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. Interview steps Interview with the recruiter Live Coding (up to 30 min) Tech Interview: ML Fundamentals, Software Engineering (up to 60 min) ML System Design Interview (up to 45 min) Interview with Engineering Manager (60 min)

Location: Morocco We are looking to hire passionate Senior AI Engineers to help turn data into intelligent, production-ready solutions. You will work across the full AI stack: traditional machine-learning models, large language models (LLMs), computer-vision pipelines, and analytics / forecasting workflows. If you enjoy exploring data, building state-of-the-art models, and shipping reliable AI services, we would love to meet you. Responsibilities Model Development – Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion). Data Exploration & Analytics – Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs. End-to-End Pipelines – Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring. LLM & Agentic AI Engineering – Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements. AI Automation & Integration – Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines. Continuous Learning – Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team. Mentor junior engineers and contribute to technical direction and engineering best practices. Requirements BSc in Computer Science, Mathematics, or related field. 5+ years of professional experience working on AI/ML projects. Good command of English and Arabic (written and spoken). Proficient in Python and core libraries (PyTorch / TensorFlow, scikit-learn, pandas, NumPy). Solid understanding of machine-learning algorithms, deep-learning fundamentals, and basic statistics. Experience with data wrangling and visualization (Matplotlib / Plotly) and exploratory analysis. Familiarity with at least one of: OpenCV, Hugging Face Transformers, LangChain, MLflow, or similar. Good grasp of software-engineering best practices: Git, code reviews, testing, CI. Preferred Qualifications Knowledge of C++ or C# for performance-critical modules. Experience deploying models via Docker, Kubernetes, or cloud AI services. Exposure to vector databases and RAG workflows. Skill in BI / dashboard tools (Power BI, Tableau, Streamlit) or time-series frameworks (Prophet, statsmodels). Familiarity with MLOps / LLMOps tooling (DVC, MLflow Tracking, Weights & Biases, BentoML). Experience with image processing techniques (e.g., OpenCV, image segmentation, feature extraction) Experience with Spark (PySpark) and distributed data processing, including usage of platforms such as Databricks, AWS EMR, or GCP Dataproc. Strong SQL skills and experience working with large-scale datasets, including partitioning and performance tuning. Familiarity with modern data lake architectures and scalable data storage concepts.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a strong Robotics Learning Engineer to join our Manipulation team based in London. In this role, you will work on teaching our robots to manipulate the world around them. This is a role at the intersection of applied deep learning and robotics, and to be set up for success you need both experience of working with real robot hardware (e.g. identifying issues in control or teleop), and applied deep learning (you don’t have to be an expert on cutting edge neural network techniques, but you should be perfectly capable of curating data, fine-tuning a policy on that data and hypothesising potential mitigations when something doesn’t work). WHAT YOU’LL DO - Post-train manipulation policies via behaviour cloning and RL; own the full loop from data to deployment. - Come up with data preprocessing strategies to improve the quality of collected data. - Work with the simulation team to set up RL training using digital twin, and then iterate on reward and simulation quality to ensure successful transfer to the real world. - Partner with the data collection organization to drive data collection activities for a specific capability: specify what good data looks like, ensure diversity and coverage, and iterate on instructions. - Expand observation and action spaces with new components required to support novel capabilities, and work with the Teleoperations team to expose these components to robot operators. - Partner with Teleoperations and Controls teams to improve motion smoothness and teleoperation experience. - Interface with hardware design team to ensure that manipulation team findings regarding the current generation of hardware are reflected in future designs. WHAT WE'RE LOOKING FOR - 3+ years working on robots (industry or research) with shipped artifacts to show for it. A good understanding of modern teleoperation and low-level control stack. - Experience with neural network post-training. - Familiarity with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training, PyTorch or JAX. Ability to profile & debug numerics and write maintainable research code. - Good familiarity with modern software engineering practices. - Ability to document experiments clearly and communicate trade‑offs crisply. Nice to have: - Experience training VLA models for manipulation (autoregressive, diffusion or flow-matching based). Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open VLA frameworks. - Experience applying RL to robotics problems. - Publications at top-tier robotics or deep learning conferences or equivalent open‑source contributions. WHAT WE OFFER - Competitive equity: stock options with meaningful upside as we scale. - 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown). - Private healthcare, including virtual and in-person care. - Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Location: UK, London Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE We're hiring a Senior Dev/MLOps Engineer to join our Data & Compute Platform team based in London. WHAT YOU’LL DO - Own and manage the full lifecycle of both ML models and core infrastructure - from development and deployment to monitoring and continuous improvement. - Build and maintain robust CI/CD pipelines for both software and ML workflows. - Ensure reliability, scalability, observability, and security of production systems and ML infrastructure. - Automate deployment, orchestration, and environment management using modern DevOps tooling. - Collaborate closely with software engineers, data scientists, and product teams to bring ML-powered features to production. - Proactively detect, troubleshoot, and resolve infrastructure and model performance issues. - Stay up to date with industry best practices in DevOps, MLOps, and infrastructure engineering. - Document infrastructure, workflows, and operational procedures clearly and thoroughly. WHAT WE'RE LOOKING FOR - Proven experience in a senior-level DevOps, MLOps, or related infrastructure-focused engineering role. - Strong proficiency in Python and familiarity with ML frameworks such as TensorFlow or PyTorch. - Deep experience with cloud platforms (AWS, GCP, or Azure) and container orchestration tools (Docker, Kubernetes). - Solid understanding of CI/CD systems (e.g., GitHub Actions, GitLab CI, ArgoCD) and infrastructure-as-code tools (e.g., Terraform, Helm). - Familiarity with data engineering concepts such as ETL pipelines, data lakes, and large-scale batch/stream processing. - Ability to design scalable, secure, and observable systems in fast-moving environments. - Strong debugging and problem-solving skills across distributed systems. - Excellent collaboration and communication skills, with experience working in cross-functional teams. - Understanding of security and compliance best practices for both software and ML systems. WHAT WE OFFER - Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays. - Fully funded private healthcare for UK employees, with broad provider access, virtual and in‑person care, and strong mental health and serious illness support. - Equity included–we believe builders should share in what they build. - Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings. - Free daily breakfast, catered lunch, and snacks in‑office. - Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics. - Freedom to influence the product and own key initiatives.

Location: United States; Canada (BC & ON only) Workplace: Remote Webflow is the agentic web marketing platform for modern marketing teams, helping organizations build, manage, and optimize high-performing web experiences that drive predictable growth and strengthen brand trust. Building at Webflow will need grit, because we move fast, without ever sacrificing craft or quality. We’re looking for a Senior Staff Machine Learning Scientist to help us solve challenging problems to address emerging customer needs and behaviors. The ideal candidate can move fast but with high quality, and views the early stages of product development as a creative canvas and opportunity for high impact. ABOUT THE ROLE: - Location: Remote-first (United States; BC & ON, Canada) - Full-time - Permanent - Exempt - Our cash compensation amount for this role ranges depending on the cost of labor of the geographic area. The ranges shared below may change if you are hired in another geographic location.  - United States  (all figures cited below in USD and pertain to workers in the United States) - Zone A: 232,000 - 290,000 - Zone B: 217,600 - 272,000 - Zone C: 204,800 - 256,000 - Canada  (All figures cited below in CAD and pertain to workers in ON & BC, Canada) - 253,000 - 316,000 Please visit our Careers page https://webflow.com/careers#pay-zones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. - Application Information: - Application deadline: applications accepted on an ongoing basis until position is closed and filled - This posting is for a new position - Reporting to the Director, Engineering  As a Senior Staff Machine Learning Scientist, you’ll:  - Lead and drive ambitious research initiatives that advance the state of the art in computer vision, multimodal understanding, and visual generation. - Develop novel models, algorithms, and training methodologies for challenging vision problems such as image understanding, video understanding, visual search, scene representation, segmentation, detection, generation, and multimodal reasoning. - Translate cutting-edge research into practical model improvements that can shape product direction and unlock new user experiences. - Design, implement, train, and optimize large-scale vision and multimodal foundation models across diverse datasets and tasks. - Partner closely with applied scientists, ML engineers, and product teams to move research from exploration to production-ready systems. - Set technical direction for high-impact research areas, identifying promising bets and influencing longer-term strategy for vision and multimodal AI. - Mentor other scientists and engineers, raise the quality bar for research, and help build a strong scientific culture across the team. - Stay at the forefront of research in computer vision, multimodal learning, generative modeling, and foundation models, and apply emerging techniques to real-world problems. ABOUT YOU: Requirements: - BA/BS degree or equivalent experience You’ll thrive as a Senior Staff Machine Learning Scientist if you: - Deep theoretical and practical expertise in computer vision, with strong foundations in areas such as representation learning, visual recognition, image or video generation, multimodal learning, and large-scale model training. - Advanced degree in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related field; PhD strongly preferred. - 8+ years of relevant industry and/or research experience, or equivalent impact, including a track record of driving major research initiatives in computer vision or multimodal machine learning. - Strong experience with state-of-the-art vision and multimodal model architectures, including transformers, diffusion models, contrastive learning approaches, and foundation models. - Proven ability to formulate research problems clearly, design rigorous experiments, and translate findings into meaningful model or product advances. - Strong coding and prototyping skills in Python, with the ability to write clean, scalable, and well-documented research code. - Proficiency in modern deep learning frameworks such as PyTorch and TensorFlow. - Demonstrated technical leadership, including mentoring scientists or engineers and influencing research direction across a team or organization. - Strong communication skills, with the ability to present complex research clearly to both technical and cross-functional audiences. - Stay curious and open to growth — demonstrating a proactive embrace of AI, and actively building and applying fluency in emerging technologies to elevate how we work, drive faster outcomes, and expand collective impact.   OUR CORE BEHAVIORS - Build lasting customer trust. We build trust by taking action that puts customer trust first. - Win together. We play to win, and we win as one team. Success at Webflow isn't a solo act. - Reinvent ourselves. We don't just improve what exists, we imagine what's possible. - Own it all the way. We move fast because the moment demands it, and we hold ourselves accountable to deliver outcomes — for each other, and for customers.   BENEFITS - Ownership in what you help build. Every permanent Webflower receives equity (RSUs) in our growing, privately held company. - Health coverage that actually covers you. Comprehensive medical, dental, and vision plans for full-time employees and their dependents, with Webflow covering most premiums. - Support for every stage of family life. 12 weeks of paid parental leave for all parents and 6+ weeks of additional paid leave for birthing parents. Plus inclusive care for family planning, menopause, and midlife transitions. - Time off that’s actually off. Flexible vacation, paid holidays, and a sabbatical program to help you recharge and come back inspired. - Wellness for the whole you. Access to mental health resources, therapy and coaching. - Invest in your future. A 401(k) with 100% employer match (up to $6,000/year) in the U.S., and support for retirement savings globally. - Stipends to support our distributed team. Localized support for cell, Wi-Fi, and WFH expenses to help you settle into your best remote life. - Bonus for building together. All full-time, permanent, non-commission employees are eligible for our annual WIN bonus program.   Temporary employees may be eligible for paid holiday and time off, statutory leaves of absence, and company-sponsored medical benefits depending on their Fixed Term Contract and their country/state of employment.   BE YOU, WITH US At Webflow, equality is a core tenet of our culture. We are an Equal Opportunity (EEO)/Veterans/Disabled Employer and are committed https://webflow.com/diversity-equity-inclusion to building an inclusive global team that represents a variety of backgrounds, perspectives, beliefs, and experiences. Employment decisions are made on the basis of job-related criteria without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other classification protected by applicable law. Pursuant to the San Francisco Fair Chance Ordinance, Webflow will consider for employment qualified applicants with arrest and conviction records.   STAY CONNECTED Not ready to apply, but want to be part of the Webflow community? Consider following our story on our Webflow Blog https://webflow.com/blog, LinkedIn https://www.linkedin.com/company/webflow-inc-/, X (Twitter) https://twitter.com/webflow, and/or Glassdoor https://www.glassdoor.com/Reviews/Webflow-Reviews-E890506.htm.   PLEASE NOTE: We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Upon interview scheduling, instructions for accommodation requests will be administered. To join Webflow, you'll need a valid right to work authorization depending on the country of employment. If you are extended an offer, that offer may be contingent upon your successful completion of a background check, which will be conducted in accordance with applicable laws. We may obtain one or more background screening reports about you, solely for employment purposes. Webflow uses AI tools to assist in screening and evaluating applications. AI does not make final hiring decisions. For details on how we process your personal information, see Webflow’s Applicant Privacy Notice https://webflow.com/legal/applicant-privacy-notice.

Location: London Workplace: Hybrid 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 management, and cluster configuration - Python and C++ programming with focus on systems and infrastructure automation - Hands-on experience with ML frameworks such as PyTorch in distributed training contexts - Strong understanding of networking, storage, and compute resource management for ML workloads - Experience developing APIs and managing distributed systems for both batch and real-time workloads - Solid debugging and monitoring skills with expertise in observability tools for containerized environments PREFERRED SKILLS - Experience with Kubernetes operators and custom controllers for ML workloads - Advanced Slurm administration including multi-cluster federation and advanced scheduling policies - Familiarity with GPU cluster management and CUDA optimization - Experience with other ML frameworks like TensorFlow or distributed training libraries - Background in HPC environments, parallel computing, and high-performance networking - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads REQUIRED EXPERIENCE - Demonstrated experience managing large-scale Kubernetes deployments in production environments - Proven track record with Slurm cluster administration and HPC workload management - Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure - Experience supporting both long-running training jobs and high-availability inference services - Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Location: London Workplace: Hybrid 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 management, and cluster configuration - Python and C++ programming with focus on systems and infrastructure automation - Hands-on experience with ML frameworks such as PyTorch in distributed training contexts - Strong understanding of networking, storage, and compute resource management for ML workloads - Experience developing APIs and managing distributed systems for both batch and real-time workloads - Solid debugging and monitoring skills with expertise in observability tools for containerized environments PREFERRED SKILLS - Experience with Kubernetes operators and custom controllers for ML workloads - Advanced Slurm administration including multi-cluster federation and advanced scheduling policies - Familiarity with GPU cluster management and CUDA optimization - Experience with other ML frameworks like TensorFlow or distributed training libraries - Background in HPC environments, parallel computing, and high-performance networking - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads REQUIRED EXPERIENCE - Demonstrated experience managing large-scale Kubernetes deployments in production environments - Proven track record with Slurm cluster administration and HPC workload management - Previous roles in SRE, DevOps, or Platform Engineering with focus on ML infrastructure - Experience supporting both long-running training jobs and high-availability inference services - Ideally, 3-5 years of relevant experience in ML systems deployment with specific focus on cluster orchestration and resource management

Location: San Francisco; Palo Alto; New York City 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, custom operators. - Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. - Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. - Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. - Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. QUALIFICATIONS - 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. - Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). - Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). - Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).

Location: San Francisco; Palo Alto; New York City 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, custom operators. - Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. - Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. - Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. - Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. QUALIFICATIONS - 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. - Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). - Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). - Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).

Location: London 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. - Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. - Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. - Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. - Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. QUALIFICATIONS: - 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. - Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). - Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). - Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation). Final offer amounts are determined by multiple factors including experience and expertise. Equity: In addition to the base salary, equity may be part of the total compensation package.

Location: London 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. - Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism. - Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving. - Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis. - Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads. QUALIFICATIONS: - 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems. - Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow). - Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores). - Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation). Final offer amounts are determined by multiple factors including experience and expertise. Equity: In addition to the base salary, equity may be part of the total compensation package.

Location: Amsterdam, Netherlands; Germany; London, United Kingdom; Poland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We’re looking for a Senior Software Systems Engineer to join our team and play a key role in the development of our cutting-edge hyperscaler platform. The GPU & InfiniBand team is responsible for enhancing and optimizing the core components of our Cloud platform, with a specific focus on GPU computing, InfiniBand networks, and the KVM/QEMU stack. You’ll work closely with hardware virtualization and device emulation technologies, ensuring high performance and security in multi-GPU, HPC environments. The role involves analyzing, troubleshooting, and improving infrastructure to support new hardware, fine-tuning system performance, and automating fault detection and resolution in a complex system. In this position, you will be responsible for: Tuning the performance of GPU clusters and InfiniBand networks to ensure optimal operation in HPC and GPU-based environments. Analyzing and troubleshooting the root cause of issues related to GPUs and InfiniBand networks, and proposing corrective actions. Integrating new hardware into the existing infrastructure, including support for new GPU hardware through software stacks like Kubernetes, QEMU, and KVM. Enhancing automation systems for proactive monitoring, detecting, and resolving issues in GPU and InfiniBand environments. Configuring and managing GPU devices and InfiniBand fabrics, ensuring efficient and reliable operation. We expect you to have: 5+ years of professional experience in system-level software development (focused on performance optimization, low-level programming). 3+ years of hands-on experience with Linux systems (administration, troubleshooting, and performance tuning). In-depth understanding of server architecture, including PCIe devices, NICs, Linux OS/Kernel, and high-performance computing (HPC) systems. Strong proficiency in one or more performance-oriented programming languages (C/C++, Go, Python). It would be a plus if you have: Experience with GPU end-to-end testing in a cluster environment using InfiniBand networking. Proven track record of analyzing and optimizing the performance of HPC workloads (e.g., simulations, data analysis, AI/ML workloads). Familiarity with RDMA, RoCE, and InfiniBand protocols for high-performance communication. Background in Software-Defined Networking (SDN) and experience with HPC cluster networking . Understanding of QEMU/KVM virtualization and managing virtualized environments. Experience with deep learning frameworks such as PyTorch and TensorFlow , and their integration with HPC systems. Familiarity with collective communication libraries like MPI and NCCL for distributed computing. We offer competitive salaries ranging from $170k-$300k + equity based on your experience. We conduct coding interviews as part of the process. #LI-LH2 Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Global Talent is everywhere, opportunity is not. Sora Union changes that. Sora Union is a globally distributed professional services company that specializes in Design, Software Engineering, QA. Our experienced talent comes from communities where there is displacement or risk of displacement due to conflict or climate change. We provide services to global companies and start-ups across various industries, including healthcare, financial services, technology, and education. We are seeking a Forward Deployed Engineer to join our growing team, where you will act as an internal entrepreneur, driving AI initiatives from ideation to production both internally and for our customers. As a FDE, you will work closely with the  Engineering , Product/Brand Design, and QA teams. You'll be responsible for: Customer-Facing AI Solutions: Act as the primary technical partner for customers, translating operational needs into production-grade AI experiences. End-to-End Implementation: Own projects entirely, from data cleaning and prototyping to full deployment, monitoring, and debugging in customer environments. Performance Optimization : Implement model compression, quantization, and efficient serving techniques to meet strict latency and throughput requirements for customer-facing applications. Advanced Prompt Engineering & LLM Customization: Develop sophisticated prompt chaining, self-correction mechanisms, and context management strategies (e.g., using RAG architectures) to enhance LLM application performance and accuracy. Ideally, you'll have: AI/ FDE Development Experience: 3+ years of experience building and deploying production-level AI/ML models. MLOps Tooling: Hands-on experience with MLOps platforms and tools such as MLflow, Kubeflow, or Weights & Biases for experiment tracking and model registry. Technical Proficiency: Strong expertise in Python and deep learning frameworks (TensorFlow, PyTorch), along with experience in LLM orchestration frameworks (e.g., LangChain, LlamaIndex). Data Engineering/API Skills: Proven ability to build and maintain high-performance, low-latency RESTful APIs (e.g., using FastAPI or Flask) for serving models, and strong proficiency in SQL/NoSQL databases for data integration. Customer-Facing Experience: Proven ability to work directly with stakeholders to define requirements and deliver value, ideally in a consulting or specialized engineering role. Deployment Expertise: Experience containerizing models (Docker) and deploying to cloud environments (AWS, GCP, or Azure). Entrepreneurial Mindset: Ability to thrive in a fast-paced environment, taking initiative to solve ambiguous problems Sora Union is committed to creating and fostering a diverse team. We encourage people from underrepresented backgrounds and all walks of life to apply. We are committed to providing reasonable accommodations to all applicants throughout the application process.

Location: London Perplexity is looking for a Search Machine Learning Engineer Intern to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. You will work closely with experienced engineers to improve search quality, experiment with new models, and ship features that directly impact how users search and discover information. Internship program: 12 - 24 weeks, full-time, in-person in the London office. Responsibilities: - Contribute to experiments that improve search quality through better models, data usage, and evaluation tools, under the guidance of senior engineers. - Design and implement components of the search platform and model stack, including retrieval, ranking, and classification models. - Train evaluating models (including LLM-based approaches) for retrieval, ranking, and classification tasks. - Support deployment and monitoring of search and ranking models in a scalable and performant way. - Help build and iterate on RAG pipelines for grounding and answer generation. - Collaborate with Data, AI, Infrastructure and Product teams to deliver improvements quickly and learn best practices in production ML. Qualifications: - Strong foundation in machine learning and statistics, with coursework or projects related to information retrieval, ranking, or recommender systems. - Experience with Python and common ML frameworks (e.g. PyTorch, TensorFlow, JAX) through academic, open source, or personal projects. - Familiarity with evaluating model quality using offline metrics and/or A/B testing is a plus, but not required. - Previous experience (internships, research, or significant projects) working on search, recommendation, or NLP is a plus, but not required. - Self-driven and curious, with a strong sense of ownership, willingness to learn, and comfort working in a fast-paced environment - Experience with Rust will be a plus