Вакансии

Location: Budapest The Wow As a Machine Learning Engineer, you will play a pivotal role in designing, developing, and deploying advanced machine learning models that enhance our products and services. You will collaborate with cross-functional teams to solve complex problems and drive the adoption of machine learning solutions across the organization. A taster of what you will be involved with: Design, implement, and optimize machine learning models to solve complex business problems. Oversee the entire machine learning lifecycle, from data collection and pre-processing to model deployment and monitoring. Work closely with data scientists, software engineers, and product managers to integrate machine learning models into production systems. Stay up-to-date with the latest advancements in machine learning and apply them to improve model performance and functionality. Write and maintain high-quality, production-ready code for machine learning models and services. Provide guidance and mentorship to junior team members, fostering a culture of continuous learning and improvement. What we are looking for: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 3-5 years of experience as a Machine Learning Engineer, with a strong track record of deploying models in production environments. Proficiency in Python, MLFlow and Spark would be considered an asset. Experience with cloud platforms (AWS, GCP, Azure). Experience with AWS Sagemaker would be preferred. Strong problem-solving skills and the ability to analyse complex data sets to derive actionable insights. Excellent communication skills, with the ability to explain complex concepts to both technical and non-technical stakeholders. What we offer Fitness-wellness allowance 🧗‍♂️ Company mobile phone for private use with 100 GB 📱 Annual HUF devaluation compensation 💡 Hybrid model: 3 days in the office 🏢 & 2 days from home 🏠 Private Health Insurance 🩺 Career development 📈 Technical and soft-skill training opportunities 🎓 Breakfast, fruits & lunch 🍎 Team building events 🥳 Challenge accepted? If you are interested to learn more, please apply with your CV in English. By submitting your application, you understand that your personal data will be processed as set out in our Privacy Policy

Location: London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA 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. Anthropic Fellows Program overview The Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent - regardless of previous experience. Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers. We run multiple cohorts of Fellows each year and review applications on a rolling basis. Apply at the bottom of this page. We are accepting applications on a rolling basis for the next cohort expected to start in January 2027. In some circumstances, we can accommodate fellows starting outside the usual cohort timelines — please note in your application if the January 2027 start date doesn't work for you. What to expect 4 months of full-time research Direct mentorship from Anthropic researchers Access to a shared workspace Connection to the broader AI safety and security research community Weekly stipend of 4,615 USD / 3,440 GBP / 6,470 CAD / 4,110 EUR + benefits (these vary by country) Funding for compute (~$15k/month) and other research expenses Interview process The interview process will include an initial application, assessments, and interviews. We may ask for references later in the process as well. 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. Compensation The expected base stipend for this role is 4,615 USD / 3,440 GBP / 6,470 CAD / 4,110 EUR per week, with an expectation of 40 hours per week for 4 months (with possible extension). ML Systems & Performance Fellows Mentors, research areas, & past projects Fellows will undergo a project selection & mentor matching process. Potential mentors include: Alwin Peng Zygi Straznickas Note: You may research mentors' prior work, but all applications must go through the official form, not the mentors. For a past example of an engineering-heavy project, see: AI agents find $4.6M in blockchain smart contract exploits Projects in this workstream may include: Building a CPU simulator for accelerator workloads Adding backends for different accelerators on an open source project Building on demand infrastructure for other infrastructure heavy fellows projects Building complex synthetic data or environment pipelines Unique candidate criteria You might be a particularly great fit for this workstream if you: Have strong software engineering skills with experience building complex ML systems Can balance research exploration with engineering rigor and operational reliability Enjoy collaborating across research and engineering disciplines Are comfortable working with large-scale distributed systems and high-performance computing (e.g. in trading) Have experience with training, fine-tuning, or evaluating large language models Are adept at analyzing and debugging model training processes You may be a good fit if you Are motivated by making sure AI is safe and beneficial for society as a whole Are excited to transition into empirical AI research and would be interested in a full-time role at Anthropic Have a strong technical background in computer science, mathematics, or physics Thrive in fast-paced, collaborative environments Can implement ideas quickly and communicate clearly Candidates must be Fluent in Python programming Available to work full-time on the Fellows program Reinforcement Learning Fellows Mentors, research areas, & past projects Fellows will undergo a project selection & mentor matching process. Potential research areas and mentors include: Ruhua Jiang Kaidi Cao Sunny Duan David Brandfonbrener Colt Steele Dino Distefano Will Williams Projects in this workstream may include: Building model-based tools to better understand AI training data and improve training data quality A research project to better understand generalization Creating RL environments to improve Claude models at capabilities that are within your domain of expertise Building RL environments for safety-related tasks Conducting research and implementing solutions in areas such as RL algorithms Unique candidate criteria You might be a particularly great fit for this workstream if you: Have strong software engineering skills with experience building complex ML systems Can balance research exploration with engineering rigor and operational reliability Enjoy collaborating across research and engineering disciplines Are comfortable working with large-scale distributed systems and high-performance computing Have experience with training, fine-tuning, or evaluating large language models Are adept at analyzing and debugging model training processes Logistics Logistics Requirements: To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program. Workspace Locations: We have designated shared workspaces in SF. We are also open to remote fellows in the UK, US, or Canada . Visa Sponsorship: We are not currently able to sponsor visas for fellows. To participate in the Fellows program, you need to have or independently obtain full-time work authorization in the UK, the US, or Canada. Program Duration: The program runs for 4 months, full-time. If you can't commit to the full duration, please still apply and note your constraints in the application. We review these requests on a case-by-case basis. Please note: We do not guarantee that we will make any full-time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit for full-time roles at Anthropic. In previous cohorts, 25-50% of fellows received a full-time offer, and we’ve supported many more to go on to do great work on AI safety and security at other organizations. The below are Anthropic's policies for full time roles. These do NOT apply to the Fellows Program. 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: 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

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

Location: Switzerland Workplace: Remote About Inworld Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads. Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production. We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA. Who We're Looking For A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood. Experience We Find Useful You don't need all of this. But you need enough to make a case. - Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM. - Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding. - High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. - Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. - Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups. - Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production. - Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems. - Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams. Who Thrives Here - You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go. - You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations. - You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem. - You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make. What Working Here Is Like We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater. Location & Employment - Location: remote within Switzerland - Employment type: Full-time, permanent employment - Hiring model: Employment via Employer of Record (EOR) Candidates must already have the legal right to work in Switzerland, as visa sponsorship is not available for this role. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements. Inworld Jobs Privacy https://inworld.ai/jobs-privacy

Location: UK Workplace: Remote About Inworld Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads. Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production. We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA. Who We're Looking For A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood. Experience We Find Useful You don't need all of this. But you need enough to make a case. - Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM. - Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding. - High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. - Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. - Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups. - Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production. - Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems. Who Thrives Here - You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go. - You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations. - You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem. - You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make. What Working Here Is Like We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater. The base salary range for this full-time position is £140,000 – £200,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future. Candidates must already have the legal right to work in the United Kingdom, as visa sponsorship is not available for this role. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements. Inworld Jobs Privacy https://inworld.ai/jobs-privacy

Location: Serbia Workplace: Remote About Inworld Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads. Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production. We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA. Who We're Looking For A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood. Experience We Find Useful You don't need all of this. But you need enough to make a case. - Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM. - Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding. - High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. - Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. - Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups. - Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production. - Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems. - Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams. Who Thrives Here - You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go. - You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations. - You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem. - You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make. What Working Here Is Like We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements. Inworld Jobs Privacy https://inworld.ai/jobs-privacy

Location: Germany Workplace: Remote About Inworld Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads. Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production. We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA. Who We're Looking For A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood. Experience We Find Useful You don't need all of this. But you need enough to make a case. - Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM. - Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding. - High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. - Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. - Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups. - Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production. - Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems. - Professional fluency in English (written and spoken) is required, as you will be collaborating daily with our US-based leadership and engineering teams. Who Thrives Here - You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go. - You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations. - You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem. - You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make. What Working Here Is Like We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements. Inworld Jobs Privacy https://inworld.ai/jobs-privacy

Location: Mountain View, California, USA Workplace: Hybrid About Inworld Inworld is a research lab and inference provider focused on realtime AI for consumer-facing applications. We build first-party speech models, serve LLMs, and run the inference behind modular APIs designed for high-volume, realtime workloads. Hundreds of millions of users interact with Inworld powered apps every day and we serve over 10 trillion LLM tokens per month. Our models and infrastructure support consumer applications across companions, healthcare, fitness, education, media, and more. Our work spans model research, realtime inference, large-scale serving infrastructure, and the APIs developers use to bring these capabilities into production. We’ve raised more than $125M from Lightspeed Venture Partners, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta, Stanford, and others. Our technology has powered experiences from companies including NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella, and Bible Chat. Inworld has also been recognized by CB Insights as one of the 100 most promising AI companies globally and named one of LinkedIn’s Top 10 Startups in the USA. Who We're Looking For A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood. Experience We Find Useful You don't need all of this. But you need enough to make a case. - Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM. - Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding. - High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. - Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. - Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups. - Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production. - Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems. Who Thrives Here - You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go. - You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations. - You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem. - You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make. What Working Here Is Like We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater. We believe in the power of in-person collaboration to solve the hardest problems and foster a strong team culture. We offer relocation assistance and look forward to you joining us in our Mountain View office. The base salary range for this full-time position is $270,000 - $430,000+ bonus + equity + benefits. Inworld Jobs Privacy https://inworld.ai/jobs-privacy

Location: Strava SF Workplace: Hybrid ABOUT STRAVA Strava is the app for active people. With over 200 million athletes in more than 185 countries, it’s more than tracking workouts—it’s where people make progress together, from new habits to new personal bests. No matter your sport or how you track it, Strava’s got you covered. Find your crew, crush your goals, and make every effort count. Start your journey https://www.strava.com/subscription with Strava today. Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward. We are looking for a Machine Learning Engineer to join the growing AI and Machine Learning team at Strava. This team is responsible for sophisticated machine learning models and systems which provide value to Strava athletes including personalization, recommendations, search, and trust and safety. The team also maintains the ML platform and infrastructure that enables our team to iterate on models quickly and deploy them reliably at scale. This is an important role in the ML team and across Product teams designing, roadmapping, and implementing innovative machine learning algorithms. We value full stack ML engineers who are able to work on all parts of an ML pipeline from model building, evaluation, optimizing performance, and ensuring the scalability and reliability of these production models. We also seek those who can improve the systems and tools behind the ML pipeline to further empower the team. We follow a flexible hybrid model that translates to more than half your time on-site in our San Francisco Office — three days per week. WHAT YOU’LL DO: - Build for a Well Loved Consumer Product: Work at the intersection of AI and fitness to launch and optimize product experiences that will be used by tens of millions of active people worldwide - Own End to End AI Systems: Drive key projects powered by ML on the Strava platform end-to-end, from initial model prototyping to shipping production code to scaling and optimizing inference and deployment - Shape AI at Strava: Be a strong voice on a highly collaborative team with a range of experience levels. Work across teams to deploy ML solutions in multiple surfaces and build out our technical ML capabilities. - Innovate in AI for Fitness: Design and develop novel models and methodologies to take on novel problems that improve athlete experience, including recommendation systems, activity prediction, and personalized insights. - Build from a rich dataset: Explore and use Strava’s extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features YOU WILL BE SUCCESSFUL HERE BY: - Driving innovation with Product in mind: Stay up-to-date with the latest research in machine learning, AI, and related fields. Experiment, advocate and get buy-in for innovative techniques to improve existing products or explore new features that result in step function changes to how we build AI at Strava. - Leading as an Owner: Owning your work end-to-end and being accountable for the outcomes in the projects you drive and landing impact for the business. Ensure the end-to-end system delivers as expected through collaboration with partners. - Analyzing the Data: Work closely with product managers, data scientists, and engineers to find opportunities for applying machine learning to drive business impact and enhance Strava’s features and measure impact. - Collaborating in and across teams: Build relationships, advocate, and communicate with cross-functional partners and product verticals to identify opportunities and bring your technical vision to life. - Raising the ML standard: Help work towards best practices for model development, deployment, and maintenance. - Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values WHAT YOU’LL BRING TO THE TEAM: - Have worked on numerous machine learning problems and broken them down into incremental tasks. - Have demonstrated solid interpersonal and communication skills, and collaborative approach to drive business impact across teams. - Have experience building, shipping, and supporting ML models in production at scale. - Have experience with exploratory data analysis and model prototyping, using languages such as Python or R and tools like Scikit learn, Pandas, Numpy, Pytorch, Tensorflow, and Sagemaker. - Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake). - Are experienced and interested in production ML model operational excellence and best practices, like automated model retraining, performance monitoring, feature logging, and A/B testing. - Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go. For information on benefits, please click here. https://docs.google.com/document/d/e/2PACX-1vTkFMPz3eCjd0FgQ28VWnK4d2BEnzGyaSFGwATR-dQdBb9HxZgay6kboqnT0-V-qaBDzSvYIzI76vT5/pub WHY JOIN US? Movement brings us together. At Strava, we’re building the world’s largest community of active people, helping them stay motivated and achieve their goals. Our global team is passionate about making movement fun, meaningful, and accessible to everyone. Whether you’re shaping the technology, growing our community, or driving innovation, your work at Strava makes an impact. When you join Strava, you’re not just joining a company—you’re joining a movement. If you’re ready to bring your energy, ideas, and drive, let’s build something incredible together. Strava builds software that makes the best part of our athletes’ days even better. Just as we’re deeply committed to unlocking their potential, we’re dedicated to providing a world-class, inclusive workplace where our employees can grow and thrive, too. We’re backed by Sequoia Capital, TCV, Madrone Partners and Jackson Square Ventures, and we’re expanding in order to exceed the needs of our growing community of global athletes. Our culture reflects our community. We are continuously striving to hire and engage teammates from all backgrounds, experiences and perspectives because we know we are a stronger team together. Strava is an equal opportunity employer. In keeping with the values of Strava, we make all employment decisions including hiring, evaluation, termination, promotional and training opportunities, without regard to race, religion, color, sex, age, national origin, ancestry, sexual orientation, physical handicap, mental disability, medical condition, disability, gender or identity or expression, pregnancy or pregnancy-related condition, marital status, height and/or weight. 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. Please contact us to request accommodation. California Consumer Protection Act Applicant Notice https://docs.google.com/document/d/e/2PACX-1vSc8y6On4NlP5s12hf4kmWe_pvenVyQ78TbtVaz_clROhj9ADQ6kY4YScglBIuY2joKFKalOReX7vP_/pub

Location: Ульяновск Мы ищем редактора (AI-тренера), который станет наставником для GigaChat. Если ты любишь работать с текстами, видишь красоту в деталях и мечтаешь влиять на технологии будущего — это твой шанс. Обязанности * создавать и редактировать тексты на самые разные темы * анализировать ответы GigaChat, находить ошибки и улучшать логику его мышления * разрабатывать сценарии диалогов и тестовые задания * искать оригинальные источники, проверять факты и доводить текст до совершенства * тестировать новые версии модели и давать фидбэк разработчикам. Требования * получил высшее лингвистическое, журналистское или переводоведческое образование * имеешь опыт работы с текстами - копирайтер, технический писатель или любые подобные должности * обожаешь детали: твоя сверхспособность — находить нюансы даже в идеальных предложениях * легко ориентируешься в больших объёмах информации и быстро погружаешься в новые темы. Будет круто, если у тебя есть: * опыт работы с LLM (ChatGPT, GigaChat, DeepSeek и пр.) * навыки написания промтов * понимание технической терминологии (JSON, Markdown) * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия * работа в удалённом формате для Ульяновска и Ульяновской области * ежегодный пересмотр зарплаты и годовая премия * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера..

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Who We Are: The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams. What You’ll Do: As a Senior Staff Software Engineer, you will help define and lead the vision for Reddit’s large-scale GenAI Platform, shaping the strategy, architecture, and operating model that enable teams across the company to build, deploy, and scale generative AI products with confidence. Contribute to the design, implementation, and maintenance of the LLM Gateway, focusing on features like unified API endpoints for internal/externally hosted LLM, rate/token limit management, and intelligent failover mechanisms to boost uptime and reliability. Lead and execute the vision, strategy, and roadmap for Reddit’s large-scale GenAI Platform. Define the platform architecture and operating model that enable teams to build, deploy, and scale GenAI products reliably. Drive the strategy for a unified LAG Gateway supporting internally and externally hosted LLMs through consistent APIs and abstractions. Set the direction for core platform capabilities such as rate and token limit management, intelligent failover, and production resilience. Shape Reddit’s approach to an enterprise-grade RAG system Establish the strategic direction for agentic AI workflows and tool-use patterns across the platform. Own the end-to-end platform strategy from concept through production adoption and long-term evolution. Drive MLOps and LLMOps standards across CI/CD, testing, versioning, evaluation, and lifecycle management. Define best practices for observability, monitoring, governance, and operational excellence across GenAI systems. Partner across engineering, product, and leadership to align platform investments with company priorities and user needs. Champion platform thinking with a strong focus on scalability, reliability, performance, and developer experience. Influence technical direction across teams by turning emerging AI capabilities into a scalable platform strategy. Who You Might Be: 10+ years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment roles. Have a track record of leading technical strategy and delivering AI platforms in cloud-based production environments at scale. Demonstrate strong execution by turning strategy into action, driving complex initiatives end to end, and consistently delivering high-quality platform outcomes. Bring deep experience operating Kubernetes and other orchestration systems in large-scale production environments. Deep experience with cloud-based technologies for supporting an ML platform, including tools like AWS, Google Cloud Storage, infrastructure-as-code (Terraform), and more Proficiency with the common programming languages and frameworks of ML, such as Go, Python, etc. Excellent communication skills with the ability to articulate technical AI concepts to non-technical stakeholders Strong focus on scalability, reliability, performance, and developer experience. You are an undying advocate for platform users and have a deep intuition for the genAI product development lifecycle. Strong knowledge of model serving, inference pipelines, monitoring, and observability for AI systems is a plus Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $292,500 — $409,500 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: Malta The Wow As a Machine Learning Engineer, you will play a pivotal role in designing, developing, and deploying advanced machine learning models that enhance our products and services. You will collaborate with cross-functional teams to solve complex problems and drive the adoption of machine learning solutions across the organization. A taster of what you will be involved with: Design, implement, and optimize machine learning models to solve complex business problems. Oversee the entire machine learning lifecycle, from data collection and pre-processing to model deployment and monitoring. Work closely with data scientists, software engineers, and product managers to integrate machine learning models into production systems. Stay up-to-date with the latest advancements in machine learning and apply them to improve model performance and functionality. Write and maintain high-quality, production-ready code for machine learning models and services. Provide guidance and mentorship to junior team members, fostering a culture of continuous learning and improvement. What we are looking for: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 3-5 years of experience as a Machine Learning Engineer, with a strong track record of deploying models in production environments. Proficiency in Python, MLFlow and Spark would be considered an asset. Experience with cloud platforms (AWS, GCP, Azure). Experience with AWS Sagemaker would be preferred. Strong problem-solving skills and the ability to analyse complex data sets to derive actionable insights. Excellent communication skills, with the ability to explain complex concepts to both technical and non-technical stakeholders. What we offer Much like riding a rollercoaster, sometimes life at Betsson can be lightning fast with twists and turns but always FUN! Then again, what else would you expect from a business 75% millennial and 1700 strong, spread across 7 offices with 900 based out of our Malta HQ alone! We recognise it may not be for the faint-hearted, but if you’re a go-getter, initiator and adrenaline junkie, always striving to push the boundaries and challenge yourself, then you’ll fit right in. Challenge accepted? By submitting your application, you understand that your personal data will be processed as set out in our Privacy Policy

Задачи: — Конфигурировать и разворачивать инфраструктурные компоненты — Адаптировать/разрабатывать с нуля автоматизированные пайплайны под актуальные бизнес-задачи (AutoML, LLM, computer vision и пр) — Создавать и поддерживать пользовательские инструменты для работы в едином ML-контуре применения и обучения — Масштабировать разрабатываемые системы и инструменты для управления жизненным циклом моделей машинного обучения Требования: — Опыт DevOps от 3х лет (Docker, Helm, Jenkins/GitLab CI, Python) — Опыт ML/MLOps от 1 года (Airflow, JupyterHub, MLflow, Seldon, CUDA) — Опыт администрирования Kubernetes от 2х лет — Опыт работы с Hadoop, Spark, Kafka, ELK Условия: — Работа в аккредитованной IT компании, имеющей собственные продукты — Формат работы: удаленный по РФ — Предоставляем расширенный ДМС (со стоматологией) — Выдаем необходимую для работы технику (ноутбук) — Возможность воспользоваться реферальной программой: “Приведи товарища” — Участие в Бонусной программе — Помогаем в ипотеке и других пособиях — Предоставляем возможность построить карьеру в рамках ведущих компаний России

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning. We are hiring Machine Learning Engineers across our Consumer Engineering organization, giving you the opportunity to work on a wide range of high-impact problems across the Consumer ecosystem. We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, and who want to help shape the future of discovery, relevance, and monetization at Reddit. If you love working on complex, real-world ML problems at massive scale, this role is for you. What You’ll Work On We are looking for a Staff Machine Learning Engineer to help drive the next generation of Reddit’s ML ecosystem across recommendations, search, messaging, and foundational AI systems. You will lead high-impact initiatives from ideation to production, shaping both technical strategy and product direction across multiple ML domains. This is a highly cross-functional role partnering with Product, Data Science, and Engineering to deliver meaningful user and business impact. This role sits at the intersection of: Relevance & recommendation systems (content, search, notifications) AI-powered discovery & LLM-driven experiences Content and user understanding & large-scale representation learning Large-scale ML infrastructure and pipelines What You’ll Do Lead end-to-end ML initiatives from ideation through production and iteration, shaping technical direction and translating product goals into scalable solutions Architect, build and deploy large-scale ML systems across recommendation, search, and content/user understanding, including retrieval/ranking models, representation learnings embeddings optimizations, and LLM or GenAI-powered capabilities Drive measurable impact on user engagement, discovery, and long-term value Collaborate with cross-functional teams to align product and technical roadmaps and unlock key future ML capabilities Stay at the forefront of AI research, evaluating and introducing new AI/ML paradigms to keep Reddit’s ML ecosystem at the cutting edge Contribute to the development of best practices, guidelines, and ethical AI principles for responsible LLM development and deployment Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing Set technical vision and drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making Required Qualifications 7+ years of experience building, deploying, and operating machine learning systems in production Deep understanding of machine learning methods, spanning classical approaches and modern deep learning (e.g., Transformers, GNN, etc) Expert at developing and productionizing models using TensorFlow, PyTorch, or Hugging Face Transformers Experience building production-quality code incorporating testing, evaluation, and monitoring using object-oriented programming, including experience in Python and Golang Experience designing and scaling ML systems, including data pipelines, feature engineering, model training/serving, and production monitoring Excellent communication and collaboration skills, with the ability to discuss complex technical topics with diverse teams and translating product needs into scalable ML solutions Track record of driving measurable impact through applied machine learning in real-world products Preferred Qualifications Subject matter expertise in one of the following domains: Recommender systems Search systems (lexical and semantic retrieval and ranking) Content understanding (NLU/NLP/LLM, topic/taxonomy modeling, interest graphs or clustering, and multimodal understanding) Familiarity with distributed systems and large-scale data processing frameworks (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.) Experience working with real-time systems and low-latency production environments Experience with LLM/GenAI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale Strong experimentation rigor, with experience formulating clear hypotheses, designing actionable learning plans and building offline/online correlations Advanced degree in Computer Science, Machine Learning, or related quantitative field Potential Teams Home Experience ML Understanding Feed Relevance Answer Experience Search and Answers Relevance Search Experience Benefits Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-Remote Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $230,000 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: San Francisco 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. We are looking for an engineer with strong experience in machine learning and solid foundations in math and computer science to join our growing Post-Training team at Baseten. THE ROLE Custom models are instrumental to the success of Baseten customers. By inference volume, the overwhelming majority of traffic at Baseten is to and from models that have been post-trained in some way, whether that be through reinforcement learning, supervised finetuning, a recent technique from the literature, or an in-house research technique from Baseten. The Post-Training team is responsible for the success of our customers’ post-trained models, and we employ a wide array of techniques to produce models that are more efficient and higher quality than even the biggest closed source models for the customer’s specific needs. Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often it involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels. The Manifesto: https://labs.baseten.co/manifesto RECENT RESEARCH - Dense, on-policy or both? https://www.baseten.co/research/dense-on-policy-or-both/ - Repeated kv cache for long-running agents https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/ - Distillation without the dark – replicating black-box on-policy distillation on Baseten https://www.baseten.co/research/distillation-without-the-dark/ We don’t have a rigid set of skills, but here’s some of what we’re looking for: - A deep understanding of modern ML techniques and tools for training transformers - Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar - A detailed understanding of transformer training parallelism strategies like data parallelism, sharded data parallelism, tensor parallelism, pipeline parallelism, context parallelism - The experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library - The ability to perform roofline analysis on a transformer training setup - A willingness to dive into messy problems, work with researchers, derive specifications by asking important questions, and execute - Familiarity with HPC and distributed computing platforms like Slurm, Ray, Kubernetes, and Dask - Familiarity with cluster networking technology like Infiniband, RoCE, GPUDirect - Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerization, and networking protocols - A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices 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: Brazil About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . *This is a remote role and can be performed anywhere in Brazil/Colombia* The Role We are looking for a Research Engineer to help deliver frontier-quality datasets, RL environments, and evaluations that improve state-of-the-art models for a frontier AI lab client. You will work directly with the client's researchers and engineers, turning inbound requests and post-training goals into concrete technical proposals and data/environment specifications, and then owning the technical execution: building the quality and verification systems that ensure what we deliver meets extremely high standards for correctness, realism, diversity, difficulty, and measurable model lift. Most of this work is bespoke and custom, built for one client's evolving needs.You'll typically stay attached to a single frontier lab client so you can build real context and a working relationship with their team, with flexibility to support other accounts when needed. Because bespoke work is judged on both turnaround time and quality, both matter equally here. This role is designed for candidates with experience building and improving deep learning systems, especially where strong results depend on data quality, data curation, denoising, synthetic data generation, and rigorous evaluation. You'll operate in one or more of the following capability areas: Coding and software engineering agents (repositories, unit tests, debugging, tool use, code reviews, long-horizon workflows) RL environments and verifier-based training (tasks, rewards/verifiers, trajectories, evaluation harnesses) Multimodal data and reasoning (text + images + documents + tables/charts; optional audio/video) STEM reasoning (math, physics, chemistry, bio, engineering – solution verification and error analysis) Modern embodied AI / VLM-driven agents (vision-language(-action) models, embodied task suites, tool/sensor/action abstractions, long-horizon interaction data) What You'll Do 1) Own data and environment quality from an AI researcher perspective Respond to inbound requests from the client and translate ambiguous, evolving research goals into a technical proposal and clear data requirements: target skills, failure modes, difficulty calibration, coverage, and success metrics. Provide the technical feasibility assessment, assumptions, acceptance criteria, and evaluation requirements needed to scope the contract or statement of work. Define what “good” looks like by creating detailed rubrics, counterexamples, and boundary cases (what to include vs. exclude). Perform deep, detail-oriented audits of produced data: spot subtle errors, reward hacking opportunities, leakage, ambiguity, inconsistent assumptions, and distribution shifts. Drive iterative improvements using evidence: group failures (for example, by semantic similarity) to find the underlying gap, then turn that into concrete instructions for the production team — better few-shot examples, explicit counterexamples, clearer guidance on what's causing rejections. 2) Design and build datasets and RL environments for your capability area(s) Contribute to or lead the design of: Task suites (single-step and long-horizon workflows) Ground-truth signals (verifiers, unit tests, structured checks, reward functions, automatic validators) Environment interfaces (APIs, tool schemas, state abstractions, database schemas, simulator-like dynamics) Depending on your mapped capability area(s), you may focus on: Coding / SWE agents: data reflecting real development work (codebase navigation, bug localization, patching, tests, code reviews, CI-like constraints, refactors, security fixes). Multimodality: tasks that test true multimodal reasoning (chart reading, document QA, UI understanding, diagram-based STEM reasoning, OCR-aware tasks). STEM: tasks with verifiable solutions (symbolic checks, reference solvers, numerical validation, step consistency, unit sanity). Modern embodied AI / VLM-driven agents: interaction data and environments for vision-language(-action) models. 3) Build robust validation, denoising, and synthetic data systems Build project-specific verification systems that combine deterministic checks, model-based evaluators calibrated against gold sets, and targeted human review. Implement automated validation and filtering to achieve frontier-grade signal-to-noise: deduplication, decontamination, leakage checks, consistency checks, difficulty and diversity controls. Develop synthetic data generation and augmentation pipelines where appropriate: programmatic task generators, controlled perturbations, scenario templating, simulator-/tool-driven rollouts. Create documentation and data cards: dataset intent, known limitations, recommended use, and evaluation linkage. 4) Use evaluations and training runs to prove impact Design and run evals that reflect the client's intended usage. Produce analysis that connects data to outcomes: pre/post comparisons, error breakdowns, ablations that identify which data attributes drive lift. When needed, run in-house fine-tuning or RL-style experiments (or partner with research) to demonstrate that the data/environment improves model behavior in measurable ways. 5) Collaborate effectively with large production teams without being ops-heavy Give technical direction to the distributed expert network producing the data, by providing clear specs, examples, edge cases, and fast feedback loops based on audits and quantitative signals. You own technical translation, acceptance criteria, evaluator and verifier design, quality diagnosis, and the technical release recommendation. The Strategic Project Lead owns production execution, staffing, throughput, project cost, and recovery. You are expected to be highly engaged in reviewing and improving outputs from large annotation/creation efforts, but not primarily responsible for hiring, staffing, or people operations — that sits with the SPL. Communicate directly with the client on technical findings, quality risks, and recovery options. Align changes to scope, schedule, or external commitments with the account and delivery owners. Who We're Looking For 4–5 years of experience building or improving deep learning systems where data quality mattered materially (training, post-training, evals, or agentic systems). Strong intuition for the “data ingredients” that drive model improvements: what to collect, what to filter, what to synthesize, and how to measure. Ability to communicate clearly with researchers and engineers: turning research objectives into concrete specs, and turning messy outputs into actionable insights. Comfortable being the direct technical point of contact for a client, including explaining setbacks or trade-offs when things don't go as planned. Demonstrated ability to be extremely detail-oriented in diagnosing subtle data quality issues and failure modes. Solid programming ability with a bias for shipping: Python proficiency required; comfort with SQL/structured data workflows strongly preferred; for coding-focused work, proficiency in a major language (e.g., C++, Java, Go, Rust, JS/TS) is a plus. Comfort designing quality systems: rubrics, validation scripts, gold sets, sampling strategies, statistical checks, slice-based evaluation, human-in-the-loop review loops grounded in measurable criteria. Direct experience with a frontier AI lab is not required, and is uncommon in the LATAM market — what matters most is hands-on engineering depth and the ability to operate with ambiguity. Strong pluses RL or post-training experience (any of: RLHF/RLAIF, verifier training, reward modeling, RL fine-tuning, environment design). Experience with agentic evaluation (tool use, multi-step workflows, long-horizon tasks, trajectory analysis). Multimodal expertise (document understanding, charts, diagrams, OCR, UI/vision grounding; audio/video optional). STEM depth (math/physics/engineering) with an eye for verifiability and rigorous correctness. Modern embodied AI / VLM-driven agent experience (vision-language(-action) models, interaction datasets, embodied evals, long-horizon grounding, tool/sensor/action interfaces). Systems thinking: ability to “simulate” an application's API/data schema and design tasks that realistically reflect real-world constraints and workflows. Why Turing Work directly with a frontier AI lab, at the cutting edge of post-training and RL environment design, embedded in a real research relationship rather than a rotating set of anonymous tasks. Real impact (path to AGI): your datasets and environments will directly influence the trajectory toward Artificial General Intelligence and, ultimately, Superintelligence. High autonomy and fast iteration, with a Strategic Project Lead handling scaling and resourcing so you can stay focused on the technical approach and quality bar. Talent-dense team, where you'll find rapid iteration and an exceptional learning curve. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .