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We are seeking a Research Engineer/Research Scientist to join our Audio team. As a member of this team, you will work across the full stack of audio ML, developing audio codecs and representations, sourcing and synthesizing high-quality audio data, training large-scale speech language models and large audio diffusion models, and developing novel architectures for incorporating continuous signals into LLMs.

\n

Our team focuses primarily but not exclusively on speech, building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities. The team works closely with many collaborators across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high-impact real-world deployments.

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Responsibilities:

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Strong candidates may also have experience with:

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Representative projects:

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XML job scraping automation by YubHub

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We're looking for research engineers with a strong computer vision background to work on research, development, and evaluation for state-of-the-art Claude models. In this role, you'll run experiments to evaluate architectural variants, data strategies, and SL and RL techniques to improve Claude's vision. You'll also develop and test tools, skills, and agentic infrastructure that enable Claude to reason over visual inputs. Additionally, you'll create evaluations and benchmarks that measure progress on multimodal capabilities across training and deployment.

\n

As a research engineer, you'll partner with the product org to ensure that the vision improvements you deliver impact Claude's performance on real-world tasks. You'll also work with our product org to find solutions to our most vexing API customer challenges related to vision and spatial reasoning.

\n

Strong candidates may also have experience with large-scale pretraining, SL, and RL on language models, deep learning research on images, video, or other modalities, developing complex agentic systems using LLMs, high-performance ML systems (GPUs, TPUs, JAX, PyTorch), and large-scale ETL and data pipeline development.

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The annual compensation range for this role is $350,000-$850,000 USD.

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XML job scraping automation by YubHub

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We're building the next-generation Grounding Service that powers the latest AI applications—chat assistants, copilots, and autonomous agents—with factual, cited, and trustworthy responses. Our platform stitches together retrieval, reasoning, and real-time data so that large language models stay anchored to enterprise knowledge, the public web, and proprietary tools.

\n

We're looking for a Senior Applied Scientist to lead end-to-end science for grounding: inventing retrieval and attribution methods, defining factuality/faithfulness metrics, and shipping production models and APIs that scale to billions of queries. You'll partner closely with engineering, product, research, and customers to deliver fast, reliable, and explainable answers with source citations across a diverse set of domains and modalities.

\n

As a team, we value curiosity, pragmatic rigor, and inclusive collaboration. We believe great systems emerge when scientists and engineers co-design metrics, models, and infrastructure—and when we obsess over customer impact, privacy, and safety.

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Responsibilities\n Owns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact.\n Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images.\n Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust.\n Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs.\n Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web.\n Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems.\n Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology.\n Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property.\n Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs.\n Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively.\n Identifies and solves significant business problems using novel, scalable, and data-driven solutions.\n Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work.\n Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review.\n Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols.\n Communicates clearly through design documentation, progress updates, and presentations to executives and customers.\n Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.

\n

Qualifications\n Required Qualifications:\n Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research)\n OR Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)\n OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)\n OR equivalent experience.\n Minimum of 4 years of hands-on experience designing and building search, retrieval, or ranking systems.\n Proven track record of shipping LLM-powered or Retrieval-Augmented Generation (RAG) systems into production environments.\n Solid coding skills and solid foundation in machine learning, with the ability to implement and optimize models effectively.\n Demonstrated ability to lead through ambiguity, make principled trade-offs, and deliver measurable impact in cross-functional, fast-paced settings.\n Preferred Qualifications:\n Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)\n OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)\n OR equivalent experience.\n Demonstrated expertise in information retrieval, with publications in top-tier conferences or journals such as NeurIPS, ICML, ICLR, SIGIR, or ACL.\n Hands-on experience in large language model (LLM) development, including pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL).\n Proven track record in optimizing LLM inference, or active contributions to open-source frameworks like vLLM, SGLang, or related projects.

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We're building the next-generation Grounding Service that powers the latest AI applications—chat assistants, copilots, and autonomous agents—with factual, cited, and trustworthy responses. Our platform stitches together retrieval, reasoning, and real-time data so that large language models stay anchored to enterprise knowledge, the public web, and proprietary tools. We're looking for a Senior Applied Scientist to lead end-to-end science for grounding: inventing retrieval and attribution methods, defining factuality/faithfulness metrics, and shipping production models and APIs that scale to billions of queries. You'll partner closely with engineering, product, research, and customers to deliver fast, reliable, and explainable answers with source citations across a diverse set of domains and modalities. As a team, we value curiosity, pragmatic rigor, and inclusive collaboration. We believe great systems emerge when scientists and engineers co-design metrics, models, and infrastructure—and when we obsess over customer impact, privacy, and safety. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction. Responsibilities

\n

Owns the science roadmap for grounding—including retrieval, re-ranking, attribution, and reasoning—driving initiatives from problem framing to production impact. Designs and evolves state-of-the-art retrieval and RAG orchestration across documents, tables, code, and images. Builds citation and provenance systems (e.g., passage highlighting, quote-level alignment, confidence scoring) to reduce hallucinations and increase user trust. Leads experimentation and evaluation using A/B testing, interleaving, NDCG, MRR, precision/recall, and calibration curves to guide measurable trade-offs. Advances tool-augmented grounding through schema-aware retrieval, function calling, knowledge graph joins, and real-time connectors to databases, cloud object stores, search indexes, and the web. Partners with platform engineering to productionize models with scalable inference, embedding services, feature stores, caching, and privacy-compliant multi-tenant systems. Nurtures collaborative relationships with product and business leaders across Microsoft, influencing strategic decisions and driving business impact through technology. Authors white papers, contributes to internal tools and services, and may publish research to generate intellectual property. Bridges the gap between researchers (e.g., Microsoft Research) and development teams, applying long-term research to solve immediate product needs. Leads high-stakes negotiations to ensure cutting-edge technologies are applied practically and effectively. Identifies and solves significant business problems using novel, scalable, and data-driven solutions. Shapes the direction of Microsoft and the broader industry through pioneering product and tooling work. Mentors applied scientists and data scientists, establishing best practices in experimentation, error analysis, and incident review. Collaborates cross-functionally with PMs, research, infrastructure, and security teams to align on milestones, SLAs, and safety protocols. Communicates clearly through design documentation, progress updates, and presentations to executives and customers. Contributes to ethics and privacy policies, identifies bias in product development, and proposes mitigation strategies.

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About Anthropic

\n

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.

\n

You may be a good fit if you:

\n\n

Strong candidates may also have experience with:

\n\n

Representative projects:

\n\n

Logistics

\n

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. 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.

\n

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.

\n

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.

\n

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.

\n

How we're different

\n

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 systems that benefit society.

\n

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About the role

\n

As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterise model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.

\n

Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organisational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.

\n

This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.

\n

Responsibilities:

\n\n

You may be a good fit if you:

\n\n

Strong candidates may also have:

\n\n

Representative projects:

\n\n

Logistics

\n

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience. 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 repsectively.

\n

Visa sponsorship: We do sponsor visas!

\n

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About the role

\n

We're looking for research engineers with a strong computer vision background who believe that visual and spatial reasoning are core to fully unlocking the capabilities of LLMs. In this role, you'll work on research, development, and evaluation for state-of-the-art Claude models, with a focus on visual and spatial capabilities.

\n

What you'll do:

\n\n\n\n\n

You may be a good fit if you:

\n\n\n\n\n\n\n\n

Strong candidates may also have experience with:

\n\n\n\n\n\n

Representative projects:

\n\n\n\n\n

Logistics

\n\n\n\n

How we're different

\n

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.

\n

The easiest way to understand our research directions is to read our recent research. This research can be found on our website.

\n

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Job Posting

\n

Researcher, Pretraining Safety

\n

Location

\n

San Francisco

\n

Employment Type

\n

Full time

\n

Department

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Safety Systems

\n

Compensation

\n\n

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

\n\n\n\n\n\n\n\n\n\n\n\n\n

More details about our benefits are available to candidates during the hiring process.

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This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

\n

About the Team

\n

The Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency.

\n

The Pretraining Safety team’s goal is to build safer, more capable base models and enable earlier, more reliable safety evaluation during training. We aim to:

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    \n
  1. Develop upstream safety evaluations that to monitor how and when unsafe behaviors and goals emerge;
    2. \n
\n
    \n
  1. Create safer priors through targeted pretraining and mid-training interventions that make downstream alignment more effective and efficient
    2. \n
\n
    \n
  1. Design safe-by-design architectures that allow for more controllability of model capabilities
    2. \n
\n

In addition, we will conduct the foundational research necessary for understanding how behaviors emerge, generalize, and can be reliably measured throughout training.

\n

About the Role

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The Pretraining Safety team is pioneering how safety is built into models before they reach post-training and deployment. In this role, you will work throughout the full stack of model development with a focus on pre-training:

\n\n\n\n\n

We collaborate across OpenAI’s safety ecosystem—from Safety Systems to Training—to ensure that safety foundations are robust, scalable, and grounded in real-world risks.

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In this role, you will:

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You might thrive in this role if you:

\n\n\n\n\n\n\n

About OpenAI

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OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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XML job scraping automation by YubHub

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Summary

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Microsoft are looking for a talented Member of Technical Staff, AI Platform Engineer at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

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About the Role

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As an AI Platform Engineer, you will play a crucial role in building and releasing production software at the platform level. You will work on APIs, data flows, systems, and services that form the backbone of our AI products. Your expertise will help us create robust and scalable platforms that drive innovation and efficiency.

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XML job scraping automation by YubHub

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Summary

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Microsoft AI are looking for a talented Member of Technical Staff - AI Pretraining - MAI Superintelligence Team at their Zürich office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

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About the Role

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As a Member of Technical Staff - AI Pretraining - MAI Superintelligence Team, you will develop algorithms, model architectures, data mixtures, and scaling laws for large-scale training using a rigorous data-driven approach grounded in meticulous ablations. You will drive algorithmic implementations, conduct experiments, and oversee flagship training runs on our in-house large-scale distributed stack. You will collaborate closely with teams on infrastructure, data, post-training, and multimodality.

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The Candidate we're looking for

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XML job scraping automation by YubHub

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Summary

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Microsoft AI are looking for a talented Member of Technical Staff - AI Pretraining - MAI Superintelligence Team at their London office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

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About the Role

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As a Member of Technical Staff - AI Pretraining - MAI Superintelligence Team, you will develop algorithms, model architectures, data mixtures, and scaling laws for large-scale training using a rigorous data-driven approach grounded in meticulous ablations. You will drive algorithmic implementations, conduct experiments, and oversee flagship training runs on our in-house large-scale distributed stack. You will collaborate closely with teams on infrastructure, data, post-training, and multimodality.

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The Candidate we're looking for

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XML job scraping automation by YubHub

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Summary

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Microsoft AI are looking for a talented Member of Technical Staff - AI Pretraining - MAI Superintelligence Team at their Zürich office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

\n

About the Role

\n

As a Member of Technical Staff - AI Pretraining - MAI Superintelligence Team, you will develop algorithms, model architectures, data mixtures, and scaling laws for large-scale training using a rigorous data-driven approach grounded in meticulous ablations. You will drive algorithmic implementations, conduct experiments, and oversee flagship training runs on our in-house large-scale distributed stack. You will collaborate closely with teams on infrastructure, data, post-training, and multimodality.

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Accountabilities

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The Candidate we're looking for

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Experience:

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Technical skills:

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XML job scraping automation by YubHub

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Summary

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Microsoft AI are looking for a talented Member of Technical Staff - AI Pretraining - MAI Superintelligence Team at their London office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI market.

\n

About the Role

\n

As a Member of Technical Staff - AI Pretraining - MAI Superintelligence Team, you will develop algorithms, model architectures, data mixtures, and scaling laws for large-scale training using a rigorous data-driven approach grounded in meticulous ablations. You will drive algorithmic implementations, conduct experiments, and oversee flagship training runs on our in-house large-scale distributed stack. You will collaborate closely with teams on infrastructure, data, post-training, and multimodality.

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Accountabilities

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The Candidate we're looking for

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Experience:

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Technical skills:

\n\n

Personal attributes:

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Benefits

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XML job scraping automation by YubHub

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