As a hands-on contributor to the core systems that ensure the safety, reliability, and continuous improvement of LLM-powered workflows and agents for the enterprise, you will partner with Scale's Operations team and enterprise customers to translate ambiguity into structured evaluation data. This involves guiding the creation and maintenance of gold-standard human-rated datasets and expert rubrics that anchor AI evaluation systems.

Your responsibilities will also include analysing feedback and collected data to identify patterns, refine evaluation frameworks, and establish iterative improvement loops that enhance the quality and relevance of human-curated assessments. You will design, research, and develop LLM-as-a-Judge autorater frameworks and AI-assisted evaluation systems, including creating models that critique, grade, and explain agent outputs.

To succeed in this role, you will need a strong foundational knowledge of large language models, a passion for tackling complex evaluation challenges, and the ability to thrive in a dynamic, fast-paced research environment. You should be able to think outside the box, stay current with the latest literature in AI evaluation, and be passionate about integrating novel research ideas into our workflows to build best-in-class evaluation systems.

In addition to your technical expertise, you will need excellent communication and collaboration skills, as you will work closely with cross-functional teams to drive project success.

If you are a motivated and detail-oriented individual with a passion for AI research and evaluation, we encourage you to apply for this exciting opportunity.

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Key Responsibilities:

• Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.
• Design and experiment new approaches to preference optimization.
• Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.
• Publish research findings in top-tier AI conferences.

Ideal Candidate:

• Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.
• Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.
• Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer-facing role.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

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We're seeking a Tech Lead Manager to lead a talented team of research scientists and research engineers focused on developing and implementing novel evaluation methodologies, metrics, and benchmarks to assess the capabilities and limitations of our cutting-edge LLMs.

Key responsibilities:

• Lead a team of highly effective research scientists and research engineers on LLM evals.
• Conduct research on the effectiveness and limitations of existing LLM evaluation techniques.
• Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness.
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols.
• Implement scalable and reproducible evaluation pipelines using modern ML frameworks.
• Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives.

Ideal candidate has 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development. Experience supporting and leading a team of research scientists and research engineers is also required.

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This role focuses on pushing the frontier of reward modeling for large language models. You'll develop novel architectures and training methodologies for RLHF, research new approaches to LLM-based evaluation and grading (including rubric-based methods), and investigate techniques to identify and mitigate reward hacking. You'll collaborate closely with teams across Anthropic, including Finetuning, Alignment Science, and our broader research organization, to ensure your work translates into concrete improvements in both model capabilities and safety.

We're looking for someone who can drive ambitious research agendas while also shipping practical improvements to production systems. You'll have the opportunity to work on some of the most important open problems in AI alignment, with access to frontier models and significant computational resources. Your work will directly advance the science of how we train AI systems to be both highly capable and safe.

Responsibilities:

• Lead research on novel reward model architectures and training approaches for RLHF
• Develop and evaluate LLM-based grading and evaluation methods, including rubric-driven approaches that improve consistency and interpretability
• Research techniques to detect, characterize, and mitigate reward hacking and specification gaming
• Design experiments to understand reward model generalization, robustness, and failure modes
• Collaborate with the Finetuning team to translate research insights into improvements for production training pipelines
• Contribute to research publications, blog posts, and internal documentation
• Mentor other researchers and help build institutional knowledge around reward modeling

You may be a good fit if you

• Have a track record of research contributions in reward modeling, RLHF, or closely related areas of machine learning
• Have experience training and evaluating reward models for large language models
• Are comfortable designing and running large-scale experiments with significant computational resources
• Can work effectively across research and engineering, iterating quickly while maintaining scientific rigor
• Enjoy collaborative research and can communicate complex ideas clearly to diverse audiences
• Care deeply about building AI systems that are both highly capable and safe

Strong candidates may also

• Have published research on reward modeling, preference learning, or RLHF
• Have experience with LLM-as-judge approaches, including calibration and reliability challenges
• Have worked on reward hacking, specification gaming, or related robustness problems
• Have experience with constitutional AI, debate, or other scalable oversight approaches
• Have contributed to production ML systems at scale
• Have familiarity with interpretability techniques as applied to understanding reward model behavior

The annual compensation range for this role is $350,000-$500,000 USD.

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For example, you might:

• Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them;
• Design and build harnesses to test AI agents' tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment;
• Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use;
• Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents.

Ideally you'd have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance;
• Practical experience conducting technical research collaboratively;
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches;
• A track record of published research in machine learning, particularly in generative AI;
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development;
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools;
• Experience with red-teaming, prompt injection, or adversarial testing of AI systems.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organisational culture. We will not ask any LeetCode-style questions. If you're excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn't perfectly align with every requirement.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

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You will make Grok extraordinarily adept at reading emotional subtext, social context, and user intent, then responding with perfect calibration.

Responsibilities

• Detect and interpret implied emotional states, attachment styles, and unspoken needs.
• Teach models appropriate mirroring, boundary-setting, defusing, challenging, or comforting.
• Detect and correct emotionally tone-deaf responses in AI-generated outputs.
• Build datasets exposing dark-pattern social tactics (concern-trolling, gaslighting, etc.) so Grok remains immune.
• Ensure cross-cultural sensitivity without performative overcorrection.

Basic Qualifications

• Direct clinical experience with diverse populations (trauma, PTSD, personality disorders, neurodivergence, etc.).
• Expertise in attachment theory, polyvagal theory, mentalization, or equivalent frameworks.
• Demonstrated ability to read subtext and calibrate responses in high-stakes therapeutic or educational settings.
• Familiarity with DSM-5 diagnostics and evidence-based interventions (CBT, DBT, trauma-focused modalities).
• Strong ethical grounding and understanding of scope-of-practice boundaries.

Preferred Skills and Experience

• Active or past clinical licensure.
• Experience supervising clinicians or teaching at the postgraduate level.
• Published research or clinical writing on trauma, emotional regulation, or social cognition.
• Public policy or advocacy experience around mental health.

Location and Other Expectations

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit.
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required. On average most projects may involve at least 10 hours per week to achieve deliverables effectively though this is not a fixed commitment and depends on the scope of work. Contractors have full flexibility to set their own hours and determine the exact amount of time needed to complete deliverables.
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs.
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time.
• We are unable to provide visa sponsorship.
• For those who will be working from a personal device, your computer must be a Chromebook, Mac with MacOS 11.0 or later, or Windows 10 or later.

Compensation and Benefits

US based candidates: $45/hour - $70/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates: Information will be provided to you during the recruitment process.

Benefits vary based on employment type, location and jurisdiction. Benefits for eligible U.S. based positions include health insurance, 401(k) plan, and paid sick leave. Specific details and role specific information will be provided to you during the interview process.

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This role offers a unique opportunity to shape how AI systems handle sensitive chemical and explosives information. You'll work with leading AI safety researchers while tackling critical problems in preventing catastrophic misuse. If you're excited about using your expertise to ensure AI systems remain safe and beneficial, we want to hear from you.

Responsibilities:

• Design and implement evaluation methodologies for assessing AI model capabilities relevant to chemical weapons, explosives synthesis, and energetic materials

• Develop and execute strategies to identify and mitigate potential C/E misuse in model outputs

• Create C/E threat models, including precursor identification, synthesis routes, and weaponization techniques

• Review and analyze traffic to identify potential policy violations related to C/E content

• Collaborate with software engineers to develop and refine detection systems and automated enforcement tools for C/E threats

• Conduct rapid response to escalations involving dangerous C/E queries

• Collaborate across teams to establish safety benchmarks and develop appropriate model guardrails

• Translate C/E domain knowledge into actionable safety requirements

• Develop approaches to assess C/E model knowledge boundaries for dual-use chemical information

• Monitor emerging threats in the C/E landscape to inform policy development

You may be a good fit if you:

• Have a Ph.D. in Chemistry, Chemical Engineering, or a related field with focus on energetic materials, explosives, and/or chemical weapons

• Have 5-8+ years of experience in chemical weapons and/or explosives defense, with deep expertise in energetic materials, chemical weapon agents, or related areas

• Have knowledge of high yield explosives application to radiological dispersal devices (dirty bombs) and related radiological weapons

• Have a track record of translating specialized technical knowledge into actionable safety policies or guidelines

• Are comfortable navigating ambiguity and developing solutions for novel safety challenges

• Can work independently while maintaining strong collaboration with cross-functional teams including engineering, enforcement, and research

• Thrive in a fast-paced environment where you balance rigorous scientific standards with rapid threat response

• Are passionate about preventing misuse of dangerous technical knowledge while enabling beneficial applications

Strong candidates may have:

• Experience with both chemical weapons and high yield explosives defense

• Experience working with defense, intelligence, or nonproliferation organizations (e.g., OPCW, IAEA, national labs, defense contractors)

• Published research or practical experience in explosives characterization, chemical weapons detection, or related security applications

• Knowledge of international chemical weapons conventions (CWC) and controlled substances regulations

• Demonstrated ability to communicate complex technical concepts to non-specialist audiences

• Experience with chemical databases (PubChem, Reaxys, SciFinder) and computational chemistry tools

• Understanding of radiological materials and their interaction with explosive dispersal mechanisms

• Familiarity with dual-use C/E research concerns and responsible disclosure practices

The annual compensation range for this role is $245,000-$285,000 USD.

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We are seeking an Applied AI, AI Engineer to join our customer-facing technical organization. As a member of our team, you will work directly with enterprise clients from pre-sales through implementation to deploy cutting-edge AI solutions that deliver measurable business impact.

Your primary responsibility will be to identify high-value internal use cases across engineering, legal, HR, sales, and operations, and build or vibe code end-to-end LLM applications. You will own the full lifecycle of these applications, from prototype to production, maintenance, and iteration.

In addition to your technical skills, you will also be responsible for documenting learnings and sharing insights with product and research teams, and converting successful internal tools into customer demos or case studies where appropriate.

How We Work in Applied AI

We care about people and outputs. What matters is what you ship, not the time you spend on it. Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week. Always ask why. The best solutions come from deep understanding, not from copying what worked before. We say what we mean. Feedback is direct, timely, and given because we care. No politics. Low ego, high standards. We embrace an unstructured environment and find joy in it.

About You

You are fluent in English and have 3+ years of experience building production software, with meaningful experience deploying LLM applications. You have a bias toward shipping, preferring a working prototype over a perfect specification. You possess strong technical coding skills in Python and front-end skills with React Frameworks. You are comfortable working autonomously across teams with different needs and constraints, and have strong communication skills to bridge non-technical teams and AI capabilities.

Ideally, you have contributions to open-source evaluation frameworks or published research on LLM evaluation, experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Technical Product Manager, and experience with ML frameworks (PyTorch, HuggingFace Transformers).

Benefits

PTO: The CDI contract will be a 'Forfait 218 jours', corresponding to 25 days of holidays and on average 8 to 10 days of RTT days, and complete autonomy on working hours.

Health: Full health insurance coverage for you and your family.

Transportation: We offer a €600 annual mobility allowance, covering 50% of your public transportation costs and including the Sustainable Mobility Allowance (FMD), encouraging eco-friendly travel options such as cycling or carpooling.

Food: Swile meal vouchers with 10,83€ per worked day, including 60% offered by the company.

Sport: Gymlib - sponsorship by Mistral of a significant part of the monthly fee (depending on the program you chose).

Parental policy: 4 additional weeks for parents on top of what is offered by the French state.

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Member of Technical Staff - Image / Video Generation

Job Description

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX,foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video,tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.

Why This Role

You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes,it's about running the experiments that clarify which architectural choices matter and which are less impactful.

What You’ll Work On

• Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters
• Rigorously ablates design choices,running experiments that isolate variables, control for confounds, and produce insights you can actually trust,then communicating those results to shape our research direction
• Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously
• Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough

What We’re Looking For

• You've trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You're comfortable debugging distributed training issues and presenting research findings to the team.

Required Skills

• Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training
• Experience fine-tuning diffusion models for specialized applications,upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture
• Deep understanding of how to effectively evaluate image and video generative models,knowing which metrics correlate with quality and which are just convenient proxies
• Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning
• Solid understanding of distributed training techniques,FSDP, low precision training, model parallelism,because our models don't fit on one GPU and training decisions impact research outcomes

Preferred Skills

• Experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors
• Proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers
• Know the performance characteristics of different architectural choices at scale
• Have published research that contributed to how people think about generative models

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

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This role offers a unique opportunity to shape how AI systems handle sensitive chemical and explosives information. You'll work with leading AI safety researchers while tackling critical problems in preventing catastrophic misuse. If you're excited about using your expertise to ensure AI systems remain safe and beneficial, we want to hear from you.

Responsibilities:

• Design and implement evaluation methodologies for assessing AI model capabilities relevant to chemical weapons, explosives synthesis, and energetic materials
• Develop and execute strategies to identify and mitigate potential C/E misuse in model outputs
• Create C/E threat models, including precursor identification, synthesis routes, and weaponization techniques
• Review and analyze traffic to identify potential policy violations related to C/E content
• Collaborate with software engineers to develop and refine detection systems and automated enforcement tools for C/E threats
• Conduct rapid response to escalations involving dangerous C/E queries
• Collaborate across teams to establish safety benchmarks and develop appropriate model guardrails
• Translate C/E domain knowledge into actionable safety requirements
• Develop approaches to assess C/E model knowledge boundaries for dual-use chemical information
• Monitor emerging threats in the C/E landscape to inform policy development

You may be a good fit if you:

• Have a Ph.D. in Chemistry, Chemical Engineering, or a related field with focus on energetic materials, explosives, and/or chemical weapons
• Have 5-8+ years of experience in chemical weapons and/or explosives defense, with deep expertise in energetic materials, chemical weapon agents, or related areas
• Have knowledge of high yield explosives application to radiological dispersal devices (dirty bombs) and related radiological weapons
• Have a track record of translating specialized technical knowledge into actionable safety policies or guidelines
• Are comfortable navigating ambiguity and developing solutions for novel safety challenges
• Can work independently while maintaining strong collaboration with cross-functional teams including engineering, enforcement, and research
• Thrive in a fast-paced environment where you balance rigorous scientific standards with rapid threat response
• Are passionate about preventing misuse of dangerous technical knowledge while enabling beneficial applications

Strong candidates may have:

• Experience with both chemical weapons and high yield explosives defense
• Experience working with defense, intelligence, or nonproliferation organizations (e.g., OPCW, IAEA, national labs, defense contractors)
• Published research or practical experience in explosives characterization, chemical weapons detection, or related security applications
• Knowledge of international chemical weapons conventions (CWC) and controlled substances regulations
• Demonstrated ability to communicate complex technical concepts to non-specialist audiences
• Experience with chemical databases (PubChem, Reaxys, SciFinder) and computational chemistry tools
• Understanding of radiological materials and their interaction with explosive dispersal mechanisms
• Familiarity with dual-use C/E research concerns and responsible disclosure practices

Logistics

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.

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.

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.

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.

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Microsoft are looking for a talented Technical Advisor to join their Microsoft Superintelligence team in the Office of the CEO. This role requires someone who can provide direct technical and strategic support to the CEO of Microsoft AI, identifying opportunities and risks across our research portfolio.

About the Role

As a Technical Advisor, you will engage deeply with research teams on pre-training, post-training, multimodal systems, infrastructure, and safety/alignment work. You will prototype and evaluate models, techniques, and infrastructure to develop insights and inform strategic decisions. You will analyze technical tradeoffs and synthesize findings into clear, actionable recommendations for leadership.

Accountabilities

• Provide direct technical and strategic support to the CEO of Microsoft AI—identifying opportunities and risks across our research portfolio
• Engage deeply with research teams on pre-training, post-training, multimodal systems, infrastructure, and safety/alignment work

The Candidate we're looking for

Experience:

• 4+ years technical engineering experience with coding

Technical skills:

• Software engineering skills with the ability to rapidly prototype and evaluate technical solutions
• Understanding modern ML techniques, including large language models, multimodal systems, agents, or related areas

Personal attributes:

• Written and verbal communication skills, with the ability to distill complex technical concepts for executive audiences
• Track record of operating effectively in fast-paced, ambiguous environments

Benefits

• Software Engineering IC4 – The typical base pay range for this role across the U.S. is USD $119,800 – $234,700 per year
• Certain roles may be eligible for benefits and other compensation

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