Location:  Need to be able to work EST timezone. Remote | Full-time Compensation: $175K - $250K We are hiring on behalf of our client who is developing a cutting-edge autonomous agent runtime focused on high-frequency financial environments. While current agents operate effectively as independent units, the next phase of evolution involves building a sophisticated intelligence layer where the entire fleet learns autonomously from real-time market outcomes. The Staff AI Engineer will be responsible for moving beyond manual propagation of insights to a system where the fleet gets smarter with every trade. This is a high-stakes production role, not a research position. The feedback loop is immediate and measurable: the work produced either enhances agent profitability or it does not. The successful candidate will own the intelligence layer that turns thousands of daily trading decisions into compounding, autonomous growth. Key Responsibilities: Learning & Optimization - Feedback Loop Implementation: Design and implement systems that connect trade outcomes back to strategy improvement, specifically focusing on signal selection, risk parameters, position sizing, and timing. - Evaluation Frameworks: Build frameworks to quantify which signals and market conditions accurately predict profitable trades versus noise. - Automated Strategy Generation: Develop systems to explore new configurations, backtest them against real fleet data, and surface candidates for deployment autonomously. - Market Adaptation: Build mechanisms to detect shifts in market conditions (e.g., trending vs. choppy) and adapt fleet behavior in real-time. Autonomous Fleet Intelligence - Fleet Monitoring: Create higher-order agents for automated monitoring to catch configuration errors and performance degradation across all concurrent agents. - Performance Attribution: Decompose trades into component drivers—signal accuracy, execution efficiency, and exit timing—to feed insights back into strategy design. - Coordination & Risk: Manage concentration risk and capital allocation across the fleet, balancing the exploration of new approaches with the exploitation of proven strategies. Model & Inference - Infrastructure Ownership: Transition from external LLM dependence to controlled intelligence, evaluating hosting strategies ranging from proxied external models to fine-tuned, domain-specific models. - Data Capture: Build the telemetry and data capture layer to ensure every decision and outcome is structured and queryable. - Domain-Specific Training: Determine the efficacy of domain-specific training over general-purpose prompting and build the necessary pipelines for implementation. - Inference Optimization: Optimize inference for many concurrent agents, ensuring structured decision outputs and cost-efficiency at scale.

Location:  Need to be able to work EST timezone. Remote | Full-time Compensation: $175K - $250K We are hiring on behalf of our client who is developing a cutting-edge autonomous agent runtime focused on high-frequency financial environments. While current agents operate effectively as independent units, the next phase of evolution involves building a sophisticated intelligence layer where the entire fleet learns autonomously from real-time market outcomes. The Staff AI Engineer will be responsible for moving beyond manual propagation of insights to a system where the fleet gets smarter with every trade. This is a high-stakes production role, not a research position. The feedback loop is immediate and measurable: the work produced either enhances agent profitability or it does not. The successful candidate will own the intelligence layer that turns thousands of daily trading decisions into compounding, autonomous growth. Key Responsibilities: Learning & Optimization - Feedback Loop Implementation: Design and implement systems that connect trade outcomes back to strategy improvement, specifically focusing on signal selection, risk parameters, position sizing, and timing. - Evaluation Frameworks: Build frameworks to quantify which signals and market conditions accurately predict profitable trades versus noise. - Automated Strategy Generation: Develop systems to explore new configurations, backtest them against real fleet data, and surface candidates for deployment autonomously. - Market Adaptation: Build mechanisms to detect shifts in market conditions (e.g., trending vs. choppy) and adapt fleet behavior in real-time. Autonomous Fleet Intelligence - Fleet Monitoring: Create higher-order agents for automated monitoring to catch configuration errors and performance degradation across all concurrent agents. - Performance Attribution: Decompose trades into component drivers—signal accuracy, execution efficiency, and exit timing—to feed insights back into strategy design. - Coordination & Risk: Manage concentration risk and capital allocation across the fleet, balancing the exploration of new approaches with the exploitation of proven strategies. Model & Inference - Infrastructure Ownership: Transition from external LLM dependence to controlled intelligence, evaluating hosting strategies ranging from proxied external models to fine-tuned, domain-specific models. - Data Capture: Build the telemetry and data capture layer to ensure every decision and outcome is structured and queryable. - Domain-Specific Training: Determine the efficacy of domain-specific training over general-purpose prompting and build the necessary pipelines for implementation. - Inference Optimization: Optimize inference for many concurrent agents, ensuring structured decision outputs and cost-efficiency at scale.

• Review and enhance AI-generated outputs and recommendations related to AI architecture, design, and scalability • Evaluate the technical accuracy and feasibility of AI-driven architectural scenarios and solutions • Develop practical examples, use cases, and risk assessments from your domain experience • Create scenario variations for different architectural roles – solution architects, data scientists, leads • Identify shortcomings, risks, and potential gaps in AI-generated suggestions, providing actionable feedback • Directly influence how AI models reason about architecture design, best practices, and industry challenges

Title: Design Engineer, AI Capability Development (Education Labs) Location: San Francisco, CA | New York City, NY Job Description: About Anthropic Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We believe skill with AI is fundamental to human agency. Education Labs builds the paradigms that help people become genuinely more capable-not just more engaged. This is a new kind of role: part researcher, part product builder, part interaction designer. You'll be the second technical builder on a small team studying how AI transforms human capability-and shipping features based on what we discover. You'll have significant creative license to define what "good" looks like, exploring new interaction patterns rather than optimizing existing ones. We're skeptical of tutorials, onboarding flows, and engagement metrics. We care about experiences that make users progressively more capable, curious, and empowered over time. This means integrating skill development into product design, using Claude itself as a capability-building partner, and measuring success by how users actually grow. You'll operate as a one-person technical shop: prototyping new ideas, establishing technical direction, and shipping production-quality features to millions of users. You'll need strong product instincts and clean interface design sensibilities, balanced with comfort in ambiguity and frontier thinking. Responsibilities: - Ship features that help users develop real skill with AI-measuring success by capability growth, not time-on-site - Architect end-to-end prototypes (front-end and back-end) that test new interaction paradigms, with particular attention to the front-of-the-frontend: motion, polish, and interaction feel - Define technical direction for the team-establish patterns others can follow - Build relationships across Product, Design, and Research to influence how skill development principles shape Anthropic's broader product strategy - Shape team strategy and roadmap-identify the highest-leverage opportunities and build conviction across stakeholders - Translate research insights about skill development and human-AI collaboration into shipped product through close collaboration with researchers - Document and share your work through clear writing, prototypes, and presentations that influence thinking across the organization You may be a good fit if you have: Strong full-stack engineering with design sensibility - 6+ years building and shipping web products, with deep expertise across the stack - Strong front-end craft: TypeScript/JavaScript, React, CSS-with an eye for interaction design, motion, and visual detail - Solid back-end and data pipeline experience: Python, API design, analytics infrastructure - A portfolio showcasing innovative interaction designs and high-quality implementations - Track record of independently driving features from prototype to production Deep conviction about human capability - Strong perspective on how technology should enhance human capabilities rather than diminish them - Experience or genuine passion for skill development, HCI, developer tools, or products that help people become more capable - Skepticism of purely engagement-driven metrics; interest in measuring capability outcomes Research mindset with product execution - Comfort with ambiguity and exploring undefined problem spaces - Ability to rapidly prototype, test with users, and iterate toward production - Strong instincts for product design and user experience, even without formal design training Strategic leadership and coalition building - Experience setting vision, shaping team strategy, and building conviction across cross-functional stakeholders - Ability to build productive relationships with Product, Design, Research, and Engineering teams-especially when your team isn't the owner - Strong sense of prioritization-knowing what to build now, what to defer, and what to cut - Track record of influencing roadmaps and decisions beyond your immediate team Strong candidates may also have: - Experience in developer tools, creative tools, learning platforms, or other products where user skill development and mastery matter more than time-on-site - Background in learning sciences, cognitive science, HCI, skill acquisition research, or educational psychology (formal or self-directed) - Experience with experimentation frameworks, A/B testing, or analytics that measure capability development in production - Previous experience in research labs, frontier tech companies, or startups with high autonomy and ambiguity - Published writing, talks, or open-source work on skill development, human-AI interaction, or product philosophy - Experience building AI-native product experiences or working with LLMs in production contexts Strong candidates may also have - Background in learning sciences, cognitive science, HCI, or educational psychology - Experience in developer tools, creative tools, or learning platforms where mastery matters more than engagement - Published writing, talks, or open-source work on skill development or human-AI interaction - Experience building AI-native product experiences or working with LLMs in production What this role is not This is a hands-on technical role building product features, embedded within a research team. You'll provide technical guidance and help set direction, but this role doesn't involve people management off the bat. If you're looking to immediately transition into engineering management or lead a large team, this likely isn't the right fit. The annual compensation range for this role is listed below. For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $300,000 - $405,000 USD Logistics Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. 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