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Pinterest

<div><div><p><b>About Pinterest:</b></p>
<p>
Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.</p>
<p>
Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace the flexibility to do your best work. Creating a career you love? It's Possible.</p>
<p>
At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.</p>
<p>
Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.</p></div><p>
Pinterest is seeking an experienced Staff Data Scientist to join our Finance &amp; Business Operations team. This is a hybrid data-science / applied-AI / product-engineering role inside Pinterest's CFO organization. It sits at the intersection of forecasting and finance analytics, internal tool-building, and AI adoption &mdash; and the person in it is expected to operate across all three.</p>
<p>
The core mandate is to make the CFO org's forecasting and planning work faster, be more rigorous, and more self-serve. In practice that has meant owning a forecasting product end to end (data pipeline through user-facing UI), partnering directly with Finance, BizOps, and Core/Monetization stakeholders to embed it in their workflows, and turning the company's emerging AI platform capabilities into tools that finance teams actually use day to day.</p>
<p>
This is a high-autonomy, high-trust individual-contributor role with broad cross-functional reach.</p><br>
<p></p>
<p><b>
What you'll do:</b></p>
<ul>
<li><b>Own forecasting tooling end to end.</b> Build and maintain the team's primary forecasting workbench &mdash; from the underlying data and forecast logic through the interactive web UI that planners use to create, adjust, and review forecasts. This spans baseline vs. adjusted forecast modeling, scenario/delta workflows, backtesting, and diagnostics (year-over-year and month-over-month seasonality, engagement rates, and similar).</li>
<li><b>Ship product, not just analysis.</b> Design and build user-facing features: chart and visualization work, guided onboarding, history/audit views, region and time-grain filtering, performance optimization, and the kind of polish and bug-fixing that makes an internal tool feel like a real product. Instrument usage (collect and analyze raw logs) and let adoption data drive the roadmap.</li>
<li><b>Drive AI adoption across Finance &amp; BizOps.</b> Take platform-level AI capabilities and turn them into concrete, trusted tools for finance users. Bring structured business cases (not wishlists) to platform/IT partners, pilot new capabilities, and write the enablement material &mdash; walkthroughs, documentation, &quot;where to get started&quot; guidance &mdash; that gets non-technical teams productive.</li>
<li><b>Stay ahead of the AI capability curve.</b> A significant part of this role is forward-looking: continuously read and interpret AI research (papers, model and tooling releases) and translate it into a grounded point of view on what will be possible in the next 6&ndash;12 months. Track the engineering roadmap closely, understand what platform capabilities are landing and when, and connect those dots to concrete opportunities for the CFO org &mdash; so the team builds for where AI is going, not just where it is today.</li>
<li><b>Set AI strategy and guide executives.</b> Turn that capability foresight into strategy: shape the CFO org's AI roadmap, prioritize where to invest, and advise senior leaders and executives on what's real, what's hype, and what to bet on. Communicate complex AI and technical trade-offs in plain, decision-ready terms, and act as a trusted technical advisor in executive conversations.</li>
<li><b>Deliver recurring finance analytics.</b> Support core CFO-org deliverables: budget-vs-actuals (BVAs), variance commentary, executive slide/deck preparation, and metric diagnostics (e.g., MAU and revenue diagnostics), including catching and resolving data-quality issues.</li>
<li><b>Partner broadly and communicate clearly.</b> Work directly with Finance, BizOps, Monetization, and platform/IT stakeholders. Translate ambiguous business questions into tooling and analysis, post clear release notes and stakeholder updates, and run live walkthroughs and training sessions.</li>
<li><b>Set technical and analytical standards.</b> raise the bar on rigor (validation, backtesting, sound metric definitions), make pragmatic build-vs-buy and scope calls, and create artifacts and documentation durable enough to outlive any single contributor.</li></ul><p><br>
<br>
<b>What we're looking for:</b></p>
<ul>
<li><b>Data science &amp; forecasting</b></li>
<li>Strong applied background in time-series forecasting and quantitative analysis: baseline construction, scenario/adjustment modeling, backtesting and forecast-accuracy evaluation, and seasonality analysis (y/y, m/m).</li>
<li>Fluency in turning messy business questions into well-defined metrics and diagnostics; rigorous about metric definitions, data quality, and validation.</li>
<li>Advanced SQL and proficiency in a primary analysis language (Python strongly preferred); comfort working directly with data warehouses and large datasets.</li>
<li><b>Engineering &amp; tool-building</b></li>
<li>Demonstrated ability to build and ship internal web tools, not just notebooks or one-off analyses &mdash; meaningful front-end / full-stack capability (e.g., JavaScript/TypeScript, modern UI frameworks, interactive data visualization).</li>
<li>Practical product-engineering instincts: UX/usability sense, performance debugging and optimization, handling state/data edge cases, and disciplined release hygiene (testing, build/lint, changelogs).</li>
<li>Experience building dashboards and self-serve analytics (e.g., Superset, Tableau, Looker, or equivalent).</li>
<li><b>Applied AI</b></li>
<li>Hands-on experience applying modern AI/LLM tooling to real workflows &mdash; prototyping with AI assistants, agentic/MCP-style tooling, or internal AI platforms &mdash; and a track record of moving from experiment to adopted tool.</li>
<li>Ability to build the business case for AI investment and to drive adoption with non-technical users (enablement, documentation, training).</li>
<li><b>AI foresight &amp; strategy (critical)</b></li>
<li>Demonstrated habit of staying current with AI research and the broader landscape: able to read papers and model/tooling release notes and form a credible, independent view of what will be feasible 6&ndash;12 months out.</li>
<li>Able to interpret an engineering roadmap and reconcile it with where the technology is heading &mdash; translating both into a concrete capability plan for the business.</li>
<li>Strong product/business strategy instincts: prioritizing AI investments, sequencing bets, and distinguishing durable capability from hype.</li>
<li><b>Executive influence</b></li>
<li>Proven ability to advise and guide senior leaders and executives on technical and AI strategy, and to make complex trade-offs legible to a non-technical executive audience.</li>
<li>Comfortable being the trusted technical voice in the room &mdash; framing decisions, managing expectations, and earning credibility with both finance leadership and engineering/platform partners.</li>
<li><b>Scope, ownership &amp; communication</b></li>
<li>Staff-level autonomy: can independently scope, prioritize, and deliver multi-month efforts with minimal direction, and make sound trade-off calls.</li>
<li>Excellent written and verbal communication; can write for executives and for end users, and can run live training and walkthroughs.</li>
<li>Strong cross-functional collaboration across finance, operations, and technical/platform partners.</li>
<li><b>Experience</b></li>
<li>Minimum of 8 years of relevant experience in data science, analytics engineering, or applied ML.</li>
<li>Bachelor's degree in a quantitative field (e.g., statistics, computer science, economics, engineering, math) or equivalent practical experience; advanced degree is a plus.</li></ul><br>
<p></p>
<p><b>
Relocation Statement:</b></p>
<ul>
<li>This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.</li></ul><br>
<p></p>
<p><b>
In-Office Requirement Statement:</b></p>
<ul>
<li>We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.</li>
<li>This role will need to be in the office for in-person collaboration1-2 times every 6-months and therefore can be situated anywhere in the country.</li><br>
</ul>
<p>#LI-NM4</p>
<p>
#LI-REMOTE</p><div><p><b>
Our Commitment to Inclusion:</b></p>
<div>
Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.</div>
<div></div>
<div><i>By submitting this application, I certify that all information submitted in my application and throughout the hiring process is true, accurate, and complete to the best of my knowledge. I understand that any false statement, omission, or misrepresentation may disqualify me from employment consideration or result in termination if discovered after hire.</i></div></div></div>

Staff Data Scientist, Finance & Business Ops

David Joseph & Company

<div><p><b>San Francisco, CA &middot; On-site &middot; Full-time</b><br>
<b>Compensation:</b> $130,000&ndash;$150,000 base (up to $300K total cash comp) + equity</p><h3 class="jobSectionHeader"><b>
About the Company</b></h3><p>
A fast-growing, venture-backed AI company building the training data and evaluation infrastructure that leading frontier AI labs use to improve their models &mdash; designing high-signal datasets and running rigorous evaluations that go beyond static benchmarks. One of the fastest-growing companies in its cohort.</p><p>
Founded 2025 &middot; 11&ndash;50 people &middot; Industry: AI training data &amp; model evaluation infrastructure</p><h3 class="jobSectionHeader"><b>
The Role</b></h3><p>
A founding-level role at the intersection of technical execution and strategic leadership. You'll own high-impact initiatives end-to-end &mdash; from requirements through delivery &mdash; managing teams of engineers, data scientists, and ML specialists while shipping production code yourself, and interfacing directly with research teams at leading AI labs.</p><p><b>
What you'll be doing</b></p><ul><li>
Own end-to-end technical initiatives: scoping requirements, building solutions, managing experts through delivery, and shipping code yourself when it unblocks the team.</li><li>
Design and build technical infrastructure and tooling for dataset generation, quality control, and delivery pipelines across multiple concurrent lab contracts.</li><li>
Manage and mentor teams of software engineers, data scientists, and ML specialists; set technical direction and hold the bar for execution velocity.</li><li>
Interface directly with research teams at leading AI labs to translate their needs into technical specs and ensure delivery meets their standards.</li><li>
Debug production issues live under pressure; rapidly context-switch between hands-on engineering, team leadership, and strategic problem-solving.</li><li>
Support the founding team on the highest-impact work &mdash; internal tooling, major contracts, and hiring.</li></ul><p><b>
Tech stack:</b> Python; data pipelines, APIs, ML/AI tooling</p><h3 class="jobSectionHeader"><b>
Requirements</b></h3><ul><li>
CS degree, engineering experience, or equivalent demonstrated ability to ship technical projects</li><li>
Manage teams of technical experts across software engineering, data science, and machine learning</li><li>
Build technical infrastructure and tooling for dataset generation, quality control, and delivery pipelines</li><li>
Drive technical and operational initiatives end-to-end: scoping requirements, building solutions, and managing technical experts</li><li>
Interface directly with engineering and research teams at leading AI labs</li><li>
Support founding team on high-priority technical projects and operational initiatives</li><li>
Proficiency in Python and production-quality code</li><li>
Experience with data pipelines, APIs, or ML/AI tooling</li><li>
Strong leadership experience and communication skills</li><li>
Ability to context-switch rapidly between technical and strategic work</li><li>
Intense work ethic, agency, and drive</li><li>
Competitive nature and track record of exceptional achievement</li><li>
Undergraduate degree from the U.S., Canada, or the U.K.</li></ul><h3 class="jobSectionHeader"><b>
Green Flags</b></h3><ul><li>
Background in software engineering, ML engineering, AI research, or computer science</li><li>
Experience at top tech company (FAANG, frontier AI lab) or quantitative trading firm</li><li>
Prior startup experience or founding experience</li><li>
Willingness to work long hours and weekends when projects demand it</li></ul><h3 class="jobSectionHeader"><b>
Red Flags</b></h3><ul><li>
Worked cushy tech jobs (FAANG PM, etc.) and may not be used to intense work environments.</li><li>
Struggles with ambiguity or lacks examples of owning large, complex projects.</li><li>
Only interested in remote work &mdash; role is strictly in-person.</li><li>
Can't articulate why they want to pivot into AI ops or join a startup now.</li><li>
No experience managing teams or unclear on motivating people toward execution.</li></ul><h3 class="jobSectionHeader"><b>
Why Join</b></h3><ul><li>
Direct access to the founding team on their highest-priority problems</li><li>
Exposure to leading frontier AI labs; work that powers major contracts</li><li>
Up to $300K total cash comp plus meaningful equity in a fast-scaling company</li></ul><h3 class="jobSectionHeader"><b>
Details</b></h3><ul><li><b>
Location:</b> San Francisco, CA</li><li><b>
Work policy:</b> On-site &mdash; strictly in-person</li><li><b>
Compensation:</b> $130,000&ndash;$150,000 base (up to $300K total cash comp) + equity</li><li><b>
Visa sponsorship:</b> None available</li><li><b>
Employment type:</b> Full-time</li></ul></div>

Strategic Projects Lead - Coding

Anthropic

<div><div><h2 class="jobSectionHeader"><b>About Anthropic</b></h2>
<p>
Anthropic&rsquo;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.</p></div><div><h2 class="jobSectionHeader"><b>
About the Role</b></h2>
<p>
We&rsquo;re looking for a creative, high-agency content producer to help us tell internal stories about the people and work at Anthropic. In this role, you&rsquo;ll work closely with the Internal Editorial Lead to discover and share stories about the progress, moments, and people that define our culture through videos, audio, physical artifacts &mdash; whatever format makes the most sense for the material.</p>
<p>
We&rsquo;re looking for someone who is <i>extremely</i> creative: the work is wildly interesting, and the way we share it should be equally compelling. This person should be ambitious in their pursuit of thinking outside the box, willing to take risks, and not be precious about the final product being a little rough around the edges. (Turnaround times will be quick!)</p>
<h2 class="jobSectionHeader"><b>
What You'll Do</b></h2>
<ul><li>Work with the Internal Editorial Lead to identify the stories</li>
<li>Tell those stories in unexpected ways (e.g. through videos, in-office pop-up exhibits, podcast series)</li>
<li>Own the end-to-end content production process in-house</li>
</ul><h2 class="jobSectionHeader"><b>What We're Looking For</b></h2>
<ul><li>A knack for finding and building narratives</li>
<li>Experience producing content in different mediums (e.g. video, audio, physical artifacts)</li>
<li>Experience in high-trust, time-sensitive environments</li>
<li>Ability to move extremely quickly and fluidly</li>
<li>Based in San Francisco</li>
</ul></div><div><div><div><div><p>
The annual compensation range for this role is listed below.</p>
<p>
For sales roles, the range provided is the role&rsquo;s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p></div></div><p>
Annual Salary:</p><p>
$295,000 - $345,000 USD</p></div></div><div><h2 class="jobSectionHeader"><b>
Logistics</b></h2>
<p><b>
Minimum education: </b>Bachelor&rsquo;s degree or an equivalent combination of education, training, and/or experience</p>
<p><b>
Required field of study: </b>A field relevant to the role as demonstrated through coursework, training, or professional experience</p>
<p><b>
Minimum years of experience: </b>Years of experience required will correlate with the internal job level requirements for the position</p>
<p><b>
Location-based hybrid policy:</b> 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.</p>
<p><b>
Visa sponsorship:</b> 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.</p>
<p><b>
We encourage you to apply even if you do not believe you meet every single qualification.</b> 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.<br>
<br>
<b>Your safety matters to us.</b> 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&mdash;visit anthropic.com/careers directly for confirmed position openings.</p>
<h2 class="jobSectionHeader"><b>
How we're different</b></h2>
<p>
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 &mdash; advancing our long-term goals of steerable, trustworthy AI &mdash; 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.</p>
<p>
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 &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>
<h2 class="jobSectionHeader"><b>
Come work with us!</b></h2>
<p>
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. <b>Guidance on Candidates' AI Usage:</b> Learn about our policy for using AI in our application process.</p></div></div>

Internal Content Producer

<div><p><b>San Francisco, CA &middot; On-site &middot; Full-time</b><br>
<b>Compensation:</b> $180,000&ndash;$220,000 + competitive equity</p><h2 class="jobSectionHeader"><b>
About the Company</b></h2><p>
An early-stage (post&ndash;Series A) company building the training data and evaluation infrastructure that frontier AI labs use to improve their models &mdash; designing high-signal datasets and running rigorous evaluations that go beyond static benchmarks. A small team where individual contributors have direct impact on how the next generation of models learns. The company has raised $30M (~$300M valuation), with a founding team drawn from Jane Street, Citadel, Google, Goldman, and Stanford AI Lab.</p><p>
Founded 2025 &middot; 11&ndash;50 people &middot; Industry: Consumer Tech</p><h2 class="jobSectionHeader"><b>
The Role</b></h2><p>
As a SWE (Environments), you'll design the datasets and evaluation rubrics that directly influence how frontier models learn &mdash; going from hypothesis to live experiment quickly, with output feeding directly into model training runs at scale.</p><p><b>
What you'll be doing</b></p><ul><li>
Design data slices and explore data shapes that expose meaningful model failure modes across domains like finance, code, and enterprise workflows</li><li>
Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines</li><li>
Model annotator behavior and run experiments to improve different model capabilities</li><li>
Develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on model alignment and capability</li><li>
Create and manage both real-world and synthetic data pipelines</li><li>
Partner with lab research teams to translate their training objectives into concrete data and evaluation specifications</li></ul><p><b>
Tech stack:</b> Not specified</p><h2 class="jobSectionHeader"><b>
Requirements</b></h2><ul><li>
1&ndash;4 years of software engineering experience with strong technical depth</li><li>
Design targeted data slices that surface model failure modes across high-stakes domains (finance, code generation, enterprise workflows)</li><li>
Build and iterate on evaluation rubrics and reward signals powering RLHF and RLVR training pipelines</li><li>
Develop quantitative frameworks to measure dataset quality, diversity, and downstream impact on model alignment and capability</li><li>
Own end-to-end real world and synthetic data pipelines, from scoping with research teams to production-ready evaluation specs</li><li>
Run annotator modeling experiments to improve model capabilities across task types</li></ul><h2 class="jobSectionHeader"><b>
Green Flags</b></h2><ul><li>
Experience at RL environment companies</li><li>
Background in AI safety or benchmarking organizations like METR or Artificial Analysis</li><li>
Genuine obsession with how data structure, selection, and quality drive model behavior</li><li>
Ability to design lightweight experiments and move fast</li><li>
Former founders or early engineers at early stage startups</li><li>
Demonstrated ability to work hard, learn fast, and care deeply about details</li></ul><h2 class="jobSectionHeader"><b>
Red Flags</b></h2><ul><li>
Pure research profile with limited engineering output, this is a SWE role, shipping matters</li><li>
Looking for standard product engineering work &mdash; the real scope is data pipelines, reward modeling, and eval infra</li></ul><h2 class="jobSectionHeader"><b>
Why Join</b></h2><ul><li>
Outsized total cash: base plus substantial profit share, plus competitive equity</li><li>
Direct impact on frontier AI model development, working with the world's leading AI labs</li><li>
High ownership on a small, early team &mdash; scope, build, and ship end to end</li></ul><h2 class="jobSectionHeader"><b>
Details</b></h2><ul><li><b>
Location:</b> San Francisco, CA</li><li><b>
Work policy:</b> On-site</li><li><b>
Compensation:</b> $180,000&ndash;$220,000 + equity</li><li><b>
Visa sponsorship:</b> O-1, OPT</li><li><b>
Employment type:</b> Full-time</li></ul></div>

Software Engineer, RL Environments

Miso Labs

<div><p>At Miso Labs, our goal is to pass the voice Turing test. In order to do so, we must move beyond the current STT-&gt;LLM-&gt;TTS paradigm, and train full-duplex speech-to-speech models. This requires exploring new architectural ideas, and scaling them rapidly.</p><p></p><p>
We&rsquo;ve raised a large seed round from top investors and are hiring our founding research team. We&rsquo;re a small team, so you can expect to work on every part of the model training stack from small-scale architecture experiments, to large pre-training runs on up to 1000 H100s. We are an in-person culture in San Francisco, and you will be expected to relocate to the SF area (we will cover the moving costs).</p><p></p><p>
It is important to us that team members get credit for their work. Outside of our core IP, researchers are encouraged to publish their work in conferences, and we are happy to sponsor travel to top conferences in AI/ML.</p><p></p><p>
Preferred Qualifications:</p><ul><li><p>
Have a Bachelor&rsquo;s Degree in a technical field (Computer Science, Physics, Mathematics etc.) or equivalent experience</p></li><li><p>
Excited to work on all aspects of model training and development</p></li><li><p>
Are comfortable with both research and hands-on engineering</p></li><li><p>
Are capable of owning long-term research outcomes end-to-end</p></li><li><p>
Willing to learn technical skills quickly, in a self-directed manner</p></li><li><p>
Prior experience with both PyTorch and CUDA</p></li></ul><p></p><p>
If you&rsquo;re interested in this role, please apply even if you don&rsquo;t fit all the qualifications. We&rsquo;re more interested in evidence of exceptional ability than prior experience in deep learning/AI.</p><p></p><p>
This role comes with full benefits including:</p><ul><li><p>
Healthcare, vision, and dental</p></li><li><p>
Stipend for gym</p></li><li><p>
Relocation bonus</p></li></ul><p>
Compensation Range: $400K - $500K</p></div>

Founding Researcher

Google

<div>Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.<br>
<br>
In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:<br>
<ul><li>
Health, dental, vision, life, disability insurance</li><li>
Retirement Benefits: 401(k) with company match</li><li>
Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment</li><li>
Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
</li><li>Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks</li><li>
Baby Bonding Leave: 18 weeks</li><li>
Holidays: 13 paid days per year</li></ul><br>
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: <b>San Francisco, CA, USA; Mountain View, CA, USA; New York, NY, USA; Washington D.C., DC, USA</b>.<h3 class="jobSectionHeader"><b>
Minimum qualifications:</b></h3><ul>
<li>Bachelor's degree or equivalent practical experience.<br>
</li>
<li>3 years of experience designing, scoping, executing, and delivering research and analysis projects.<br>
</li>
<li>Experience translating business problems into research questions and translating research findings and insights into marketing recommendations.<br>
</li>
<li>Experience managing qualitative, quantitative and measurement agencies.<br>
</li>
<li>Experience managing cross-functional or cross-team projects.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>Experience using AI tooling to conduct or facilitate research and measurement.</li>
<li>Knowledge of Washington DC and the US political environment.</li>
<li>Knowledge of policy in international and multinational (e.g. EU) policymaking processes.</li>
<li>Demonstrated interest in AI and its impact on society.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2>
In order to build excellent products and tell great stories, we must first profoundly understand our audiences - what motivates them, why they think and behave the way they do, and what they think of our brands and products. The Consumer Apps Marketing Research and Insights team is at the heart of developing this understanding.<div><p>
As a Research Manager, you will be embedded within the Reputation Marketing team, and inform how Google builds its reputation with the priority audiences shaping the AI landscape (policymakers, scientists, voters, and more). In this role, you will utilize both traditional research methods and new AI tooling to help the Reputation team understand and respond to the changing AI landscape via sharp, insights-driven marketing and communications.</p><p>
Your primary focus will be partnering with the AI Responsibility team: conducting and synthesizing brand tracking, qualitative research, and real-time social analysis across a variety of core issue areas (science, health, government, economy, and more). Your role will be to identify the audiences that matter in shaping the narratives for each issue, understand how those narratives spread and solidify, and provide tactical recommendations for how the AI Responsibility team should react. Managing stakeholder relationships, staying informed on Marketing priorities, scoping syntheses/studies to support strategy discussions, and managing a measurement framework will be an integral part of your day-to-day work.</p><p>
As a secondary-priority, you will help manage the global policymaker sentiment tracker. Your key responsibilities include helping to improve the program&rsquo;s operations, synthesizing findings and connecting dots between other programs, and presenting findings to teams across Google.</p></div>
Know the user. Know the magic. Connect the two. At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems-from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can-changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
<br>
<br>
US: $116000 - $167000 (USD) + 15% bonus target + equity + benefits<br>
<br>
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Scope custom research from start to finish, including: designing an effective brief, managing Requests for Proposals (RFPs), onboarding research agencies, choosing a methodology, executing the study, and deploying insights and recommendations via compelling narratives in a variety of formats (decks, sites, emails, presentations, etc.).</li>
<li>Distill findings from a variety of sources into cohesive research narratives, ultimately delivering the underpinning insight that drives critical leadership decision-making.</li>
<li>Manage the global policymaker tracking program, synthesize insights from the research, and present to teams throughout Google.</li>
<li>Share research findings and insights via presentations in forums large and small, with everyone from junior to executive staff.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Research and Insights Manager, Marketing, Reputation and AI Responsibility

<div><div><h2 class="jobSectionHeader"><b>About Anthropic</b></h2>
<p>
Anthropic&rsquo;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.</p></div><div><h2 class="jobSectionHeader"><b>
About the role</b></h2>
<p>
The Fraud Prevention team protects Anthropic's payment and monetization surfaces from financial abuse &mdash; keeping fraud losses, dispute rates, and network monitoring exposure in check while preserving a smooth experience for legitimate customers. As a software engineer on this team, you will build the systems that make risk decisions in real time, manage the dispute and chargeback lifecycle, and detect monetization abuse across subscriptions, in-app purchases, and promotions. The ideal candidate can see things from attackers' perspectives, anticipate their responses to countermeasures, and never loses sight of the fact that a false positive here is a paying customer.</p>
<p>
Payments fraud is more externally coupled than most trust and safety work &mdash; you'll collaborate closely with finance, support, and legal teams internally, and with payment processors and platform partners externally.</p>
<h2 class="jobSectionHeader"><b>
Responsibilities:</b></h2>
<ul><li>Design and build real-time risk decisioning that scores transactions at authorization time, balancing fraud loss, approval rates, and latency constraints</li>
<li>Build tooling and automation for the dispute and chargeback lifecycle, from review queues to evidence collection and loss reporting</li>
<li>Engineer fraud signals at scale &mdash; device fingerprinting, BIN and issuer signals, velocity features, and cross-account linkage &mdash; and detect monetization abuse across subscriptions, trials, promotions, and in-app purchases</li>
<li>Own a portfolio of metrics &mdash; loss rate, dispute rate, authorization approval impact, and false-positive rate &mdash; rather than optimizing any single number</li>
<li>Lead investigations into emerging fraud patterns, building multi-layered defenses designed for attacker adaptation rather than point-in-time rules</li>
<li>Work cross-functionally with finance, support, legal, and data science, and with external payment processors and platform partners</li>
</ul><h2 class="jobSectionHeader"><b>Minimum Qualifications:</b></h2>
<ul><li>Proficiency in Python, SQL, and data analysis tools</li>
<li>Experience building or operating fraud, risk, or abuse detection systems in production</li>
<li>Strong communication skills and ability to explain complex technical tradeoffs to non-technical stakeholders</li>
</ul><h2 class="jobSectionHeader"><b>Preferred Qualifications:</b></h2>
<ul><li>8+ years of industry software engineering experience, with a focus on payments fraud or risk</li>
<li>Fluency with payments rails: card networks, payment service providers (e.g., Stripe, Adyen), in-app purchase platforms (Apple, Google), refund flows, and the chargeback and dispute lifecycle</li>
<li>Direct experience combating fraud typologies such as card testing, stolen-card monetization, refund and chargeback abuse, subscription and trial abuse, promotional abuse, and friendly fraud</li>
<li>Understanding of fraud loss accounting &mdash; fraud loss vs. dispute fees vs. card network monitoring programs (e.g., VDMP, i VFMP, Mastercard ECP) &mdash; and why chargeback rate thresholds carry existential stakes</li>
<li>Experience building hybrid rules-and-ML risk systems: real-time scoring at authorization plus post-authorization review workflows</li>
<li>Experience at a marketplace or subscription business, or on a processor-side or issuer-side risk team</li>
</ul></div><div><div><div><div><p>
The annual compensation range for this role is listed below.</p>
<p>
For sales roles, the range provided is the role&rsquo;s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p></div></div><p>
Annual Salary:</p><p>
$320,000 - $485,000 USD</p></div></div><div><h2 class="jobSectionHeader"><b>
Logistics</b></h2>
<p><b>
Minimum education: </b>Bachelor&rsquo;s degree or an equivalent combination of education, training, and/or experience</p>
<p><b>
Required field of study: </b>A field relevant to the role as demonstrated through coursework, training, or professional experience</p>
<p><b>
Minimum years of experience: </b>Years of experience required will correlate with the internal job level requirements for the position</p>
<p><b>
Location-based hybrid policy:</b> 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.</p>
<p><b>
Visa sponsorship:</b> 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.</p>
<p><b>
We encourage you to apply even if you do not believe you meet every single qualification.</b> 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.<br>
<br>
<b>Your safety matters to us.</b> 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&mdash;visit anthropic.com/careers directly for confirmed position openings.</p>
<h2 class="jobSectionHeader"><b>
How we're different</b></h2>
<p>
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 &mdash; advancing our long-term goals of steerable, trustworthy AI &mdash; 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.</p>
<p>
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 &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.</p>
<h2 class="jobSectionHeader"><b>
Come work with us!</b></h2>
<p>
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. <b>Guidance on Candidates' AI Usage:</b> Learn about our policy for using AI in our application process.</p></div></div>

Staff+ Software Engineer, Financial Fraud

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