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Common Sense Media

<div><p><b>Data Scientist</b></p><div></div><div><div><div><div><p><b>
Organization</b></p><p><br>
Common Sense Media</p></div><div><p><b><br>
Department</b></p><p><br>
Data</p></div><div><p><b><br>
Reports To</b></p><p><br>
Senior Director, Data &amp; Analytics</p></div><div><p><b><br>
Location</b></p><p><br>
San Francisco, CA</p></div><div><div><p><b><br>
Classification</b></p><p><b><br>
Salary Range</b></p></div><div><p><br>
Full-time, Exempt</p>
<p>$140,00&ndash;$166,250</p></div></div><br>
</div></div></div><p>
</p><p></p><p><b>POSITION OVERVIEW</b></p><p>
Common Sense Media is seeking a Data Scientist to help build data capabilities that allows the organization to answer the questions that matter most to advancing our mission and driving impact for kids and families. This is a hands-on, full-stack individual contributor role responsible for developing pipelines, models, and analyses that turn data from across the Common Sense Media ecosystem into reliable, decision-ready insights.</p><p>
The Data Scientist will develop predictive and statistical models that support organization-wide decisions, design and analyze experiments, apply causal inference methods, and lead impact analyses that measure how Common Sense Media's work affects kids, families, and educators, translating findings into clear, actionable recommendations for both technical and non-technical audiences. The Data Scientist's work will span the full range of data science at Common Sense Media, from foundational data engineering and data modeling through analytics, machine learning, experimentation, and impact analysis.</p><p>
As a senior technical voice on the team, the Data Scientist will help uplevel the organization's capabilities in data science, machine learning, and generative AI, advising on methodology, helping to develop and test AI product features, and raising the technical bar within product and engineering.</p><p>
</p><p><b>KEY RESPONSIBILITIES</b></p><p><i>
Analytics &amp; Modeling</i></p><ul><li><p>
Conduct rigorous exploratory and descriptive analyses that establish the foundational understanding of our audiences, programs, and performance.</p></li><li><p>
Develop predictive and statistical models that support organization-wide decisions.</p></li><li><p>
Apply modern machine learning techniques to ship models and analyses that drive impact.</p></li></ul><p><i>
Experimentation &amp; Causal Inference</i></p><ul><li><p>
Design and execute A/B tests and randomized controlled trials.</p></li><li><p>
Leverage rigorous quasi-experimental designs (e.g., difference-in-differences, regression discontinuity) and advanced causal inference techniques to evaluate the impact of complex product and program decisions where randomized testing is not feasible.</p></li></ul><p><i>
Impact Measurement</i></p><ul><li><p>
Lead rigorous impact analyses and program evaluations that measure how Common Sense Media's work affects kids, families, and educators.</p></li><li><p>
Apply causal inference and evaluation methods to measure real-world outcomes.</p></li></ul><p><i>
Insights &amp; Communication</i></p><ul><li><p>
Translate findings into clear, actionable recommendations for both technical and non-technical audiences.</p></li><li><h1 class="jobSectionHeader">
Partner closely with colleagues across Common Sense Media to put data to work across the organization.</h1></li></ul><p><i><br>
Analytics Engineering</i></p><ul><li>
Own the data models that turn raw data into clean, reliable, analysis-ready data sets used across the organization.</li><li>
Work with internal teams and external partners to establish data collection approaches, integrate new data sources, and ensure that incoming data is structured and trustworthy.</li><li><h1 class="jobSectionHeader">
Build and maintain the data pipelines and transformations necessary to support modeling, reporting, and impact measurement.</h1></li></ul><p><i><br>
Technical Leadership</i></p><ul><li><p>
Serve as a senior technical voice on the team, advising on methodology and raising the technical bar around how to use data in thoughtful decision-making.</p></li><li><p>
Help uplevel the organization's capabilities in data science, machine learning, and generative AI, supporting the development and testing of AI tools and features across the organization.</p></li></ul><p>
</p><p><b>QUALIFICATIONS</b></p><ul><li>
8+ years of experience in applied data science, analytics, or a closely related quantitative field, with a track record of shipping models and analyses that have changed decisions.</li><li>
Strong hands-on skills in SQL and Python or R, and expert use of standard machine learning libraries.</li><li>
Experience building and maintaining production data pipelines using modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and modern data stack tooling (dbt, Airflow, Fivetran, or equivalents).</li><li>
Demonstrated experience designing and analyzing experiments, including A/B tests, quasi-experiments, and causal inference methods.</li><li>
Experience leading rigorous impact or program evaluation that measures real-world outcomes.</li><li>
Experience mentoring junior data scientists.</li><li>
Excellent written and verbal communication skills, with the ability to translate technical work into clear narratives for both technical and non-technical audiences.</li><li>
Demonstrated ability to serve as a senior technical voice within an organization, raising the technical bar of teammates and helping non-technical colleagues use data and AI tools effectively in their work.</li><li><h1 class="jobSectionHeader">
Strong understanding of data governance, privacy, and responsible data and AI practices, with particular sensitivity to data involving children and families.</h1></li><li><h1 class="jobSectionHeader">
Familiarity with product analytics, CRM, and marketing platforms (Amplitude, Google Analytics, Salesforce, Braze) and their underlying data structures is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience building predictive models for subscription, retention, lifetime value, or similar member or customer lifecycle problems is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience implementing and evaluating generative AI or LLM-based solutions, including prompt engineering, fine-tuning, retrieval-augmented generation, or self-hosted models, with rigorous evaluation against human or ground-truth baselines, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience designing AI evaluation frameworks, including human-in-the-loop evaluation, bias and fairness assessment, and ongoing monitoring of model performance, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Prior experience in a mission-driven, nonprofit, education, or media organization is a plus.</h1></li></ul><p>
</p><p><b>WHAT WE OFFER</b></p><ul><li>
The opportunity to really make a difference in the lives of kids and families!</li><li>
A mission-driven team with a 23-year track record of impact for families and children.</li><li>
Competitive nonprofit compensation and comprehensive benefits.</li><li>
A collaborative, flexible work environment with meaningful leadership access and visibility.</li></ul><p>
</p><p><b>ABOUT COMMON SENSE MEDIA</b></p><p>
Common Sense Media is the leading nonprofit organization dedicated to improving the lives of kids and families by providing the research-backed information, education, and independent voice they need to thrive in the age of apps, algorithms, and AI. We rate, educate, and advocate for policies to protect and prepare kids online. Our ratings, research, and resources reach more than 150 million users globally, over 1.4 million educators, and more than 100,000 schools worldwide every year. Learn more at commonsense.org.</p></div><p></p>

Data Scientist

Data Scientist, Analytics (Technical Leadership)

<div>
 As a Data Scientist at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Oculus). By applying your technical skills, analytical mindset, and product intuition to one of the richest data sets in the world, you will help define the experiences we build for billions of people and hundreds of millions of businesses around the world. You will collaborate on a wide array of product and business problems with a wide-range of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others. You will use data and analysis to identify and solve product development's biggest challenges. You will influence product strategy and investment decisions with data, be focused on impact, and collaborate with other teams. By joining Meta, you will become part of a world-class analytics community dedicated to skill development and career growth in analytics and beyond.Product leadership: You will use data to shape product development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and Meta. You will help your partner teams prioritize what to build, set goals, and understand their product's ecosystem.Analytics: You will guide teams using data and insights. You will focus on developing hypotheses and employ a varied toolkit of rigorous analytical approaches, different methodologies, frameworks, and technical approaches to test them.Communication and influence: You won't simply present data, but tell data-driven stories. You will convince and influence your partners using clear insights and recommendations. You will build credibility through structure and clarity, and be a trusted strategic partner.
 <br/>
 <br/>
 Data Scientist, Product Analytics Responsibilities:
 <br/>
 <br/>
 <ul>
  <li>
   Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches
  </li>
  <li>
   Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our products that serve billions of people and hundreds of millions of businesses
  </li>
  <li>
   Identify and measure success of product efforts through goal setting, forecasting, and monitoring of key product metrics to understand trends
  </li>
  <li>
   Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations
  </li>
  <li>
   Partner with Product, Engineering, and cross-functional teams to inform, influence, support, and execute product strategy and investment decisions
   <br/>
   <br/>
  </li>
 </ul>
 Minimum Qualifications:
 <br/>
 <br/>
 <ul>
  <li>
   A minimum of 6 years of work experience in analytics (minimum of 4 years with a Ph.D.)
  </li>
  <li>
   Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R)
   <br/>
   <br/>
  </li>
 </ul>
 Preferred Qualifications:
 <br/>
 <br/>
 <ul>
  <li>
   Master's or Ph.D. Degree in a quantitative field
  </li>
  <li>
   Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  </li>
  <li>
   Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  </li>
  <li>
   Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
   <br/>
   <br/>
  </li>
 </ul>
 About Meta:
 <br/>
 <br/>
 Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today&mdash;beyond the constraints of screens, the limits of distance, and even the rules of physics.
 <br/>
 <br/>
 Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
 <br/>
 <br/>
 Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
 <br/>
 <br/>
 $177,000/year to $247,000/year + bonus + equity + benefits
 <br/>
 <br/>
 Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about  benefits  at Meta.
</div>


Data Scientist, Product Analytics

Plaid

<div><div><h2 class="jobSectionHeader"><b>Location</b></h2>
<p>San Francisco HQ; New York City Office; Seattle Office</p>
</div><div><h2 class="jobSectionHeader"><b>Address</b></h2>
<p>1098 Harrison Street, San Francisco, California, 94103</p>
</div><div><h2 class="jobSectionHeader"><b>Employment Type</b></h2>
<p>Full time</p>
</div><div><h2 class="jobSectionHeader"><b>Location Type</b></h2>
<p>Hybrid</p>
</div><div><h2 class="jobSectionHeader"><b>Department</b></h2>
<p>All DepartmentsData</p>
</div><div><h2 class="jobSectionHeader"><b>Compensation</b></h2>
<ul><li>$211,680 &ndash; $272,160</li>
</ul><div><p>Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.</p></div></div></div>
<br><div><div><p>We believe that the way people interact with their finances will drastically improve in the next few years. We&rsquo;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid&rsquo;s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.</p>
<p>The Data Foundation &amp; AI team sits within Plaid&rsquo;s Data organization and is responsible for building the shared machine learning and AI infrastructure that powers innovation across Plaid&rsquo;s product portfolio. The team transforms Plaid&rsquo;s unique financial network data into scalable, general-purpose representations that can be leveraged by teams throughout the company. Working across the full model lifecycle, the team develops and curates pretraining datasets, trains and evaluates foundation models, and operates production-grade serving and monitoring systems to ensure reliable, high-impact AI capabilities at scale.</p>
<p></p><p>As a Research Scientist, you will advance Plaid&rsquo;s foundation models by developing novel model architectures, pretraining objectives, and fine-tuning strategies that generalize across a broad range of financial and product applications. You will contribute to the design, development, and operation of production machine learning systems, working across the full stack from data and feature engineering to training pipelines, model serving, and monitoring. The role also involves creating comprehensive evaluation frameworks that assess model performance across diverse tasks and use cases, ensuring robust and reliable outcomes. In close partnership with product and engineering teams, you will adapt foundation models to solve specific business challenges, validate their impact through rigorous experimentation, and translate research advances into production capabilities. You will also share insights and results with both internal and external audiences, helping drive innovation and elevate the practice of machine learning and AI across Plaid.</p>
<p></p><p>Responsibilities:</p>
<ul><li><p>Building a foundation model on one of the world&rsquo;s richest financial datasets that no one else has.</p>
</li><li><p>Doing research that ships: moving from experimentation and prototypes to production systems serving real customers.</p>
</li><li><p>Working across the full ML stack, from pretraining objectives and architectures to serving infrastructure and monitoring.</p>
</li><li><p>Collaborating with a high-caliber team and seeing your work amplify the capabilities of multiple product teams.</p>
</li><li><p>Helping hundreds of millions of consumers achieve greater financial freedom through data-driven products.</p>
</li></ul><p>Qualifications:</p>
<ul><li><p>MS or PhD in ML/AI/CS/Stats/Applied Math (or closely related). PhD preferred but not required &mdash; candidates with equivalent industry research and production experience will be considered.</p>
</li></ul><ul><li><p>1-3 years of industry experience building and deploying ML models, with evidence of both research depth and production delivery</p>
</li><li><p>Strong applied ML research skills with production delivery experience</p>
</li><li><p>Depth in Transformers/LLMs, representation learning, or large-scale model training</p>
</li><li><p>Distributed training experience and strong Python + software engineering fundamentals</p>
</li><li><p>Fintech / financial data domain experience - Nice to have</p>
</li><li><p>Demonstrated ability to ship models to production (not just prototype) - Nice to have</p>
</li><li><p>External publications or open-source contributions - Nice to have</p>
</li></ul><p>Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!</p>
<p>Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.</p><br>
<p></p>
<p>Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.</p>
Compensation Range: $211,680 - $272,160</div></div><p></p>

Machine Learning Engineer (Research Scientist) - DFAI

HARVEY

<div><div><h2 class="jobSectionHeader"><b>Location</b></h2>
<p>San Francisco</p>
</div><div><h2 class="jobSectionHeader"><b>Employment Type</b></h2>
<p>Full time</p>
</div><div><h2 class="jobSectionHeader"><b>Location Type</b></h2>
<p>Hybrid</p>
</div><div><h2 class="jobSectionHeader"><b>Department</b></h2>
<p>Product</p>
</div><div><h2 class="jobSectionHeader"><b>Compensation</b></h2>
<ul><li>$155K &ndash; $260K &bull; Offers Equity &bull; Offers Bonus</li>
</ul><div><p><i>Additionally, this role is eligible to participate in our equity plan and benefits program. Benefits include, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits (401k match up to 4%), and flexible PTO.</i></p></div></div></div>
<br><div><h1 class="jobSectionHeader"><b>Why Harvey</b></h1>
<p>At Harvey, we&rsquo;re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we&rsquo;re reshaping how critical knowledge work gets done for decades to come.</p>
<p>This is a rare chance to help build a generational company at a true inflection point. With 1500+ customers in 60+ countries, strong product-market fit, and world-class investor support, we&rsquo;re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth &mdash; personal, professional, and financial &mdash; is unmatched.</p>
<p>Our team moves fast, takes ownership, and is deeply committed to the mission &mdash; operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.</p>
<p>At Harvey, the future of professional services is being written today &mdash; and we&rsquo;re just getting started.</p>
<p></p><h1 class="jobSectionHeader"><b>Role Overview</b></h1>
<p>As a Product Data Scientist, you will be a core member of Harvey's product development organization and one of the foundational members of the Data Science function. You will partner with Product, Engineering, Design, Go-to-Market, and company leadership to define how we measure product success, understand user behavior, and make high-quality decisions in a fast-moving environment.</p>
<p>This role is both strategic and hands-on. You will define north-star and product-level metrics, design experiments and causal analyses, build source-of-truth reporting, and turn ambiguous product questions into clear recommendations. You should be comfortable operating where the data is imperfect, the product is evolving quickly, and the right answer requires both analytical rigor and strong product judgment.</p>
<p></p><h1 class="jobSectionHeader"><b>What You'll Do</b></h1>
<ul><li><p>Embed with Product and Engineering teams as a trusted analytical partner, identifying opportunities to improve user experience, adoption, retention, and business impact.</p>
</li><li><p>Define and maintain core metrics that create a shared understanding of product performance and customer value.</p>
</li><li><p>Design and evaluate experiments, including A/B tests and causal inference methods, to measure the impact of product, model, and workflow changes.</p>
</li><li><p>Analyze product usage, customer segments, and go-to-market signals to surface insights, size opportunities, and inform roadmap decisions.</p>
</li><li><p>Build dashboards, reports, and self-serve tools that help teams answer product questions with confidence.</p>
</li><li><p>Develop models, forecasts, and analytical frameworks to explain user behavior, detect anomalies, and guide prioritization.</p>
</li><li><p>Translate complex analyses into clear recommendations for technical, business, and executive audiences.</p>
</li><li><p>Partner with Engineering and Data teams to improve the infrastructure that powers analytics, experimentation, and decision-making.</p>
</li><li><p>Establish the standards, practices, and culture for Product Data Science at Harvey.</p>
</li></ul><p></p><h1 class="jobSectionHeader"><b>What You Have</b></h1>
<ul><li><p>5+ years of experience in data science, product analytics, economics, statistics, or another quantitative field, ideally in a high-growth product company, AI company, research organization, or similarly ambiguous environment.</p>
</li><li><p>A strong track record of using SQL, Python, and statistical methods to answer product questions and turn analysis into product or business impact.</p>
</li><li><p>Experience defining new metrics and measurement frameworks from scratch, especially for products where usage patterns, customer value, or success criteria are still being discovered.</p>
</li><li><p>Deep fluency in experimentation, causal inference, A/B testing, and statistical modeling, with good judgment about when precision matters and when directional clarity is enough.</p>
</li><li><p>Strong product instincts and curiosity about how users adopt, evaluate, and expand their use of AI-enabled workflows.</p>
</li><li><p>Excellent written and verbal communication skills, including the ability to influence Product, Engineering, Go-to-Market, and executive stakeholders through clear reasoning and compelling data stories.</p>
</li><li><p>Comfort creating structure in fast-moving, ambiguous environments and raising the quality of decision-making for the teams.</p>
</li></ul><p><b>Bonus</b></p>
<ul><li><p>Experience with AI/ML products, large language models, developer tools, enterprise software, or products used in complex professional workflows.</p>
</li><li><p>Experience as an early data science or analytics hire at a hyper-growth startup, including helping define team norms, tooling, and best practices.</p>
</li><li><p>Experience supporting enterprise or B2B products, including analysis of adoption, engagement, retention, expansion, or go-to-market motion.</p>
</li><li><p>Familiarity with modern data infrastructure and the practical tradeoffs involved in building reliable analytics in a rapidly evolving product environment.</p>
</li></ul><p></p><h1 class="jobSectionHeader"><b>Compensation</b></h1>
<p>$155,000 - $260,000</p>
<p></p><h3 class="jobSectionHeader"><b>Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices [</b><b>here</b><b>].</b></h3>
<p></p><p>#LI-SB1</p>
<p></p><p><i>Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.</i></p>
<p>We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai</p></div>

Data Scientist, Product

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>
The Research Data Platform team builds the tools that Anthropic's researchers use every day to manage, query, and analyze the data that goes into training and evaluating frontier models. We power the internal applications researchers rely on to monitor RL runs, explore finetuning datasets, and understand what's happening inside their experiments.</p>
<p>
We're looking for engineers who love working directly with users and who excel at building data products &mdash; the pipelines that move data out of training runs into queryable storage, and the APIs, libraries, and services researchers use to manage and explore it. This role sits closer to the research workflow than a typical data infrastructure position: you'll often embed with research teams, build ML-specific tooling alongside them, and leverage what our Data Infrastructure team has already built rather than reinventing it.</p>
<p>
We do not require prior ML or AI training experience. If you enjoy working closely with technical users, learning new domains quickly, and building tools people actually want to use, you'll pick up the research context fast.</p>
<h2 class="jobSectionHeader"><b>
Responsibilities</b></h2>
<ul><li>Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query</li>
<li>Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis</li>
<li>Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work</li>
<li>Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly</li>
<li>Collaborate with adjacent teams to build on existing systems rather than reinventing them</li>
</ul><h2 class="jobSectionHeader"><b>You may be a good fit if you</b></h2>
<ul><li>Have significant software engineering experience, particularly building data-intensive applications or internal tooling</li>
<li>Enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted</li>
<li>Are results-oriented, with a bias towards flexibility and impact</li>
<li>Pick up slack, even if it goes outside your job description</li>
<li>Want to learn more about machine learning research</li>
<li>Care about the societal impacts of your work</li>
</ul><h2 class="jobSectionHeader"><b>Strong candidates may also have experience with</b></h2>
<ul><li>Large-scale ETL, columnar storage formats, and query engines (e.g., Spark, BigQuery, DuckDB, Parquet)</li>
<li>High-volume time series data &mdash; ingestion, storage, and efficient querying</li>
<li>Data cataloging, lineage, or metadata management systems</li>
<li>ML experiment tracking or metrics platforms</li>
<li>Working in environments where engineers partner closely with quantitative users &mdash; research labs, trading firms, observability or analytics startups</li>
<li>Complex data visualization and full-stack web application development</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 - $405,000 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>

Software Engineer, Research Data Platform

<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 Domain Scaling team has the goal to make Claude world-class at real-world knowledge work in domains like finance, healthcare, and legal. This is a unique role that combines executing directly on applied research and data sourcing (real-world and synthetic) to improve our models. You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance.</p>
<h2 class="jobSectionHeader"><b>
Responsibilities</b></h2>
<ul>
<li><p>Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training</p>
</li>
<li><p>Manage technical relationships with external data vendors, including evaluation of data quality and reward design</p>
</li>
<li><p>Collaborate with domain experts to design data pipelines and evaluations</p>
</li>
<li><p>Explore novel ways of creating RL envs for high value tasks</p>
</li>
<li><p>Develop and improve QA frameworks to catch reward hacking and ensure env quality</p>
</li>
<li><p>Run generalization experiments to measure how data strategy changes improve model capabilities</p>
</li>
<li><p>Partner with other RL research teams and product teams to translate capability goals into training envs and evals</p>
</li>
</ul><h2 class="jobSectionHeader"><b>You may be a good fit if you</b></h2>
<ul>
<li><p>Have experience with fine-tuning large language models for specific domains or real-world use cases</p>
</li>
<li><p>Have experience with reinforcement learning, reward design, or training data curation for LLMs</p>
</li>
<li><p>Are comfortable managing technical vendor relationships and iterating quickly on feedback</p>
</li>
<li><p>Find value in reading through datasets to understand them and spot issues</p>
</li>
<li><p>Have strong cross-functional collaboration skills</p>
</li>
<li><p>Are passionate about making AI more useful and accessible across different industries</p>
</li>
<li><p>Are excited about a role that includes a combination of applied research and hands-on data work</p>
</li>
</ul><h2 class="jobSectionHeader"><b>Strong candidates may also</b></h2>
<ul>
<li><p>Have experience training production ML systems</p>
</li>
<li><p>Have experience designing evals or benchmarks for LLMs</p>
</li>
<li><p>Have domain expertise in a vertical where we would like to make our models more useful</p>
</li>
<li><p>Have experience working with external vendors or technical partners</p>
</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>
$350,000 - $850,000 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>

Research Engineer, Domain Scaling

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