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data scientist

Capgemini

<div><div><div><div>Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you&rsquo;d like, where you&rsquo;ll be supported and inspired by a collaborative community of colleagues around the world, and where you&rsquo;ll be able to reimagine what&rsquo;s possible. Join us and help the world&rsquo;s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
<div><div><div><h2 class="jobSectionHeader">Location</h2>
</div><div>San Francisco CA
</div></div><div><div><h2 class="jobSectionHeader">Your Role</h2>
</div><div>The GenAI Engineer / Data Scientist is responsible for designing, developing, deploying, and maintaining Generative AI (GenAI) and Predictive AI solutions that deliver business value. The role requires expertise in AI/ML, cloud platforms, APIs, data engineering, MLOps, and AI agent workflows, with a strong focus on production-grade AI implementation. 
 
Key Responsibilities 
 
Develop and deploy Generative AI solutions (GPT, Claude, Stable Diffusion) and Predictive AI models (regression, classification, clustering).
 
Design and integrate AI applications using REST APIs, authentication mechanisms, JSON services, and cloud-based architectures.
 
Build and support AI agents, autonomous systems, and agentic workflows.
 
Deploy, monitor, and optimize AI models in production using MLOps practices.
 
Design and maintain data pipelines, data lakes, and data warehouses.
 
Monitor model performance, troubleshoot issues, and manage data/model drift.
 
Collaborate with business and technical teams to align AI solutions with organizational objectives.
 
Create reusable assets, demos, and proof-of-concepts to support innovation and business development. 
 
 
</div></div><div><div><h2 class="jobSectionHeader">Required Skills and Experience
</h2></div><div>A hands-on AI professional with strong expertise in Generative AI, Machine Learning, Python, Cloud Technologies, APIs, MLOps, Data Engineering, and AI Agents, capable of building and scaling enterprise-grade AI solutions from development to production.
 
Bachelor's degree or higher in AI, Machine Learning, Computer Science, or related field.
 
3-5 years of experience in AI, Machine Learning, Data Science, or Application Development.
 
Experience with production AI deployments, MLOps, model monitoring, and cloud platforms.
GPT, Claude LLM, Stable Diffusion
 
LangChain
 
TensorFlow
 
PyTorch
 
Scikit-learn
 
RAG (Retrieval-Augmented Generation)
 
AI Agents &amp; Agentic Workflows
 
API Development
 
REST APIs
 
JSON
 
HTTP Requests
 
Authentication &amp; Tokens
 
Error Handling &amp; Debugging 
 
 
 
 
</div></div></div>The base compensation range for this role in the posted location is $90,000- $160,000
Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.
The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.
These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.
It is not typical for candidates to be hired at or near the top of the posted compensation range.
In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.
Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:
<ul><li>Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave</li>
</ul><ul><li>Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)</li>
<li>Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)</li>
<li>Life and disability insurance</li>
<li>Employee assistance programs</li>
<li>Other benefits as provided by local policy and eligibility</li>
</ul>Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini&rsquo;s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.
Disclaimers
Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.
Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.
Click the following link for more information on your rights as an Applicant in the United States. http://www.capgemini.com/resources/equal-employment-opportunity-is-the-law
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.</div></div></div></div>

Data Scientist/ AI Engineer

Pinterest

<div><div>About Pinterest:

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.

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.

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.

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.</div>
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.

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.

This is a high-autonomy, high-trust individual-contributor role with broad cross-functional reach. 


What you'll do:
<ul>
<li>Own forecasting tooling end to end. 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>Ship product, not just analysis. 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>Drive AI adoption across Finance &amp; BizOps. 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>Stay ahead of the AI capability curve. 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>Set AI strategy and guide executives. 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>Deliver recurring finance analytics. 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>Partner broadly and communicate clearly. 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>Set technical and analytical standards. 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> 
 
What we're looking for:
<ul>
<li>Data science &amp; forecasting</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>Engineering &amp; tool-building</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>Applied AI</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>AI foresight &amp; strategy (critical)</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>Executive influence</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>Scope, ownership &amp; communication</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>Experience</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> 


Relocation Statement:
<ul>
<li>This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.</li></ul> 


In-Office Requirement Statement:
<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> 
</ul>
#LI-NM4

#LI-REMOTE<div>
Our Commitment to Inclusion:
<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>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.</div></div></div>

Staff Data Scientist, Finance & Business Ops

Artificial Analysis

<div>Job Description &ndash; Member of Technical Staff
Location: San Francisco (preferred), Sydney, Melbourne, Brisbane
About Artificial Analysis
Artificial Analysis is the leading independent AI benchmarking company. We support labs, engineers and enterprises to understand AI capabilities and make critical decisions about their AI strategies. We are the go-to authority for understanding AI, from AI labs and enterprises to media, investors, and policymakers. Our benchmarks don&rsquo;t just measure the cutting edge of AI, they are actively shaping the frontier.
Our benchmarks and analysis are trusted by hundreds of thousands of users and are the go-to reference for leading AI labs including OpenAI, Google, Meta, NVIDIA and Anthropic, and major publications including the Wall Street Journal, Bloomberg, the Financial Times and The Economist.
We are a team of 40+, on track to double by end of year, backed by Nat Friedman (GitHub, Meta), Daniel Gross (SSI, Meta), Andrew Ng (Google Brain, DeepLearning.ai, Amazon), Adam D&rsquo;Angelo (Quora, Poe, OpenAI), Clem Delangue (Hugging Face) and other industry leaders.
The Opportunity
Our benchmarks and analysis are what the industry turns to when they need to understand AI capabilities, from AI labs and enterprises to media, investors, and policymakers. This role puts you at the forefront of the AI Frontier &ndash; you won&rsquo;t just observe the cutting edge of AI, your work will define what cutting edge means.
We&rsquo;re hiring Members of Technical Staff to design the evaluations that set the standard for how AI is measured, produce analysis that shapes how companies and the broader industry understand AI, and work directly with the leading AI labs and enterprises who rely on our insights. You&rsquo;ll develop new benchmarking methodologies, manage relationships with some of the most important AI labs and enterprise customers in the world, and help drive the product direction of our platform. The bar for success is becoming a world expert in modern AI technologies.
This is a unique combination of product, research, technical, and client-facing work, suited to highly driven technical generalists who thrive on breadth and ownership. Many of our strongest team members come from top-tier strategy consulting backgrounds, though we hire from a range of disciplines.
You&rsquo;ll work directly with our founders and across the full team. The people who join now will have an outsized role in shaping both our products and the company itself. The coming wave of AI scaling is going to change the world in ways we don&rsquo;t yet understand - and we&rsquo;re offering a front row seat.
What You&rsquo;ll Do<ul><li>AI Benchmarking Product Development: Structure, design and execute projects to evaluate AI systems and technologies, including developing new AI evaluation methodologies and datasets that advance our benchmarking capabilities</li><li>Strategic Analysis of AI: Drive developing reports and data visualizations to communicate complex AI concepts to enterprises looking to shape their AI strategy</li><li>Partner with Leading AI Companies: Collaborate with leading AI companies to support them in benchmarking their technologies, from agentic AI applications to models to hardware. This also requires identifying opportunities to enhance our leading AI benchmarking platform and working with our developers to make it happen</li><li>Become AI-Native: Embrace an AI-native workflow, using cutting-edge AI tools to generate leverage in a fast-changing industry and maintain our competitive edge in AI benchmarking</li><li>Company Strategy: We are a startup and are looking for the future leaders of Artificial Analysis. Team members will be expected to contribute to company strategy and should expect to drive large initiatives that will support us in being the leading AI benchmarking company</li></ul>
What We&rsquo;re Looking For
You have an intense interest in AI, a genuine desire to become a world expert in the field, and strong analytical and coding skills to back it up.
We hire MTS from three backgrounds. You should clearly fit one of these profiles:
Strategy Consulting
Backgrounds include: Management Consultant, Associate, Engagement Manager, Data Scientist, or similar roles at firms like McKinsey, BCG, Bain, or equivalent. Strong preference will be given to candidates with experience within a Data Analytics division such as QuantumBlack, AI by McKinsey, BCG X or equivalent.
You know how to structure ambiguous problems, build analytical frameworks, and communicate findings to senior stakeholders. You&rsquo;re now looking to apply those skills to something deeply technical. The key differentiator: you have a genuine technical interest in AI and ability to code. You follow model releases, you have opinions on where the technology is heading, and you want to be closer to the subject matter than consulting allows.
AI and Machine Learning
Backgrounds include: ML Engineer, ML Researcher, AI Engineer, Forward Deployed Engineer, Technical PM, Solutions Engineer, Technical Pre-Sales, or similar roles at AI companies or AI-focused teams.
You have hands-on experience with modern AI systems and understand how models work at a technical level. You&rsquo;re looking for a role with more breadth and industry exposure than a pure research, engineering, or PM position, where you can combine technical depth with client-facing work and strategic analysis.
Technical Product Management
Backgrounds include: Founding Engineer, Product Manager, Technical Co-founder, Head of Product, or generalist roles at early-stage AI companies.
You&rsquo;ve built and shipped AI products in a fast-moving environment where you had to operate across research, engineering, product, and commercial work simultaneously. You understand both how AI systems work technically and how they get brought to market. You&rsquo;re looking for a role where that breadth is the job, not a side effect of being early at a small company.
Across all three profiles, we require:<ul><li>3+ years of relevant professional experience</li><li>Strong analytical and critical thinking skills</li><li>Proficiency in Python and data analysis</li><li>Genuine, demonstrable interest and knowledge of Frontier AI. We want people who have informed opinions about where AI is heading, not just people who use AI tools</li></ul>
Why Artificial Analysis?<ul><li>Shape how AI gets built: The leading AI labs track our benchmarks and use them to guide their development priorities. Your work will directly influence the direction of AI.</li><li>Become a world expert in AI: You will evaluate every major model, across every major capability, as they are released. Very few roles offer this breadth of exposure to frontier AI.</li><li>Work with the most important players in AI: You&rsquo;ll manage relationships with teams at the leading AI labs and major enterprises as a trusted, independent voice.</li><li>Join at a defining moment: We&rsquo;re 40+ people, on track to double by end of year, backed by some of the most connected investors in AI. The people who join now will shape the product, the team, and the strategy as we scale.</li><li>Competitive compensation including equity</li></ul>
1</div>

Member of Technical Staff

Amazon Web Services

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
Amazon Interactive Video Service (Amazon IVS) is AWS's fully managed live streaming service, built on the same battle-tested technology that powers Twitch and trusted by thousands of enterprises across gaming, social, live commerce, sports, and entertainment. 
 
Our mission: enable developers to build communities around live video. We handle everything from ingest to processing to delivery at global scale, so developers can focus on creating interactive, engaging live experiences for their audiences. Whether you're streaming to tens of millions of concurrent viewers or just getting started, Amazon IVS removes the complexity so developers can move fast. 
 
As a Software Dev Engineer on the IVS's Data team, you will be responsible for empowering internal teams and IVS customers with the data they need to make key operational and business decisions. We operate petabyte-scale data infrastructure for collecting, processing, and warehousing IVS data. We support streaming and batch pipelines to facilitate analytics, reporting, and ML workloads. We offer analytics tools and data products designed to make it easy and fast for our customers to extract the insights they need from their data. 
 
Key job responsibilities 
<ul><li>Design, implement, and maintain scalable data infrastructure in native AWS</li><li>Build and optimize data pipelines (batch and streaming) to support analytics, reporting, and ML workloads</li><li>Ensure data quality, reliability, and observability through monitoring, logging, and automated alerting</li><li>Work closely with product and analytics teams to understand requirements and deliver high-performance solutions</li><li>Champion best practices for security, compliance, and governance in data infrastructure</li><li>Identify opportunities to optimize performance and cost of data platforms</li><li>Stay current with emerging tools, frameworks, and cloud services in the data infrastructure ecosystem</li></ul> 
A day in the life 
Your primary focus will be empowering customers to succeed with their data by ensuring they can: 
<ul><li>Gather and access data at scale &ndash; Enable seamless data collection and retrieval that grows with their needs</li><li>Extract meaningful insights &ndash; Provide the right tools and capabilities to uncover the answers they're seeking</li><li>Trust their data &ndash; Maintain confidence in the completeness, accuracy, and reliability of their information</li></ul>
Achieving these goals requires close collaboration and strong partnerships with Product and Data Science teams to deliver solutions that truly meet customer needs. 
 
About the team 
Amazon Interactive Video Service (Amazon IVS) is a managed live streaming solution from AWS that enables low-latency and real-time video experiences for millions of viewers worldwide. We power engaging live experiences for some of the largest customers in the live video space and continuously push the boundaries of what&rsquo;s possible with interactive video technology.</div><h2 class="jobSectionHeader"> 
BASIC QUALIFICATIONS</h2><ul><li>3+ years of non-internship professional software development experience</li><li>2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>1+ years of software development engineer or related occupational experience</li><li>1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience</li><li>1+ years of Object Oriented Design experience</li><li>Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field</li><li>Experience programming with at least one software programming language</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience</li><li>Bachelor's degree in computer science or equivalent</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company&rsquo;s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, CA, San Francisco - 165,200.00 - 223,600.00 USD annually 
USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually</div>

Software Dev Engineer II, Amazon Interactive Video Service (IVS)

MERCOR

<div><h2 class="jobSectionHeader">About Mercor</h2>
Mercor's mission is to organize human intelligence to power the AI economy. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development. Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $3 million a day.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You&rsquo;ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
About the Role:
Mercor&rsquo;s Data team is looking for a Project Manager to own the end-to-end execution of data initiatives, from intake and scoping through delivery and post-launch handoff. You&rsquo;ll be the connective tissue across data engineering, analytics, and cross-functional stakeholders, ensuring every project is delivered on time, within budget, and at the level of quality our clients and internal teams expect.
What You&rsquo;ll Do:<ul><li>
Own end-to-end project management for data initiatives, including intake and scoping, requirements gathering, readiness reviews, cross-functional approvals, and launch/post-launch handoff.</li><li>
Take direct responsibility for the successful execution of projects, including meeting project goals, client/stakeholder service, quality of work, resource utilization, and profitability.</li><li>
Lead, motivate, and build a strong team environment for project contributors, inspiring a shared vision, enabling others to act, and taking ownership of team outcomes.</li><li>
Partner with team leads to vet, scope, and kick off assignments, and own all due dates and milestones.</li><li>
Develop and maintain accurate project documentation, including timelines, budgets, and scoping documents.</li><li>
Lead communications and change management across the project lifecycle, from kickoff briefings for stakeholders through end-user notifications ahead of launches, ensuring the right people have the right information at the right time.</li><li>
Serve as the primary coordination point across functional teams (Data Engineering, Analytics, Legal, Finance, etc.), aligning workstreams and driving decisions and accountability against timelines.</li><li>
Anticipate and manage the downstream implications of project decisions before they create risk; proactively surface dependencies, resolve blockers, and escalate when needed.</li><li>
Build and continuously improve the infrastructure that makes data project delivery scalable: intake forms, templates, checklists, SOPs/playbooks, and retrospectives that feed into process improvement.</li><li>
Manage relationships with key stakeholders to promote trust and future opportunities.</li></ul>
Knowledge, Skills, &amp; Abilities:<ul><li>
Excellent leadership, communication, and stakeholder management skills.</li><li>
Ability to work collaboratively across departments (engineering, data science, leadership, legal, finance).</li><li>
Superb attention to detail, sound judgment, and strong listening skills.</li><li>
Comfortable driving process improvement and proposing more efficient ways of working.</li><li>
Passion for data-driven products and cross-functional collaboration.</li></ul>
Minimum Qualifications:<ul><li>
3-5+ years of project management experience or related experience.</li><li>
Bachelor&rsquo;s degree or equivalent experience in a related field.</li></ul>
Preferred Qualifications:<ul><li>
Proven track record managing large, cross-functional data or technical projects.</li><li>
Experience managing a high volume of projects in parallel.</li><li>
Experience with data, engineering, or analytics project types.Benefits</li></ul><ul><li>
Bi-annual performance bonus structure</li><li>
Generous equity grant vested over 4 years</li><li>
Up to $15k Relocation bonus</li><li>
$10K housing bonus (if you live within 0.5 miles of our office)</li><li>
$1.5K monthly stipend for meals</li><li>
Free Equinox membership</li><li>
$200 monthly laundry reimbursement</li><li>
$200 monthly personal wellness reimbursement</li><li>
Health, Dental, Vision insurance</li></ul>
Compensation Range: $130K - $180K</div>

Project Manager, Data Team

David Joseph & Company

<div>San Francisco, CA &middot; On-site &middot; Full-time 
Compensation: $180,000&ndash;$220,000 + competitive equity<h2 class="jobSectionHeader">
About the Company</h2>
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.
Founded 2025 &middot; 11&ndash;50 people &middot; Industry: Consumer Tech<h2 class="jobSectionHeader">
The Role</h2>
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.
What you'll be doing<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>
Tech stack: Not specified<h2 class="jobSectionHeader">
Requirements</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">
Green Flags</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">
Red Flags</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">
Why Join</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">
Details</h2><ul><li>
Location: San Francisco, CA</li><li>
Work policy: On-site</li><li>
Compensation: $180,000&ndash;$220,000 + equity</li><li>
Visa sponsorship: O-1, OPT</li><li>
Employment type: Full-time</li></ul></div>

Software Engineer, RL Environments

<div>San Francisco, CA &middot; On-site &middot; Full-time 
Compensation: $130,000&ndash;$150,000 base (up to $300K total cash comp) + equity<h3 class="jobSectionHeader">
About the Company</h3>
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.
Founded 2025 &middot; 11&ndash;50 people &middot; Industry: AI training data &amp; model evaluation infrastructure<h3 class="jobSectionHeader">
The Role</h3>
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.
What you'll be doing<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>
Tech stack: Python; data pipelines, APIs, ML/AI tooling<h3 class="jobSectionHeader">
Requirements</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">
Green Flags</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">
Red Flags</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">
Why Join</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">
Details</h3><ul><li>
Location: San Francisco, CA</li><li>
Work policy: On-site &mdash; strictly in-person</li><li>
Compensation: $130,000&ndash;$150,000 base (up to $300K total cash comp) + equity</li><li>
Visa sponsorship: None available</li><li>
Employment type: Full-time</li></ul></div>

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