2026 summer internship - third party risk management

data scientist

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2026 summer internship - third party risk management Jobs in New York

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administrative assistant intern

ai enablement analyst

machine learning intern

Fusemachines

<div>
 <strong>
  About Fusemachines
  <br/>
  <br/>
 </strong>
 Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients&rsquo; AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
 <br/>
 <br/>
 Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
 <br/>
 <br/>
 <strong>
  Type: Full-time, Remote
  <br/>
  <br/>
 </strong>
 <strong>
  Role Overview
  <br/>
  <br/>
 </strong>
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;often in partnership with client stakeholders and internal delivery teams.
 <br/>
 <br/>
 You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.
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 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Problem Framing &amp; Stakeholder Partnership
  </li>
  <ul>
   <li>
    Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
   </li>
   <li>
    Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
   </li>
  </ul>
  <li>
   Data Analysis &amp; Feature Engineering
  </li>
  <ul>
   <li>
    Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
   </li>
   <li>
    Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
   </li>
   <li>
    Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
   </li>
  </ul>
  <li>
   Model Development (Core ML)
  </li>
  <ul>
   <li>
    Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)
   </li>
   <li>
    Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
   </li>
   <li>
    Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
   </li>
   <li>
    Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
   </li>
   <li>
    Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
   </li>
  </ul>
  <li>
   Deep Learning
  </li>
  <ul>
   <li>
    Build and train deep learning models using PyTorch or TensorFlow/Keras
   </li>
   <li>
    Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
   </li>
  </ul>
  <li>
   Evaluation, Explainability, and Iteration
  </li>
  <ul>
   <li>
    Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
   </li>
   <li>
    Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
   </li>
  </ul>
  <li>
   Productionization &amp; MLOps (Project-Dependent)
  </li>
  <ul>
   <li>
    Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
   </li>
   <li>
    Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
   </li>
  </ul>
  <li>
   Documentation &amp; Communication
  </li>
  <ul>
   <li>
    Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
   </li>
   <li>
    Create documentation and lightweight demos that make results actionable
    <br/>
   </li>
  </ul>
 </ul>
 Success in This Role Looks Like
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 <br/>
 <ul>
  <li>
   You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
  </li>
  <li>
   Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
  </li>
  <li>
   Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
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  <br/>
 </strong>
 <ul>
  <li>
   3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
  </li>
  <li>
   Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
  </li>
  <li>
   Strong SQL skills (joins, window functions, aggregation, performance awareness)
  </li>
  <li>
   Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
  </li>
  <li>
   Hands-on experience across multiple model types, including:
  </li>
  <ul>
   <li>
    Classification &amp; regression
   </li>
   <li>
    Time series forecasting
   </li>
   <li>
    Clustering/segmentation
   </li>
  </ul>
  <li>
   Experience with deep learning in PyTorch or TensorFlow/Keras
  </li>
  <li>
   Strong problem-solving skills: ability to work with ambiguous goals and messy data
  </li>
  <li>
   Clear communication skills and ability to translate analysis into decisions
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
  </li>
  <li>
   Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
  </li>
  <li>
   Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
  </li>
  <li>
   Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
  </li>
  <li>
   Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
  </li>
  <li>
   Consulting or client-facing delivery experience
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Certifications (Strong Plus)
  <br/>
  <br/>
 </strong>
 Candidates with at least one relevant certification are especially encouraged to apply:
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 <br/>
 <ul>
  <li>
   Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
  </li>
  <li>
   Databricks certifications (Data Scientist, Data Engineer, or related)
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice-to-Have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
  </li>
  <li>
   Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
  </li>
  <li>
   Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  <em>
   Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
   <br/>
   <br/>
  </em>
 </em>
 Important: Immigration Sponsorship Policy
 <br/>
 <br/>
 Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including:
 <br/>
 <br/>
 <ul>
  <li>
   Direct Company Sponsorship: Such as H-1B, J-1, or TN visas
  </li>
  <li>
   Employer of Record: Listing Fusemachines as the immigration employer on any government documentation
  </li>
  <li>
   Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT)
   <br/>
   <br/>
  </li>
 </ul>
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</div>


Machine Learning Engineer / Data Scientist

Capgemini

<div><div><div><div><p>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.</p>
<div><div><div><h2 class="jobSectionHeader"><b>Location: Jersey City, NJ or NYC, NY (Hybrid).</b></h2>
</div></div><div></div><div><div><h2 class="jobSectionHeader"><b>Job Description</b></h2>
</div><div><ul><li>Demonstrated expertise and a strong foundational background in Artificial Intelligence AI and Machine Learning ML including proficiency with current industry trends and methodologies</li>
<li><div>Proven track record of successfully delivering AIML projects from inception to deployment prior experience within the financial sector is highly desirable</div>
</li><li><div>Solid programming skills particularly in Python including scripting development and data manipulation</div>
</li><li><div>Exceptional communication and interpersonal skills with the ability to articulate complex technical concepts and present effectively to diverse audiences across all organizational levels.</div>
</li></ul></div></div></div><p>The base compensation range for this role in the posted location is: 85,000 - 95,000</p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>It is not typical for candidates to be hired at or near the top of the posted compensation range.</p>
<p>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.</p><br>
<p></p><p><b>Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees.</b> In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:</p>
<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><p><b>Important Notice:</b> 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.</p><br>
<p></p><p><b>Disclaimers</b></p>
<p>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.</p>
<p>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.</p>
<p>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.</p>
<p>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</p><br>
<p></p><p>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.</p></div></div></div></div><p></p>

Machine Learning Engineer

OMERS

Choose a workplace that empowers your impact.
<br>Join a global workplace where employees thrive. One that embraces diversity of thought, expertise and experience. A place where you can personalize your employee journey to be — and deliver — your best.
<br><br>
We are a purpose-driven, dynamic and sustainable pension plan. An industry leading global investor with teams in Toronto to London, New York, Singapore, Sydney and other major cities across North America and Europe. We embody the values of our 665,000 members, placing their best interests at the heart of everything we do.
<br><br>
Join us to accelerate your growth& development, prioritize wellness, build connections, and support the communities where we live and work.
<br><br>
Don’t just work anywhere — come build tomorrow together with us.
<br><br>
Know someone at OMERS or Oxford Properties? Great! If you're referred, have them submit your name through Workday first. Then, watch for a unique link in your email to apply.
<br><br>
Reporting directly to theAI Transformation Office, theAI Enablement& Adoption Specialist is a highly approachable, people-centric guide dedicated to accelerating AI fluency across our corporate office. Operating as a hybrid between a hands-on coach and an adoption advocate, you will empower employees to confidently integrate enterprise AI tools—specificallyMicrosoft Copilot,Gemini andClaude —into their daily tasks. You will provide on-demand floor support, lightweight training, and immediate troubleshooting to ensure our teams are maximizing their productivity securely, effectively, and without frustration. This role requires a high degree of initiative and a self-starter mentality — you'll be expected to proactively identify opportunities, engage employees, and drive adoption without waiting to be asked.
<br><br>
We are looking for a student to intern for the Fall term (September to December).
<br><br>
Key Responsibilities
<br><br>
On-Demand Coaching& Support
<br><br>
<ul>
<li>Act as the visible, go-to resource in the office. Provide 1:1 and small-group troubleshooting, prompt refinement, and immediate assistance for employees stuck or unsure how to start withCopilot orGemini orClaude.</li>
</ul>
Training& Enablement
<br><br>
<ul>
<li>Develop and deliver lightweight micro-training, host office hours, and share practical “how-to” guides that demonstrate effective ways to use AI for daily tasks.</li>
</ul>
Adoption Advocacy
<br><br>
<ul>
<li>Monitor tool usage, collect employee feedback, and identify common hurdles. Share internal success stories and practical use cases to organically grow adoption across the organization.</li>
</ul>
Task-Level Integration
<br><br>
<ul>
<li>Help individuals apply AI directly to the work in front of them (e.g., drafting emails, summarizing meetings, generating ideas) without the need for formal, large-scale business process mapping.</li>
</ul>
Responsible AI Guidance
<br><br>
<ul>
<li>Educate staff on the company’s AI governance policies, ensuring data privacy, risk mitigation, and safe guardrails are followed during daily AI use.</li>
</ul>
Qualifications& Skills
<br><br>
Experience
<br><br>
<ul>
<li>Previous background in corporate support, peer coaching, training, IT helpdesk, or technology adoption. We value aptitude, enthusiasm, and a track record of helping others over a strict number of years in enablement.</li>
</ul>
AI Literacy
<br><br>
<ul>
<li>Strong practical familiarity with generative AI tools—specificallyMicrosoft Copilot andGemini andClaude —including prompt engineering basics and a clear understanding of their corporate capabilities and limitations.</li>
</ul>
Communication
<br><br>
<ul>
<li>Exceptional interpersonal skills with the ability to translate technical AI concepts into accessible, actionable advice for non-technical employees.</li>
</ul>
Problem Solving
<br><br>
<ul>
<li>A practical, solution-oriented mindset focused on helping users overcome immediate roadblocks and craft better prompts to get their desired outputs.</li>
</ul>
Empathy& Patience
<br><br>
<ul>
<li>A high degree of emotional intelligence to comfortably guide employees who have varying levels of tech-savviness or potential hesitations about adopting AI tools.</li>
</ul>
The annualized base salary ranges for the primary location and any additional locations are listed below. The base salary that is ultimately offered to the successful candidate will consider a wide array of factors including but not limited to the individual’s skill set.
<br><br>
The expected hourly range for this position is $24.00 - $29.00 per hour.
<br><br>
You may also be eligible to receive an annual Incentive Award pursuant to our Short-term Incentive plan and our Long-Term Incentive plan (if applicable), and to participate in our group benefits and retirement plans – details on these elements of compensation are included within OMERS& Oxford offer letters.
<br><br>
As one of Canada’s largest defined benefit pension plans, our people-first culture is at its best when our workforce reflects the communities where we live and work — and the members we proudly serve.
<br><br>
From hire to retire, we are an equal opportunity employer committed to an inclusive, barrier-free recruitment and selection process that extends all the way through your employee experience. This sense of belonging and connection is cultivated up, down and across our global organization thanks to our vast network of Employee Resource Groups with executive leader sponsorship, our Purpose@Work committee and employee recognition programs.

Intern, AI Enablement & Adoption Specialist (Fall 2026, 4 Months) - New York

Spotify

<div><div><div><div><div><div>Engineering</div></div></div><div><div>
Machine Learning</div></div></div><div><div>
Permanent</div></div><div><div>
New York</div></div><div><div><div>
<div>We design Spotify&rsquo;s consumer experience&mdash;end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints&mdash;from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.<br>
<br>
The Verbatim squad sits within the Enrichment &amp; Content Intelligence product area and is focused on helping Spotify better understand audio, text, and visual content through machine learning. The team develops technologies that power experiences across Spotify including content skipping, transcription, moderation, and visual understanding. Working at the intersection of large-scale machine learning and product innovation, the squad partners closely with Product, Engineering, and Data Science teams to build intelligent systems that improve how users experience content across the platform.</div>
</div></div></div></div><div><div><p>
What You'll Do</p><ul><div>
<ul>
<li>Lead end-to-end machine learning initiatives from ideation and prototyping through experimentation, deployment, and large-scale productionization.</li>
<li>Design, develop, and deploy machine learning systems that operate across hundreds of millions of content signals using both real-time and batch processing architectures.</li>
<li>Advance Spotify&rsquo;s capabilities in natural language understanding, multimodal AI, and content intelligence.<br>
Build and evaluate LLM-powered solutions using modern prompting techniques, retrieval systems, and advanced model orchestration approaches.</li>
<li>Define rigorous evaluation methodologies including golden datasets, precision and recall frameworks, offline benchmarking, and online experimentation.</li>
<li>Partner closely with Product Managers, Engineering Managers, Staff Engineers, and Data Scientists to influence technical strategy and roadmap decisions.</li>
<li>Mentor engineers across the organization and help elevate machine learning engineering standards and best practices.</li>
<li>Contribute to the adoption of AI-assisted development workflows and tooling that improve team productivity and engineering effectiveness.</li>
</ul>
</div></ul></div></div><div><div><p>Who You Are</p><ul><div>
<ul>
<li>You have solid experience developing and deploying machine learning systems in production environments.</li>
<li>You have successfully delivered large-scale machine learning architectures operating on substantial datasets and high-throughput production systems.</li>
<li>You have deep experience with machine learning, deep learning, and modern AI technologies.</li>
<li>You have hands-on experience working with large language models and understand how to evaluate, adapt, and deploy them effectively for real-world product challenges.</li>
<li>You have experience building evaluation frameworks and can quantify model performance through robust experimentation and measurement techniques.</li>
<li>You know how to navigate ambiguity and make thoughtful technical trade-offs that balance product impact, scalability, and engineering quality.</li>
<li>You have experience influencing technical direction across cross-functional teams and can communicate complex machine learning concepts to diverse audiences.</li>
<li>You care about developing others and enjoy mentoring engineers through technical guidance and collaboration.</li>
<li>You have experience working with NLP, prompt engineering, retrieval-augmented generation (RAG), vector databases, or multimodal machine learning systems.</li>
<li>You are curious about emerging AI technologies and excited about integrating tools such as Claude Code, Cursor, and other AI-assisted development capabilities into engineering workflows.</li>
</ul>
</div></ul></div></div><div><div><p>Where You'll Be</p><ul><div>
<ul>
<li>This role is based in New York City</li>
<li>We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.</li>
</ul>
</div></ul></div></div><div><div><p>Learn about life at Spotify</p></div><div><div>
<div>
The United States base range for this position is $184,049&ndash;262,928 USD, plus equity. The benefits available for this position include health insurance, six-month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, paid flexible holidays, and paid sick leave. These ranges may be modified in the future.</div>
</div></div></div><div><div><p>
Our global benefits</p></div><div></div><div><div>
Extensive learning opportunities, through our dedicated team, GreenHouse.</div></div><div></div><div><div>
Flexible share incentives letting you choose how you share in our success.</div></div><div></div><div><div>
Global parental leave, six months off - for all new parents.</div></div><div></div><div><div>
All The Feels, our employee assistance program and self-care hub.</div></div><div></div><div><div>
Flexible public holidays, swap days off according to your values and beliefs.</div></div></div></div><div></div>

Senior Machine Learning Engineer, Content Intelligence

Dagne Dover

<div><p>As a Dagne Dover intern, you&rsquo;ll have the opportunity to learn about real-world product design in the fashion and accessories industry. You&rsquo;ll work with our Product Design team and gain hands-on exposure to how bags and accessories are designed, developed, and brought to life.</p><p>
This internship is ideal for students who are excited about designing bags and accessories and who want to learn how ideas turn into finished products on shelves. If you&rsquo;re curious, motivated, and eager to learn, we&rsquo;d love to hear from you!</p><p><br>
The Role:</p><p>
We&rsquo;re looking for a Fall Product Design Intern. You&rsquo;ll support the design team as they work on new collections and products, and you&rsquo;ll get a behind-the-scenes look at the entire product development process &mdash; from early sketches to samples and production.</p><p>
You may help with:</p><ul><li><p>
Creating and revising technical drawings and files</p></li><li><p>
Supporting team in reviewing and revising samples</p></li><li><p>
Organizing and documenting samples</p></li><li><p>
Managing samples during wear testing periods</p></li><li><p>
Supporting the team with tasks related to product development</p></li></ul><p>
This role focuses on physical product technical design (bags and accessories) and is not a digital, UX/UI, or graphic design internship.</p><p></p><p>
You:</p><ul><li><p>
Are currently studying product design, fashion design, or a related field</p></li><li><p>
Are curious, positive, and eager to learn</p></li><li><p>
Enjoy working in a fast-paced, creative environment</p></li><li><p>
Take initiative and like problem solving</p></li><li><p>
Have some technical design experience through coursework, school projects, or internships</p></li><li><p>
Are comfortable learning new tools and processes</p></li><li><p>
Have working knowledge of Illustrator (we&rsquo;ll teach you more!)</p></li><li><p>
Are highly detail-oriented and highly organized</p></li><li><p>
Are able to commute to our office in Soho for 2-4 days a week</p></li></ul><p>
The expected hourly rate is $20.00.</p><p>
We may ultimately pay more or less than the posted range based on the location of the role. The amount a particular employee will earn within the salary range will be based on several factors including, but not limited to, relevant education, qualifications, experience, skills, performance and business needs.</p><p>
Founded in 2013, Dagne Dover is a digitally native, women-owned brand that designs problem-solving bags for all aspects of life. At its core, our company is aimed at making lives easier through empathy and good design. We love people, and we love making people happy.</p><p><br>
Diversity, Inclusion &amp; Belonging are important to us, as a diverse team ourselves. We&rsquo;ve created a community where everyone&rsquo;s uniqueness is celebrated, respected, and honored. We encourage you to apply if you think it sounds like a good fit for you!</p></div>

Technical Design Intern- Fall'26

Goldman Sachs

<div><div><div><p>Job Duties: Associate, Quantitative Engineering with Goldman Sachs Services LLC in New York, New York. Artificial Intelligence (AI) Quantitative role on Applied AI Team. Deploy AI-based quantitative technologies to drive revenue generation and innovation within the firm. Leverage advanced knowledge in computer science, statistics, artificial intelligence, and machine learning to address unique challenges and redefine possibilities at the intersection of Quantitative Finance and AI. Collaborate with various teams and divisions on pioneering projects that integrate artificial intelligence with quantitative finance, such as Time Series Forecasting, Market making, and Pricing. Address the specific challenges that arise when applying these techniques to the financial sector and push the state-of-the-art in AI for Quantitative Finance. Design, train, and deploy scalable AI models to drive commercial outcomes. Conduct experiments and analysis to enhance model performance. Collaborate effectively with colleagues to advance the production of machine learning systems and applications. Develop, test, and maintain high-quality, production-ready code.</p>
<p>Job Requirements: Master&rsquo;s degree (U.S. or foreign equivalent) in Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field and one (1) year of experience in job offered or a related role OR Bachelor&rsquo;s degree (U.S. or foreign equivalent) in Computer Science, Financial Engineering, Applied Mathematics, or related quantitative field and three (3) years of experience in job offered or a related role. Prior experience must include one (1) year of experience (with a Master&rsquo;s degree) OR three (3) years of experience (with a Bachelor&rsquo;s degree) with: Working in an Artificial Intelligence (AI) Quantitative role; C++, Java or Python programming language; data structures, algorithms, and software engineering practices; machine Learning, Deep Learning, Large Language Models, and Time Series Forecasting algorithms; ML libraries and frameworks, including TensorFlow, PyTorch, scikit-learn, and Keras; big Data Technologies and MLOps tools such as Kubeflow or MLflow in production; and Financial markets, market making, or asset pricing.</p>
<p>Salary Range: Annual base salary for this New York, New York-based position is $113,000 - $155,600.</p>
<p>&copy;The Goldman Sachs Group, Inc., 2026. All rights reserved. Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veteran status, disability, or any other characteristic protected by applicable law.</p>
</div></div><div><div><div>We Offer Best-In-Class Benefits
</div></div><div></div><div><div><div><div><div><div>Healthcare &amp; Medical Insurance
</div><div>We offer a wide range of health and welfare programs that vary depending on office location. These generally include medical, dental, short-term disability, long-term disability, life, accidental death, labor accident and business travel accident insurance.</div>
</div></div></div></div><div></div><div><div><div><div><div>Holiday &amp; Vacation Policies
</div><div>We offer competitive vacation policies based on employee level and office location. We promote time off from work to recharge by providing generous vacation entitlements and a minimum of three weeks expected vacation usage each year.</div>
</div></div></div></div><div></div><div><div><div><div><div>Financial Wellness &amp; Retirement
</div><div>We assist employees in saving and planning for retirement, offer financial support for higher education, and provide a number of benefits to help employees prepare for the unexpected. We offer live financial education and content on a variety of topics to address the spectrum of employees&rsquo; priorities.</div>
</div></div></div></div><div></div><div><div><div><div><div>Health Services
</div><div>We offer a medical advocacy service for employees and family members facing critical health situations, and counseling and referral services through the Employee Assistance Program (EAP). We provide Global Medical, Security and Travel Assistance and a Workplace Ergonomics Program. We also offer state-of-the-art on-site health centers in certain offices.</div>
</div></div></div></div><div></div><div><div><div><div><div>Fitness
</div><div>To encourage employees to live a healthy and active lifestyle, some of our offices feature on-site fitness centers. For eligible employees we typically reimburse fees paid for a fitness club membership or activity (up to a pre-approved amount).</div>
</div></div></div></div><div></div><div><div><div><div><div>Child Care &amp; Family Care
</div><div>We offer on-site child care centers that provide full-time and emergency back-up care, as well as mother and baby rooms and homework rooms. In every office, we provide advice and counseling services, expectant parent resources and transitional programs for parents returning from parental leave. Adoption, surrogacy, egg donation and egg retrieval stipends are also available.</div>
</div></div></div></div><div></div><div><div><div><div><div>Benefits at Goldman Sachs
</div><div>Read more about the full suite of class-leading benefits our firm has to offer.</div></div></div></div></div></div></div></div><div></div>
<br><div>Opportunity Overview
<div></div><div><div><div>CORPORATE TITLEAssociate
</div></div><div></div><div><div>OFFICE LOCATION(S)New York
</div></div><div></div><div><div>DIVISIONGlobal Banking &amp; Markets</div></div></div></div><p></p>

Global Banking & Markets - New York - Associate, Quantitative Engineering - 4195191

Google

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.</li>
<li>5 years of experience programming in Python.</li>
<li>Experience with training machine learning models.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>PhD in Computer Science or equivalent field.</li>
<li>5 years of experience with data structures/algorithms.</li>
<li>Experience publishing in venues (e.g., NeurIPS, ICML, COLT, ICLR) or contributing to open-source projects related to ML.</li>
<li>Experience with ML frameworks (JAX, PyTorch) for large-scale model training.</li>
<li>Experience applying ML research to generative models, specifically in areas of post-training Gemini models.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google&rsquo;s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.</p>
<p></p>
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
<br>
<br>
US: $174000 - $253000 (USD) + 15% bonus target + bonus + equity + benefits<br>
<br>
Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Prototype and test fundamental algorithmic approaches. Write and test product or system development code.<br>
</li>
<li>Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency,) and bring solutions into production environments<br>
</li>
<li>Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.<br>
</li>
<li>Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.<br>
</li>
<li>Design and implement solutions in one or more specialized ML areas such as reinforcement learning, optimization, privacy, and game theory.</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>

Software Engineer, Foundational ML Research

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