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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.
 <br/>
 <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
 <br/>
 <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
  <br/>
  <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

Veho

<div><h3 class="jobSectionHeader"><b>About Veho</b></h3><div></div><p><br>
Veho&rsquo;s mission is to power the future of commerce by making shopping, shipping and returns seamless for everyone.</p><div><br>
</div><p>We are building a modern, end-to-end logistics infrastructure designed entirely for the ever-evolving needs of ecommerce brands and everyday consumers.<br>
</p><div></div><p>Powered by next-generation technology and a vertically integrated supply chain, Veho gives brands and their customers unprecedented control over their deliveries and removes the pain from the ecommerce post-purchase experience. We make delivery the &lsquo;extension of the brand&rsquo; and leverage it to create deeper loyalty and trust between brands and their customers, driving customer retention and lifetime value. Our rapidly growing client list includes leading consumer brands like Hello Fresh, Zara, Macy&rsquo;s, Sephora, and more.</p><div></div><p><br>
To truly build an iconic company, we strongly believe that our people and values must be aligned with our mission. As such, we take pride in our championship team, merit-based culture. We seek team players who want to compete, win, make an impact and build a legacy, and we reward performance and impact players with generous equity and incredible career growth opportunities.
</p><h3 class="jobSectionHeader"><b>About The Role:</b></h3><p>
Veho&rsquo;s Data Science team is core to Veho&rsquo;s ability to deliver millions of packages by creating the systems that drive forecasting, network orchestration, pricing, and routing decisions. Being part of this team, the Machine Learning Operations team drives the foundation of these systems by being a partner to the data scientists to create well-designed, stable, and performant systems.</p><p>
As the Technical Lead Manager, you&rsquo;ll own our Data Science platform and our 1-2 year roadmap for creating a sophisticated and stable platform that keeps up with Veho&rsquo;s rapid growth. You and your team embed into science projects so engineering quality is built in from day one, and create the templates to get new systems up and running quickly. You&rsquo;ll push our AI‑assisted development agenda and be a thought leader for the Veho data and engineering community on how to leverage AI in improving development velocity.</p><p>
You will manage the Machine Learning Operations team and contribute significantly by writing code, reviewing designs, and setting the technical bar. You&rsquo;ll partner closely with our Agentic Developer Experience and Builder Experience teams.</p><p><b>
A great candidate:</b></p><ul><li><p>
Is an expert in their craft, creating high quality ML infrastructure and delivering impactful machine learning models to our stakeholders.</p></li><li><p>
Works in close collaboration with the other Data Science team members and keeps the business value at the center of their work. Has a bias for action, balancing delivering impact in the short-term while building out the long term vision.</p></li><li><p>
Applies their ML / MLOPS knowledge to suggest new patterns, tools, approaches to improve the team&rsquo;s models</p></li><li><p>
Drives team velocity by helping the current team develop in their careers, hiring strong new talent onto the team, and adopting AI as a core part of development.</p></li></ul><p><b>
What you&rsquo;ll do:</b></p><ul><li><p>
Lead and grow a team of four engineers spanning ML infrastructure, ML operations, and embedded data science project work.</p></li><li><p>
Improve our internal ML platform: standardize and improve ML infrastructure, improve how DS services are created, deployed, and operated. Think service performance, permissioning, environment setup, and integration with upstream and downstream systems.</p></li><li><p>
Set the roadmap for improving our Machine Learning and Operations Research infrastructure.</p></li><li><p>
Embed engineers into major science initiatives (forecasting, network orchestration, pricing) so every project is technically sound and lessons learned find their way back into our platform.</p></li><li><p>
Drive AI usage across DS. Collaborate with our Agentic Developer Experience team to ensure new tooling has a high impact on the Data Science team&rsquo;s velocity. Set standards, introduce patterns, and drive adoption of how to leverage AI in data science workflows (EDA, model iteration, ML/OR methodologies)</p></li><li><p>
Be part of the on-call rotation for our data science production systems.</p></li></ul><p><b>
What You Bring:</b></p><ul><li><p>
Bachelor&rsquo;s Degree plus at least 6 years of experience in Machine Learning Engineering, or Master&rsquo;s Degree plus at least 4 years in Machine Learning Engineering:</p></li><li><p>
This experience should include:ML platform experience: training and serving infrastructure, feature stores, orchestration, monitoring, deployment pipelinesexperience managing impactful, high velocity ML Platform / ML Ops teams in smaller scale companiesexperience driving AI/agentic tooling adoption inside an organization</p></li><li><p>
hands‑on experience with open‑source tooling for large‑scale ML (e.g., Ray, Flink, Feast).</p></li><li><p>
strong knowledge of Cloud‑based data engineering and data science tools (AWS preferred) and Data Warehouses (Redshift, Databricks, Snowflake).</p></li><li><p>
Strong proficiency in Python.</p></li><li><p>
Interest in building systems in a Supply Chain setting, enabling a physical supply chain to run like clockwork.</p></li></ul><p>
Veho is a growth company that looks for team members to grow with it. No matter the location, or the role, every Veho teammate shares one galvanizing mission: driving commerce forward with a customer-centric delivery and returns experience that&rsquo;s built for the modern era. We are deeply value-driven (Team Up, Drive Impact, Take Ownership, Solve Bigger, Obsess Over Experience, Make Today Count) and care tremendously about investing in our high-performers.</p><div><br>
</div><p>Join us in building the future of ecommerce logistics and in doing the work of our lifetime!</p><div></div><p><br>
All California applicants please reference our California Applicant Privacy Notice located here.</p><p>
Compensation Range: $199K - $241K</p></div>

Technical Lead Manager, MLOps

Amazon.com

<div><h2 class="jobSectionHeader"><b>DESCRIPTION</b></h2><div>
Amazon Advertising sits at the intersection of e-commerce and advertising, helping brands reach customers across owned surfaces, the open web, and streaming media. We build with the advertiser as the starting point and work backwards from what they need to run effective, measurable campaigns at scale.<br>
<br>
We're looking for a Sr. Software Development Engineer to join the Personalization &amp; Guidance team &mdash; a high-growth team building AI-native systems that make advertising more intelligent, transparent, and effective for the advertisers who rely on them every day.<br>
<br>
Advertising personalization is a well-studied domain. What makes this team's work different is the explainability constraint &mdash; we're building systems where the why behind a recommendation is a first-class product concern, not an afterthought. That drives real architectural decisions: how you store and version the inputs to a decision, how you surface confidence, how you maintain auditability across distributed agent systems, and how you enforce content quality standards at render time rather than at authoring time.<br>
<br>
Key job responsibilities<br>
You'll work on systems that sit at the core of how advertisers understand and act on their advertising performance. The problems are technically hard:<br>
<ul><li>Personalization at scale &mdash; building and maintaining structured models of advertiser behavior, goals, and preferences that update continuously across millions of accounts and inform real-time decisions</li><li>Real-time ML inference &mdash; systems that evaluate and rank recommendations, guidance, and actions at high volume with tight latency constraints across distributed infrastructure</li><li>Explainable AI systems &mdash; infrastructure that makes automated decisions auditable and legible to end users, not just to engineers. This includes stable data models for tracking decision inputs, surfacing confidence signals, and rendering explanations that advertisers can inspect and act on</li><li>Multi-agent coordination &mdash; APIs and contracts that allow multiple specialized AI agents to contribute outputs into a unified experience, with quality enforcement at the delivery layer</li><li>Event-driven data pipelines &mdash; high-volume ingestion, processing, and serving of advertising signals that feed both the ML models and the real-time recommendation systems</li></ul><br>
A day in the life<br>
You'll work in a space where ML infrastructure, distributed systems, and product-quality UX concerns intersect daily.<br>
<br>
Technology You'll Work With:<br>
<ul><li>Large-scale distributed systems built on cloud infrastructure (microservices, event streaming, real-time serving)</li><li>Machine learning infrastructure &mdash; model serving, feature stores, A/B testing frameworks, online evaluation</li><li>Large language models and agentic AI systems &mdash; integrating LLM-powered agents into production advertising workflows, with the reliability and observability requirements that entails</li><li>High-volume data pipelines processing advertising signals across formats and surfaces</li><li>API design for multi-team and multi-agent consumption patterns</li></ul><br>
About the team<br>
What We're Looking For:<br>
<ul><li>Strong distributed systems fundamentals &mdash; you've built services that have to be both fast and correct under real traffic</li><li>Experience with ML infrastructure or production ML-adjacent systems; you understand what it takes to build reliable systems around probabilistic models</li><li>Product instincts &mdash; you care that the output is useful and legible to the person using it, not just that the pipeline ran successfully</li><li>Comfort with ambiguity &mdash; the team is working in a fast-moving space where some of the right architectural decisions are still being established</li><li>Strong written communication &mdash; our most consequential technical decisions live in design documents, and engineers are expected to author them</li></ul><br>
If you want to work on systems that are technically sophisticated, have real daily impact on advertisers around the world, and are building into a space &mdash; AI-driven advertising &mdash; that is genuinely changing how the industry works, this team is worth a conversation.</div><h2 class="jobSectionHeader"><b>
BASIC QUALIFICATIONS</b></h2><ul><li>5+ years of non-internship professional software development experience</li><li>5+ years of programming with at least one software programming language experience</li><li>5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>Experience as a mentor, tech lead or leading an engineering team</li></ul><h2 class="jobSectionHeader"><b>
PREFERRED QUALIFICATIONS</b></h2><ul><li>5+ 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><br>
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.<br>
<br>
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.<p><br>
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.</p><br>
USA, NY, New York - 184,900.00 - 250,200.00 USD annually</div><p></p>

Sr. Software Development Engineer, Guidance & Personalization

Colgate-Palmolive

<div></div><div><div><div>Requisition ID <b>174332</b> - Posted <b>06/18/2026</b> - <b>Analytics</b> - <b>United States</b> - <b>New York</b> - <b>New York</b> - <b>Colgate-Palmolive</b> - <b>Travel - up to 10% of time</b> - <b>On-site</b></div></div><p></p><div><div><div><div><p><b><br>
</b></p></div></div></div></div><br>
<div></div><div><div><div><div><div><div><div>No Relocation Assistance Offered<br>
Job Number #174332 - New York, New York, United States<br>
<br>
<b>Who We Are</b><br>
<br>
Colgate-Palmolive Company is a global consumer products company operating in over 200 countries specializing in Oral Care, Personal Care, Home Care, Skin Care, and Pet Nutrition. Our products are trusted in more households than any other brand in the world, making us a household name!<br>
<br>
Join Colgate-Palmolive, a caring, innovative growth company reimagining a healthier future for people, their pets, and our planet. Guided by our core values&mdash;Caring, Inclusive, and Courageous&mdash;we foster a culture that inspires our people to achieve common goals. Together, let's build a brighter, healthier future for all.</div><br>
<div><b>*This role can sit in our Park Ave (NYC) or Piscataway, NJ office*</b></div><br>
<div></div>
<div><b>
Role Summary</b></div>
<p>
We are seeking a Machine Learning Engineer who brings the analytical rigor of a data scientist and the engineering discipline of a software architect. In support of Colgate-Palmolive&rsquo;s purpose to Make More Smiles and our commitment to a healthier future for our people, pets, and planet, this role builds the advanced machine learning capabilities that power smarter decisions, accelerate innovation, and create measurable impact across our global enterprise.</p>
<p>
As part of the Enterprise AI/ML Center of Excellence, you will lead the architectural design and end-to-end execution of high-priority ML initiatives. This involves integrating statistical modeling, optimization, and autonomous workflows into Colgate-Palmolive's business processes to accelerate innovation, enhance decision intelligence, and embed AI. Beyond hands-on technical work, you ensure solutions are architecturally sound, production-ready, and compliant with enterprise governance standards, translating strategy into robust execution aligned with stakeholder needs and long-term value creation.</p><br>
<div></div>
<div><b>
Responsibilities:</b></div>
<ul>
<li><div>Productionize ML Research: Transition experimental models into robust, scalable production services. You don't just build the model; you build the pipeline that sustains it.</div>
</li>
<li><div>Pipeline Orchestration: Design and maintain complex data and ML pipelines using Airflow and dbt to ensure data integrity and model reliability.</div>
</li>
<li><div>Statistical Rigor: advanced statistical modeling and hypothesis testing to validate models, ensuring outcomes are testable and honest.</div>
</li>
<li><div>DevOps &amp; MLOps: Utilize modern developer tools to work within and CI/CD frameworks for ML and software lifecycle management</div></li></ul><br>
<div>
</div><div><b>Required Qualifications:</b></div>
<ul>
<li><div>Bachelor&rsquo;s Degree (or higher) in a high-rigor field: Statistics, Physics, Chemistry, Mathematics, Data Science, or Computer Science with a heavy emphasis on Statistical Learning.</div>
</li>
<li><div>Experience: Bachelors degree: 6+ of years of technical experience; Masters or PhD (3+ years)</div></li></ul><br>
<div>
</div><div><b>Preferred Qualifications:</b></div>
<ul>
<li><div>Proven expertise in Data Science and/or Machine Learning Engineering.</div>
</li>
<li><div>Advanced proficiency in Python (Production-grade) and SQL.</div>
</li>
<li><div>Hands-on experience with Airflow for orchestration and dbt for transformation.</div>
</li>
<li><div>Familiarity with modern IDEs and Agentic Coding systems (e.g., Cursor, Windsurf, Claude Code, Antigravity) to maximize output velocity.</div>
</li>
</ul><ul><li><div>Modern Stack: Expert knowledge of Python, Scikit-learn, major ML Libraries</div>
</li>
<li><div>Data Engineering: Deep understanding of data lifecycle (ETL/ELT), data architecture, best practices for templatized data transformation</div>
</li>
<li><div>Engineering Excellence: Familiar with Docker/Kubernetes, CI/CD, Git, and &quot;Software Engineering for ML&quot; best practices.</div>
</li>
<li><div>LLM Literacy: Familiar with concepts underpinning LLMs, and strategies to integrate GenAI into MLE project lifecycle</div></li><br>
</ul>
<div><br>
<b>Compensation and Benefits</b><br>
Salary Range $130,000.00 - $170,000.00 USD<br>
<br>
Pay is determined based on experience, qualifications, and location. Salaried employees may also be eligible for discretionary bonuses, profit-sharing, and long-term incentives for Executive-level roles.<br>
<br>
Benefits: Salaried employees enjoy a comprehensive benefits package, including medical, dental, vision, basic life insurance, paid parental leave, disability coverage, and participation in the 401(k) retirement plan with company matching contributions subject to eligibility requirements. Additional benefits include a minimum of 15 vacation/PTO days (hourly employees receive a minimum of 120 hours) and 13 paid holidays (vacation days are prorated based on the employee's hire date within the calendar year). Paid sick leave is adjusted based on role and location in accordance with local laws. Detailed information regarding paid sick leave entitlements will be provided to employees upon hiring and may be subject to adjustments based on changes in legislation or company policies.<br>
<br>
<b>Our Commitment to Inclusion</b><br>
Our journey begins with our people&mdash;developing strong talent with diverse backgrounds and perspectives to best serve our consumers around the world and fostering an inclusive environment where everyone feels a true sense of belonging. We are dedicated to ensuring that each individual can be their authentic self, is treated with respect, and is empowered by leadership to contribute meaningfully to our business.<br>
<br>
<b>Equal Opportunity Employer</b><br>
Colgate is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, sexual orientation, national origin, ethnicity, age, disability, marital status, veteran status (United States positions), or any other characteristic protected by law.<br>
<br>
Reasonable accommodation during the application process is available for persons with disabilities. Please complete this request form should you require accommodation.<br>
<br>
For additional Colgate terms and conditions, please click here.<br>
<br>
#LI-On-site</div></div></div></div></div></div></div></div><p></p>

Machine Learning Engineer (174332)

Tata Consultancy Services (TCS)

<div></div><div><div><div><div><div>We are looking for a hands-on Platform Architect who will design and build the Knowledge Fabric Enablement Layer &mdash; the foundational system that allows teams to define, register, manage, and operationalize enterprise knowledge for Agentic AI. This role focuses on building the infrastructure, APIs, and orchestration capabilities that make knowledge integration, reasoning, and retrieval possible at scale.</div><br>
<div></div><br>
<div>You will architect the services, abstractions, and developer interfaces that let others plug in their own data, ontologies, and retrieval logic &mdash; while ensuring consistency, observability, and governance across the enterprise.</div><br>
<div></div><br>
<div>Key Responsibilities:</div><br>
<div></div><br>
<ul><li> Design the Knowledge Fabric Platform Services
</li></ul><div>Architect and build the underlying services that manage registration, discovery, and access of knowledge assets (e.g., ontologies, embeddings, graphs, data connectors, schemas).</div><br>
<div></div><br>
<ul><li> Develop Knowledge APIs and SDKs
</li></ul><div>Define and implement APIs, SDKs, and connectors for integrating knowledge sources, vector stores, and graph systems into the platform &mdash; enabling agent and workflow-level consumption.</div><br>
<div></div><br>
<ul><li> Implement Fabric Orchestration Layer
</li></ul><div>Build orchestration components for ingestion, transformation, indexing, and synchronization of knowledge entities across internal and external systems.</div><br>
<div></div><br>
<ul><li> Enable Ontology Lifecycle Management
</li></ul><div>Provide tooling and pipelines for ontology versioning, schema evolution, and entity linking so teams can evolve their domain models without disruption.</div><br>
<div></div><br>
<ul><li> Governance &amp; Observability Enablement
</li></ul><div>Build modules that capture metadata, lineage, and provenance for every knowledge operation &mdash; integrating with policy, audit, and TRiSM layers.</div><br>
<div></div><br>
<ul><li> Performance &amp; Scalability Engineering
</li></ul><div>Design for low-latency retrieval and high-throughput ingestion across hybrid and distributed knowledge sources.</div><br>
<div></div><br>
<ul><li> Collaboration &amp; Developer Experience
</li></ul><div>Work closely with AI platform engineers, data fabric teams, and agent developers to ensure the knowledge fabric SDK and APIs are usable, composable, and extensible.</div><br>
<div></div><br>
<div>Qualifications:</div><br>
<div></div><br>
<ul><li>Bachelor&rsquo;s or master&rsquo;s degree in computer science, Engineering, or a related field.</li></ul>
<ul><li>Experience in architecture roles with a focus on manufacturing and industrial applications.</li></ul>
<ul><li> 7+ years in distributed systems or platform engineering, with 3+ years in AI/Knowledge/ML infrastructure.
</li></ul><ul><li> Strong expertise in microservices, event-driven systems, and API platform design.
</li></ul><ul><li> Hands-on experience with one or more of:
</li></ul><ul><li> Vector DBs / Graph Stores: Weaviate, Neo4j, ArangoDB, RedisGraph, Milvus.
</li></ul><ul><li> Knowledge/Metadata Services: OpenMetadata, DataHub, Amundsen, or custom catalog services.
</li></ul><ul><li> Orchestration &amp; Pipelines: Airflow, Dagster, Ray, LangGraph, or similar.
</li></ul><ul><li> Skilled in Python, Go, or TypeScript, with strong fundamentals in API development, caching, and messaging systems (Kafka, SQS, etc.).
</li></ul><ul><li> Familiarity with retrieval architectures (RAG, hybrid retrieval) and semantic search design principles.
</li></ul><ul><li> Experience embedding governance and policy hooks (RBAC, lineage, audit) in data or AI pipelines.
</li></ul><ul><li>Proficient in traditional and modern architectural patterns, software development, and system integration.</li></ul>
<ul><li>Demonstrated ability to think strategically and integrate a broad range of ideas regarding product development.</li></ul>
<ul><li>Excellent communication and interpersonal skills, with the ability to work effectively with cross-functional teams and manage stakeholder expectations.</li></ul>
<ul><li>Proven leadership abilities and experience in working with diverse and globally distributed teams.</li></ul><br>
<div></div><br>
<div>Preferred Qualifications:</div>
<ul><li>Experience with Generative, DL/ML/AI, IoT, and data analytics in a manufacturing environment.</li></ul>
<ul><li>Certifications in cloud architecture (AWS Certified Solutions Architect, Google Cloud Professional Architect, etc.).</li></ul>
<ul><li>Prior experience in agile development environments.</li></ul>
<ul><li> Familiarity with multi-agent orchestration frameworks (LangGraph, ServiceNow Agentic AI, Azure Foundry, Bedrock AgentCore).
</li></ul><ul><li> Prior experience designing developer platforms or internal PaaS for AI or knowledge systems.
</li></ul><ul><li> Exposure to policy enforcement and observability (OpenTelemetry, SPIFFE/SPIRE, OPA).
</li></ul><ul><li> Experience with schema/ontology evolution tooling and event-driven synchronization patterns<br>
</li></ul><div></div><br>
<br>
<div>Salary Range: $132,600 - $179,400 a year</div><br>
<div></div><br>
<br>
<br>
<div>#LI-MG2</div><br>
</div></div></div><div></div><br>
<div><div><div>Location
</div><div>New York, NY
</div></div><div><div>Job Function
</div><div>CONSULTANCY
</div></div><div><div>Role
</div><div>Solution Architect
</div></div><div><div>Job Id
</div><div>419281
</div></div><div><div>Desired Skills
</div><div>Generative AI
</div></div><div><div>Salary Range
</div><div>$132,600-$179,400 a year</div></div></div></div><div></div>

Solution Architect AI

Cloaked

<div><p>Cloaked is a consumer privacy and identity platform on a mission to give people back control of their personal data. We let anyone generate unlimited identities - masked emails, phone numbers, and payment cards - scrub their information off data-broker sites, and shield themselves from spam, scams, and breaches, all from one app. Backed by a $375M Series B, we've protected 10M+ identities and removed 1B+ records from the data brokers who profit off personal information. Now we're building the AI-native future of privacy: autonomous agents that monitor, manage, and defend your digital footprint for you.</p><p></p><p><b>
ABOUT THE ROLE</b></p><p>
We're looking for a <b>Senior Software Engineer, AI</b> to lead the development and strategic direction of <b>Cloaked's AI Agents</b> - autonomous agents that act on a user's behalf to monitor, manage, and enforce their privacy across their entire digital footprint. They find where your data is exposed, take action to remove it, and continuously defend your privacy in the background - turning what's always been a manual, reactive chore into something automated and continuous. 1B+ records removed to date, and now we're automating the entire fight.<br>
<br>
You'll own the technical roadmap end-to-end: agent architecture and orchestration, tool-use and integrations across third-party services, reliability and guardrails for non-deterministic systems, adversarial robustness against hostile web content, and the cloud infrastructure underneath. Long-term, this role grows beyond Agents. You'll contribute across Cloaked's engineering org and help shape how we build AI-native consumer products at scale.</p><p></p><p><b>
WHAT WILL YOU DO?</b></p><ul><li><p>
Lead the technical roadmap and architecture for Cloaked's AI agent platform</p></li><li><p>
Design and build production agents that monitor, manage, and enforce users' privacy across their digital footprint autonomously</p></li><li><p>
Build agents that take real action on users' behalf - submitting data-removal requests, navigating opt-out flows, and adjusting privacy settings across hundreds of services</p></li><li><p>
Build defenses for adversarial environments, including prompt injection, untrusted web content, and hostile data sources</p></li><li><p>
Integrate and evaluate frontier models (LLMs, agent frameworks, tool-use) for production use</p></li><li><p>
Own the orchestration layer, integrations, and the cloud infrastructure the agents run on</p></li><li><p>
Collaborate with design, mobile, and product teams to ship agent experiences that feel effortless to end users</p></li><li><p>
Serve as the AI agents domain expert on the team, raising the engineering bar through code reviews, architecture discussions, and mentorship</p></li><li><p>
Help the team ship features faster and with fewer bugs by setting the technical standard for the product</p></li><li><p>
Actively leverage AI tools in day-to-day engineering work</p></li><li><p>
Contribute across engineering teams beyond Agents as the business scales</p></li></ul><p></p><p><b>
WHAT SKILLS AND EXPERIENCES WILL HELP YOU?</b></p><ul><li><p>
Strong software engineering fundamentals - production systems, distributed systems, real-time infrastructure</p></li><li><p>
Hands-on experience building agentic or LLM-powered products in production</p></li><li><p>
Proficiency with modern agent stacks: LLM orchestration and agent frameworks (LangGraph, LangChain, LlamaIndex, or similar), tool-use/function calling, and RAG</p></li><li><p>
Deep familiarity with LLMs, evals, and prompt engineering in production environments</p></li><li><p>
Experience building reliability and guardrails into non-deterministic systems</p></li><li><p>
Proficiency in Python and/or Go, with a track record of building scalable backend systems</p></li><li><p>
Experience designing and deploying secure, production-grade APIs</p></li><li><p>
Strong understanding of cloud infrastructure (AWS, GCP) and containerized deployments</p></li><li><p>
Track record shipping consumer products at scale</p></li><li><p>
Active daily use of AI coding tools (Cursor, Claude, Codex) with strong prompt judgment and tool selection instincts</p></li></ul><p></p><p><b>
WHAT'S NICE TO HAVE?</b></p><ul><li><p>
Experience with browser automation, headless automation, or web scraping at scale</p></li><li><p>
Background in agent observability, eval frameworks, or LLMOps</p></li><li><p>
Familiarity with adversarial ML, AI safety, prompt-injection defense, or anti-fraud system design</p></li><li><p>
PII handling and sensitive data awareness</p></li><li><p>
Prior exposure to consumer privacy or security-focused products</p></li><li><p>
Experience with workflow orchestration and job scheduling at scale</p></li></ul><p></p><p><b>
WHAT DO WE LIKE?</b></p><ul><li><p>
Passion for security, privacy, and internet ethics</p></li><li><p>
Curiosity and constant learning at the frontier of agentic AI and applied ML</p></li><li><p>
Strong analytical thinking with proactive problem-solving abilities</p></li><li><p>
Ownership mentality - comfortable holding the pen on technical direction</p></li><li><p>
Effective team player in high-velocity environments</p></li><li><p>
Comfortable mentoring and knowledge sharing across teams</p></li><li><p>
Experience in startups or growth-stage companies</p></li><li><p>
Interest in data tracking, analytics, and product instrumentation</p></li></ul><p></p><h2 class="jobSectionHeader"><b>
What We Offer</b></h2><p>
Cloaked is a well-funded Series B startup based out of NYC.</p><p>
Although we are a distributed team, the NYC team operates with a hybrid model. The office building is home to several amenities, including a gourmet cafe, cocktail bar, and a rooftop work area.</p><p>
We have a fully built out kitchen packed with drinks and snacks. The Cloaked team has diverse interests and so we frequently embark on team outings and go out for socials!</p><div><br>
</div><h2 class="jobSectionHeader"><b>
Compensation and Benefits</b></h2><p>
We offer above market rate pay and equity based off of the market&rsquo;s best commercially available data. Your compensation will be a combination of salary, bonus and equity.</p><p>
Cloaked employees have 401K, as well as top of the line Health, Dental, and Vision benefits.</p><p>
We offer flexible work arrangements and the ability to work remotely as needed. Cloaked provides a home office stipend in addition to a new company laptop (and other tech depending on the role).</p><div><br>
</div><h2 class="jobSectionHeader"><b>
Perks</b></h2><p><b>
Competitive PTO: </b>We encourage employees to take a minimum # of vacation per quarter. We see PTO as a preventative burnout measure and are committed to changing the industry standard.</p><p><b>
Monthly health stipend: </b>Used for any kind of physical, mental or emotional care you&rsquo;d like to take for yourself, be it a gym membership, a meditation app, or time with a personal trainer.</p><p><b>
Late Night Meals: </b>We understand that sometimes work can get in the way of meal prep. In response to that, we offer employees a monthly meal stipend to be used when they don&rsquo;t have time to get a home cooked meal going!</p><p><b>
Professional Growth: </b>Opportunities for career development and personal growth are provided to all employees who seek to further their knowledge and capabilities through an unlimited professional development fund. Additionally team members are encouraged to regularly attend conferences and industry events.</p><p>
We are really excited about having you join our mission-driven team and help us build the future of online privacy!</p><div></div><p><br>
Compensation Range: $180K - $250K</p></div>

Senior Software Engineer, AI

JPMorganChase

<div><b>JOB DESCRIPTION</b><br>
<br>
<br>
<div><div><div>The Chief Data and Analytics Office (CDAO) is responsible for building enterprise-scale, cutting-edge platforms for AI/ML and data operations that are used firmwide across JPMorganChase.</div></div><div><div>
</div></div><div><div>As an AI Product Manager (Senior Associate) within our governance organization, you will understand, formulate and execute well-designed, user-friendly product features that encompass AI &amp; data governance frameworks, policies, and procedures. Your role is crucial in ensuring the ethical and compliant application of AI and data technologies across the entire firm. You will be instrumental in integrating AI/ML &amp; Data Management technology into the company's structure, adhering to sustainable best practices in compliance with JPMC technology, operational risk, and relevant regulations. Collaborating with cross-functional teams such as Firmwide CDO, data scientists, engineers, design, legal, compliance, strategy and business units, you will work on AI &amp; data governance initiatives and ensure they align with regulatory requirements and industry best practices.</div></div><div><div>
</div></div><div><div><b>Job Responsibilities</b></div></div><div><ul><li><div>
Design and implement product features that apply evolving AI &amp; data governance frameworks, policies, and procedures to ensure the ethical and responsible use of AI technologies across the organization, ensuring compliance with regulations, standards, guidelines, and emerging regulations.</div></li></ul></div><div><ul><li><div>
Leverage AI-assisted rapid prototyping techniques to validate product requirements, accelerating stakeholder alignment and product discovery.</div></li></ul></div><div><ul><li><div>
Support the product roadmap and backlog; author PRDs and acceptance criteria; define and track KPIs/OKRs to measure adoption, control coverage, lifecycle compliance and streamlined governance.</div></li></ul></div><div><ul><li><div>
Translate AI and data risk into produce requirements and control points embedded into the development lifecycle including model onboarding, model lineage and modification tracking, and agentic systems, data and model risk governance.</div></li></ul></div><div><ul><li><div>
Collaborate with cross-functional stakeholders such as Firmwide CDO, data scientists, engineers, legal, compliance, design and business units to integrate AI &amp; Data governance practices into the AI/ML &amp; data development lifecycle
</div></li></ul></div><div><ul><li><div>Establish monitoring and reporting mechanisms to track compliance with AI &amp; data governance policies and identify areas for improvement.
</div></li></ul></div><div><ul><li><div>Present product updates, risk management and governance findings, and recommendations to senior leaders and stakeholders, ensuring transparency and accountability.</div></li></ul></div><div><div>
</div></div><div><div><b>Required qualifications, capabilities and skills</b></div></div><div><ul><li><div>
3+ years of experience in product management, technology governance, risk management, or compliance within regulated industries such as financial services, with a focus on Artificial Intelligence</div></li></ul></div><div><ul><li><div>
Basic understanding of AI/ML (including GenAI) and data governance, Models, MLOps, Agents, MCP, A2A and technology governance/risk/compliance principles.</div></li></ul></div><div><ul><li><div>
Familiarity with using AI-assisted coding/prototyping tools (GitHub Copilot, Claude Code etc.) to product, iterate on prototypes, PRDs and technical concepts in close partnership with engineering and design.</div></li></ul></div><div><ul><li><div>
Proven experience in business analysis to identify critical requirements by understanding complex and interdependent processes.
</div></li></ul></div></div><div><div><ul><li><div>Strong critical thinking and problem-solving skills, with the ability to identify and mitigate risks effectively.
</div></li></ul></div><div><ul><li><div>Excellent presentation and communication skills, with the ability to convey complex information to senior leaders and stakeholders.
</div></li></ul></div><div><ul><li><div>Proven ability to collaborate effectively across cross-functional teams and build strong working relationships.
</div></li></ul></div><div></div><div><div><b>Preferred qualifications, capabilities and skills</b>
</div></div><div><ul><li><div>Experience with public cloud platforms (e.g., AWS, GCP, Azure) is a plus.</div></li></ul></div><div><ul><li><div>
Demonstrated a track record of building end-to-end prototypes with AI assistance and translating them into engineering-ready acceptance criteria; strong experience with AI coding assistants.</div></li></ul></div><div><ul><li><div>
Computer Science undergrad/grad degree and ideally experience working as an engineer in Financial Services with a focus on building AI products.</div></li></ul></div><div><ul><li><div>
Certifications in AI governance, data governance, or related fields.</div></li></ul></div></div><div><br>
</div><div></div><br>
<b>ABOUT US</b><br>
<br>
<br>
<div><div>JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
</div><p></p><div><p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
</p><div><p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.</p>
<p>JPMorgan Chase &amp; Co. is an Equal Opportunity Employer, including Disability/Veterans</p><br>
</div></div></div><br>
<br>
<b>ABOUT THE TEAM</b>
<br>
<br>
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.</div>

AI Product Manager - Governance (Senior Associate)

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