Applied Scientist - Personalization and Recommendation Job at OnePay

Senior Machine Learning Engineer, Personalization and Recommendations

As a Senior Machine Learning Engineer on the Personalization & Recommendations t...

Location

United States , San Francisco

Salary:

183360.00 - 248000.00 USD / Year

EdTech Jobs

Expiration Date

Until further notice

Requirements

5+ years of experience in applied machine learning or ML-heavy software engineering, with a strong focus on personalization, ranking, or recommendation systems
Demonstrated impact improving key metrics such as CTR, retention, or engagement through recommender or search systems in production
Strong hands-on skills in Python and PyTorch, with expertise in data and feature engineering, distributed training and inference on GPUs, and familiarity with modern MLOps practices — including model registries, feature stores, monitoring, and drift detection
Deep understanding of retrieval and ranking architectures, such as Two-Tower models, deep cross networks, Transformers, or MMoE, and the ability to apply them to real-world problems
Experience with large-scale embedding models and vector search, including FAISS, ScaNN, or similar systems
Proficiency in experiment design and evaluation, connecting offline metrics (AUC, NDCG, calibration) with online A/B test outcomes to drive product decisions
Clear, effective communication, collaborating well with product managers, data scientists, engineers, and cross-functional partners
A growth and mentorship mindset, helping elevate team quality in modeling, experimentation, and reliability
Commitment to responsible and inclusive personalization, ensuring our systems respect learner privacy, fairness, and diverse goals

Job Responsibility

Design and implement personalization models across candidate retrieval, ranking, and post-ranking layers, leveraging user embeddings, contextual signals and content features
Develop scalable retrieval and serving systems using architectures such as Two-Tower models, deep ranking networks, and ANN-based vector search for real-time personalization
Build and maintain model training, evaluation, and deployment pipelines, ensuring reliability, training–serving consistency, observability, and robust monitoring
Partner with Product and Data Science to translate learner objectives (engagement, retention, mastery) into measurable modeling goals and experiment designs
Advance evaluation methodologies, contributing to offline metric design (e.g., NDCG, CTR, calibration) and supporting rigorous A/B testing to measure learner and business impact
Collaborate with platform and infrastructure teams to optimize distributed training, inference latency, and serving cost in production environments
Stay informed on industry and research trends, evaluating opportunities to meaningfully apply them within Quizlet’s ecosystem
Mentor junior and mid-level engineers, supporting technical growth, experimentation rigor, and responsible ML practices
Champion collaboration, inclusion, curiosity, and data-driven problem solving, contributing to a healthy and productive team culture

What we offer

20 vacation days
Competitive health, dental, and vision insurance (100% employee and 75% dependent PPO, Dental, VSP Choice)
Employer-sponsored 401k plan with company match
Access to LinkedIn Learning and other resources to support professional growth
Paid Family Leave, FSA, HSA, Commuter benefits, and Wellness benefits
40 hours of annual paid time off to participate in volunteer programs of choice

Fulltime

New

Principal Applied Scientist

The Core Recommendation Ranking team in Microsoft AI Content Org is looking for ...

Location

United States , Redmond

Salary:

163000.00 - 296400.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience
OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience
OR equivalent experience
Expertise in recommendation systems, ranking models, search relevance, or personalization
Experience applying LLM techniques or Recommendation system
Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow), data processing systems, and cloud‑scale infrastructure
Demonstrated ability to lead cross‑functional initiatives and influence technical direction across multiple teams
Solid communication skills with the ability to articulate complex technical concepts to diverse audiences

Job Responsibility

Architect the next generation of ranking, reranking, and retrieval systems for large‑scale content recommendation scenarios
Lead the design of robust, efficient, and extensible ML/DL models pipelines, including feature engineering, model training, evaluation, and online inference
Establish technical standards and best practices for experimentation, model governance, and system reliability
Drive innovation in model architectures (e.g., deep learning, LLM‑enhanced ranking, multi‑task learning, contextual bandits, reinforcement learning)
Partner with engineering, product, and platform teams to align roadmaps, integrate new capabilities, and ensure seamless end‑to‑end delivery
Invest in others’ growth and mentor team members, fostering a culture of scientific rigor, innovation, and operational excellence
Regularly communicate team progress internally and evangelize progress and opportunities to a wider audience including management and leadership

Fulltime

Machine Learning Engineer

FOX Corporation is looking for a SDE (L2), ML / Senior Engineer, ML to join the ...

Location

India , Bengaluru

Salary:

Not provided

Fox News Media

Expiration Date

Until further notice

Requirements

At least 3-7 years of experience in machine learning, applied data science, or related fields, with a strong focus on recommendation systems or personalization
Demonstrated experience in developing and deploying ML models into production environments
Deep understanding of ranking systems, user behavior modeling, and evaluation techniques (e.g., NDCG, AUC, MAP, CTR)
Proficient in Python and ML libraries like PyTorch, TensorFlow, and frameworks such as Transformers or LightGBM
Experience with Databricks, Spark, or similar big data platforms for large-scale model training and data processing
Familiarity with model versioning, feature stores, experiment tracking, and MLflow
Strong grasp of A/B testing design, analysis, and interpreting results for iterative model improvements
Experience with LLM-based pipelines, semantic search, or vector similarity systems (e.g., FAISS, Vespa) is a plus
Comfort working in cloud-native environments such as AWS or GCP

Job Responsibility

Design and build scalable recommendation and personalization models (ranking, re-ranking, user embeddings, semantic retrieval)
Own the full model lifecycle: from data preparation, training, and evaluation, to versioning, deployment, and monitoring
Develop and maintain continuous training loops and model refresh strategies for dynamic personalization
Set up and interpret A/B experiments to optimize model performance and user engagement
Collaborate with data engineers, MLOps teams, and product managers to ensure models integrate seamlessly into real-time and batch inference pipelines
Leverage platforms like Databricks, MLflow, and feature stores to streamline model experimentation and reproducibility
Apply LLMs and AI agents to improve personalization workflows and accelerate ML development pipelines
Contribute to architecture decisions for personalization services and model serving infrastructure
Mentor and provide technical guidance to junior data scientists and ML engineers, conducting code reviews, sharing best practices, and supporting their growth in areas such as model development, experimentation, and productionization

Research Engineer, GenAI

You will be part of Kiddom’s Data Science team, building the foundation of our s...

Location

United States , San Francisco; New York

Salary:

175000.00 - 250000.00 USD / Year

Kiddom

Expiration Date

Until further notice

Requirements

5+ years of industry experience applying machine learning to solve real-world problems with large, complex datasets
1–2 years in a technical leadership role
Proven track record designing, evaluating, and deploying ML/AI systems in production environments that drive measurable business impact, ideally in recommendation, personalization, search, or workflow optimization
Strong programming skills in Python
Fluency in data manipulation (SQL, Pandas) and common ML toolkits (scikit-learn, XGBoost, TensorFlow/PyTorch)
Strong analytical skills and ability to break down complex problems into measurable hypotheses and experiments
Excellent communication skills with a history of cross-functional collaboration with product, design, and engineering stakeholders

Job Responsibility

Architect and scale machine learning systems for search, personalization, and recommendations that power Kiddom’s teacher helper and insight engine
Develop evaluation-first development workflows to measure how models improve teaching efficiency, lesson planning, and student learning outcomes
Fine-tune machine learning models with feedback signals from teachers and students to align outputs with instructional goals and classroom needs
Design intelligent discovery pipelines that combine semantic retrieval, curriculum alignment, and real-time personalization
Build agentic assistants that help teachers plan lessons, adapt instruction, and reduce repetitive tasks
Collaborate closely with product managers, designers, and curriculum experts to translate high-level educational goals into scalable ML-powered systems
Coach and mentor junior ML engineers and data scientists, fostering technical and professional growth

What we offer

Meaningful equity
Health insurance benefits: medical (various PPO/HMO/HSA plans), dental, vision, disability and life insurance
One Medical membership (in participating locations)
Flexible vacation time policy (subject to internal approval). Average use 4 weeks off per year
10 paid sick days per year (pro rated depending on start date)
Paid holidays
Paid bereavement leave
Paid family leave after birth/adoption. Minimum of 16 paid weeks for birthing parents, 10 weeks for caretaker parents. Meant to supplement benefits offered by State
Commuter and FSA plans

Fulltime

Machine Learning Research Engineer

You will be part of Kiddom’s Data Science team, building the foundation of our s...

Location

United States , San Francisco; New York

Salary:

175000.00 - 250000.00 USD / Year

Kiddom

Expiration Date

Until further notice

Requirements

Have 5+ years of industry experience applying machine learning to solve real-world problems with large, complex datasets, with 1–2 years in a technical leadership role
Proven track record designing, evaluating, and deploying ML/AI systems in production environments that drive measurable business impact, ideally in recommendation, personalization, search, or workflow optimization
Strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and common ML toolkits (scikit-learn, XGBoost, TensorFlow/PyTorch)
Strong analytical skills and ability to break down complex problems into measurable hypotheses and experiments
Excellent communication skills with a history of cross-functional collaboration with product, design, and engineering stakeholders

Job Responsibility

Architect and scale machine learning systems for search, personalization, and recommendations that power Kiddom’s teacher helper and insight engine
Develop evaluation-first development workflows to measure how models improve teaching efficiency, lesson planning, and student learning outcomes
Fine-tune machine learning models with feedback signals from teachers and students to align outputs with instructional goals and classroom needs
Design intelligent discovery pipelines that combine semantic retrieval, curriculum alignment, and real-time personalization
Build agentic assistants that help teachers plan lessons, adapt instruction, and reduce repetitive tasks
Collaborate closely with product managers, designers, and curriculum experts to translate high-level educational goals into scalable ML-powered systems
Coach and mentor junior ML engineers and data scientists, fostering technical and professional growth

What we offer

Competitive salary
Meaningful equity
Health insurance benefits: medical (various PPO/HMO/HSA plans), dental, vision, disability and life insurance
One Medical membership (in participating locations)
Flexible vacation time policy (subject to internal approval). Average use 4 weeks off per year
10 paid sick days per year (pro rated depending on start date)
Paid holidays
Paid bereavement leave
Paid family leave after birth/adoption. Minimum of 16 paid weeks for birthing parents, 10 weeks for caretaker parents. Meant to supplement benefits offered by State
Commuter and FSA plans

Fulltime

Applied Scientist, Amazon Music AI Personalization

Amazon Music is an immersive audio entertainment service that deepens connection...

Location

United States , San Francisco

Salary:

157300.00 - 212800.00 USD / Year

Amazon

Expiration Date

Until further notice

Requirements

Experience programming in Java, C++, Python or related language
Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field

Job Responsibility

Work backwards from customer needs to research and develop novel machine learning approaches for music and spoken word multimodal content generation
Design, develop, evaluate, and deploy scalable AI and ML solutions for customer problems including training LLMs, reasoning models, and diffusion models, agentic and RAG systems, diffusion models
Ship production quality code for models and collaborate with software engineering teams to drive implementation and experimentation in complex Amazon production systems
Improve upon existing methods by developing new data sources, testing model enhancements, and fine-tuning model parameters
Analyze and extract relevant information from large amounts of structured and unstructured, complex and interrelated datasets describing customer behavior and content
Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers
Communicate results, insights and recommendations to audiences of varying levels of technical sophistication
Conduct literature reviews and stay current with advancements in the field, adapting latest in literature to build efficient and scalable models
Promote the culture of experimentation and scientific rigor at Amazon
Contribute to progress of the Amazon and broader research communities by publishing papers

What we offer

health insurance (medical, dental, vision, prescription, Basic Life & AD&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
parental leave

Software Engineer II - Personalization (Growth & Marketing)

The Growth & Marketing organization sits at the intersection of infrastructure, ...

Location

United States , Seattle

Salary:

171000.00 - 190000.00 USD / Year

Uber

Expiration Date

Until further notice

Requirements

3+ years of experience in backend or platform software engineering
Proficiency in at least one modern backend language (e.g., Go, Java)
Strong grasp of computer science fundamentals (data structures, algorithms, OOP)
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

Job Responsibility

Design, build, and operate scalable backend services for large-scale recommendation systems, including candidate generation, real-time content ranking and guardrailing
Partner with Applied Scientists to productionize and iterate on ML models that improve the relevance and quality of personalized content for millions of users
Collaborate closely with Product and Marketing teams to ship features that have a direct impact on user engagement and business growth
Write clean, maintainable code and participate in design reviews, code reviews, and incident responses

What we offer

Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
All full-time employees are eligible to participate in a 401(k) plan
Eligible for various benefits

Fulltime

Senior Machine Learning Engineer

Join us in building the future of finance. Our mission is to democratize finance...

Location

United States , Menlo Park, CA; Bellevue, WA

Salary:

209000.00 - 245000.00 USD / Year

Robinhood

Expiration Date

Until further notice

Requirements

5+ years of applied ML experience productionizing ML models with 2+ years focused on recommendations, ranking or personalization projects
A fervent interest in exploring and applying AI and ML technologies
Strive to solve sophisticated engineering problems that drive business objectives
Solid technical foundation enabling active contribution to the design and execution of projects and ideas
Familiarity with architectural frameworks of large, distributed, and high-scale ML applications
Hands-on experience in classical ML techniques with tabular data as well as modern techniques with sequential data
Proven experience in ML with a focus on ranking, recommendation systems, multi-objective optimization, and reinforcement learning
Proficiency in Python, SQL, XGboost, PyTorch/TensorFlow
Experience with Spark, Kafka, and Kubernetes is also desirable
Ideally you have experience in the Finance sector

Job Responsibility

AI and ML Research: Evaluate cutting technologies, including but not limited to, transformer based model architecture and large foundational models to identify solutions for Robinhood specific problems
Model Development and Implementation: Develop and implement scalable machine learning models focusing on advanced ranking and recommendation systems, including expertise in Collaborative Filtering, Content-Based Filtering, and Hybrid models, alongside proficiency in Learning to Rank (LTR) techniques for effective prioritization. Additionally, design reinforcement learning algorithms and apply multi-armed bandit strategies to optimize decision-making in dynamic environments, balancing exploration and exploitation
A/B Testing and Experimentation: Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance, and analyzing results
Data Analysis and Insight Generation: Analyze experimental data to extract actionable insights. Use statistical techniques to validate the findings and ensure their relevance and accuracy
Cross-Functional Collaboration: Work closely with other engineering teams, data scientists, and the marketing team to integrate machine learning models into the product and ensure they meet business requirements. Present results to different stakeholders
Tooling and Documentation: Build reusable libraries for common machine learning practices. Offer support and guidance to the usage of these tools. Maintain comprehensive documentation of libraries, models, experiments, and findings

What we offer

Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
100% paid health insurance for employees with 90% coverage for dependents
Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
Employer-paid life & disability insurance, fertility benefits, and mental health benefits
Time off to recharge including company holidays, paid time off, sick time, parental leave, and more
Exceptional office experience with catered meals, events, and comfortable workspaces

Fulltime

Applied Scientist - Personalization and Recommendation

OnePay

Location:
United States

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Additional Information:

Job Posted:
February 21, 2026

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