ML Engineer - Personalization & Recommendation Systems Job at Krea (San Francisco)

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

Senior Machine Learning Engineer

As Babylist’s Senior Machine Learning Engineer, you’ll be the founding expert dr...

Location

United States; Canada

Salary:

189900.00 - 237400.00 USD / Year

Babylist

Expiration Date

Until further notice

Requirements

6+ years of experience delivering end to end solutions that drive business growth
Deep expertise in the Python ML ecosystem (pandas, sklearn, xgboost, PyTorch)
Strong expertise creating custom embeddings from raw data sources
Experience going beyond off-the-shelf image/text embeddings to develop domain-specific representations for ML models
Proven track record building and delivering production-grade ML solutions, especially recommender systems or personalization features
Experience with the entire ML lifecycle, including workflow orchestration (Airflow) and model monitoring
Strong communication skills with technical and non-technical stakeholders
Background in consumer-facing products preferred
Expertise with e-commerce, dating apps, or complex user journey platforms is highly relevant
Demonstrate autonomy: able to define problem space, architect solutions from scratch (zero to one), and operate with ownership

Job Responsibility

Pioneer ML at Babylist: shape the roadmap, practices, and culture for machine learning and personalization at scale
Own high-impact work end-to-end: build and launch high-leverage personalization features from the ground up
Lead both technically and strategically: be the technical pioneer and strategic leader for ML at Babylist
Collaborate across a strong data organization: partner with data scientists, data engineers, and analytics engineers

What we offer

Competitive salary with equity and bonus opportunities
Company-paid medical, dental, and vision insurance
Retirement savings plan with company matching and flexible spending accounts
Generous paid parental leave and PTO
Remote work stipend to set up your office
Perks for physical, mental, and emotional health, parenting, childcare, and financial planning

Fulltime

Sr. Principal Software Engineer – Search & Recommendation

We are seeking a Sr. Principal Search & Recommendation Engineer to lead the desi...

Location

United States , Seattle

Salary:

277391.00 - 342391.00 USD / Year

Highspot

Expiration Date

Until further notice

Requirements

8+ years of experience building and scaling search or recommendation systems in production environments
Deep expertise in information retrieval, ranking algorithms, collaborative filtering, and/or neural search techniques
Strong programming skills in Python, Java, or Scala
experience with ML and IR frameworks such as Elasticsearch, FAISS, TensorFlow, or PyTorch
Familiarity with LLMs, embeddings, and modern vector search infrastructure
Proven leadership in cross-functional environments with a track record of mentoring and guiding technical teams
Strong grasp of MLOps practices and experience with cloud-native ML infrastructure (e.g., AWS, GCP)

Job Responsibility

Lead the end-to-end development of modern search and recommendation systems, from architecture to production deployment
Drive technical strategy and innovation in search relevance, personalized ranking, semantic search, and ML-powered retrieval/grounding
Collaborate with product, design, and data teams to define and deliver intelligent user experiences
Influence platform-level decisions on data pipelines, experimentation frameworks, and performance optimization
Mentor engineers, foster technical excellence, and promote a culture of learning and innovation

What we offer

Comprehensive medical, dental, vision, disability, and life benefits
Health Savings Account (HSA) with employer contribution
401(k) Matching with immediate vesting on employer match
Flexible PTO
8 paid holidays and 5 paid days for Annual Holiday Week
Quarterly Recharge Fridays (paid days off for mental health recharge)
18 weeks paid parental leave
Access to Coaches and Therapists through Modern Health
2 volunteer days per year
Commuting benefits

Fulltime

Senior Machine Learning Engineer

Groupon is a marketplace where customers discover new experiences and services e...

Location

Spain , Madrid; Valencia

Salary:

Not provided

Groupon

Expiration Date

Until further notice

Requirements

5–8+ years hands-on experience building and deploying ML models in production, ideally for recommender, ranking, or personalization systems
Expertise in Python (and optionally Java/Scala), ML frameworks (PyTorch, TensorFlow, XGBoost), feature engineering, and data transformation
Solid background in cloud (GCP strongly preferred), container orchestration (Docker, Kubernetes), and modern data/feature pipelines
Skilled at structuring ambiguous problems and navigating fast-changing priorities—ready to build with minimal legacy constraints
Comfortable communicating complex technical concepts in clear, remote team environments (professional English)

Job Responsibility

Lead the full ML model lifecycle—feature engineering, model design, training, deployment, monitoring, and ongoing improvement
Architect and implement scalable ranking, retrieval, and personalization models using state-of-the-art ML frameworks (e.g., PyTorch, TensorFlow)
Build robust, production-ready ML data pipelines and infrastructure (Python, GCP, Docker/Kubernetes)
Integrate ML models into high-traffic distributed systems
ensure observability, CI/CD, and real-time performance
Collaborate closely with Product and Data Engineering to deeply understand business needs and translate them into measurable user impact
Set technical standards and mentor less-experienced colleagues as an emerging ML leader in our scale-up environment
Experiment with advanced techniques (embeddings, deep learning, reinforcement learning) and champion an evidence-driven, AI-first culture

What we offer

Greenfield Impact: Architect the backbone of Groupon’s revitalized search and recommendations from the ground up—with your work seen by millions
AI-First Scale-Up Vibe: Join a driven, supportive team amid exciting transformation—where speed, ambition, and technical influence matter
Career Launchpad: Be the ML architect/leader you’ve always wanted to be, with clear pathways to technical or team leadership as we grow
Global Collaboration: Work cross-functionally with international colleagues and senior leadership. EMEA time zone overlap preferred for maximum impact

New

Senior Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer to lead the design, architectu...

Location

United States; Canada; Mexico , San Francisco; Atlanta; Austin; Boston; Chicago; Dallas; Denver; Houston; Jacksonville; Los Angeles; Mexico City; Miami; New York City; Ottawa; Phoenix; Portland; Seattle; Sacramento; Salt Lake City; San Diego; Toronto; Vancouver; Washington, D.C.

Salary:

129500.00 - 230000.00 USD / Year

Scribd

Expiration Date

Until further notice

Requirements

6+ years of experience as a professional ML or software engineer, with a proven track record of delivering production ML systems at scale
Proficiency in at least one key programming language (preferably Python or Golang
Scala or Ruby also considered)
Expertise in designing and architecting large-scale ML pipelines and distributed systems
Deep experience with distributed data processing frameworks (Spark, Databricks, or similar)
Strong cloud expertise (AWS, Azure, or GCP) and experience with deployment platforms (ECS, EKS, Lambda)
Proven ability to optimize system performance and make informed trade-offs in ML model and system design
Experience leading technical projects and mentoring engineers
Bachelor’s or Master’s degree in Computer Science or equivalent professional experience

Job Responsibility

Lead the design and architecture of ML pipelines, from data ingestion and feature engineering to model training, deployment, and monitoring
Own the technical direction of core ML Platform components such as the feature store, model registry, and embedding-based retrieval systems
Collaborate with product software engineers to deliver ML models that enhance recommendations, personalization, and generative AI features
Guide experimentation strategy, A/B testing design, and performance analysis to inform production decisions
Optimize systems for performance, scalability, and reliability across massive datasets and high-throughput services
Establish and uphold engineering best practices, including code quality, system design reviews, and operational excellence
Mentor and coach ML engineers, fostering technical growth and collaboration across the team
Work with leadership to align technical initiatives with long-term ML strategy

What we offer

Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
12 weeks paid parental leave
Short-term/long-term disability plans
401k/RSP matching
Onboarding stipend for home office peripherals + accessories
Learning & Development allowance
Learning & Development programs
Quarterly stipend for Wellness, WiFi, etc.
Mental Health support & resources
Free subscription to the Scribd Inc. suite of products

Fulltime

Director of AI Engineering

We are entering a hyper-growth phase of AI innovation and are hiring a Director ...

Location

Canada; United States

Salary:

300000.00 - 450000.00 USD / Year

Apollo.io

Expiration Date

Until further notice

Requirements

10–15+ years in software engineering, with significant leadership experience owning AI/ML or applied LLM systems at scale
Proven history shipping LLM-powered features, agentic workflows, or AI assistants used by real customers in production
Deep understanding of LLM orchestration frameworks (LangChain, LlamaIndex), RAG pipelines, vector search, embeddings, and prompt engineering
Expert in backend & distributed systems (Python strongly preferred) and cloud infrastructure (AWS/GCP)
Strong experience with telemetry, observability, and cost-aware real-time inference optimizations
Demonstrated ability to lead senior engineers, define technical roadmaps, and deliver outcomes aligned to business metrics
Experience building or scaling teams working on experimentation, optimization, personalization, or ML-powered growth systems
Exceptional ability to simplify complex problems, set clear standards, and drive alignment across Product, Data, Design, and Engineering
Strong product sense, ability to weigh novelty vs. impact, focus on user value, and prioritize speed with guardrails
Fluent in integrating AI tools into engineering workflows for code generation, debugging, delivery velocity, and operational efficiency

Job Responsibility

Define the multi-year technical vision for Apollo’s AI stack, spanning agents, orchestration, inference, retrieval, and platformization
Prioritize high-impact AI investments by partnering with Product, Design, Research, and Data leaders to align engineering outcomes with business goals
Establish technical standards, evaluation criteria, and success metrics for every AI-powered feature shipped
Lead the architecture and deployment of long-horizon autonomous agents, multi-agent workflows, and API-driven orchestration frameworks
Build reusable, scalable agentic components that power GTM workflows like research, enrichment, sequencing, lead scoring, routing, and personalization
Own the evolution of Apollo’s internal LLM platform for high-scale, low-latency, cost-optimized inference
Oversee model-driven experiences for natural-language interfaces, RAG pipelines, semantic search, personalized recommendations, and email intelligence
Partner with Product & Design to build intuitive conversational UX that hides underlying complexity while elevating user productivity
Implement rigorous evaluation frameworks, including offline benchmarking, human-in-the-loop review, and online A/B experimentation
Ensure robust observability, monitoring, and safety guardrails for all AI systems in production

What we offer

Equity
Company bonus or sales commissions/bonuses
401(k) plan
At least 10 paid holidays per year
Flex PTO
Parental leave
Employee assistance program and wellbeing benefits
Global travel coverage
Life/AD&D/STD/LTD insurance
FSA/HSA

Fulltime

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

ML Engineer - Personalization & Recommendation Systems

Krea

Location:
United States , San Francisco

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
February 21, 2026

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