Full-TimeWho we are:
In an era of rising AI agents and consumer skepticism, Bazaarvoice sources, verifies and amplifies authentic consumer ratings, reviews and visual content at scale, making your products discoverable, trusted, and chosen.
We source, verify, and amplify authentic product ratings, reviews, photos, and videos at scale. Driving reach, traffic, and conversion.
We make products discoverable, trusted, and chosen, by shoppers and by AI.
We are the world’s most trusted network of authentic consumer voices.
Where AI/ML is key:
Our solutions enable brands to connect with consumers and collect valuable user-generated content (UGC), at an unprecedented scale.
This content achieves global reach by leveraging our extensive and ever-expanding retail, social & search syndication network. And we make it easy for brands & retailers to gain valuable business insights from real-time consumer feedback with intuitive tools and dashboards. The result is smarter shopping: loyal customers, increased sales, and improved products.
The problem we are trying to solve: Brands and retailers struggle to make real connections with consumers. It’s a challenge to deliver trustworthy and inspiring content in the moments that matter most during the discovery and purchase cycle. The result? Time and money spent on content that doesn’t attract new consumers, convert them, or earn their long-term loyalty.
At Bazaarvoice, over the past 15 years our software has been at the core of commerce on the web for some of the world’s biggest brands and retailers.
The result is an absolutely massive amount of data, at scales comparable to the most high-traffic individual websites out there. It is critical to the future success of Bazaarvoice that we continue to find ways to leverage that data and turn it into valuable insights for our clients, and better experiences for consumers.
We’re looking for a Senior ML Engineer to join our Machine Learning team.
You’ll work closely with a team of engineers to create AI/ML solutions on top of our extensive syndication network data. These features will increase our Annual Recurring Revenue (ARR), reduce client churn, and make our Bazaarvoice User Generated Content (UGC) central to AI Shopping.
Key Responsibilities:
- Design, implement, and maintain robust AI/ML solutions and tooling for both batch and streaming ML pipelines.
- Develop and manage monitoring and observability solutions for ML systems.
- Lead DevOps practices, including CI/CD pipelines and Infrastructure as Code (IaC).
- Architect and implement cloud-based solutions on AWS (or similar public cloud providers).
- Collaborate with ML Engineers and Data Scientists to develop, train, and deploy machine learning models.
- Engage in feature engineering and model optimization to improve ML system performance.
- Participate in the full AI/ML lifecycle, from data preparation to model deployment and monitoring.
- Optimize and refactor existing systems for improved performance and reliability.
- Drive technical initiatives and best practices in both MLOps and ML Engineering.
Required Skills and Experience:
- Strong Python Proficiency: Excellent skills for developing, deploying, and maintaining our machine learning systems.
- Language Versatility: 5+ Years of Experience with statically-typed or JVM languages. Willingness to learn Scala is highly desirable.
- Cloud Engineering Skills: 5+ years experience with Cloud Platforms & Services, ideally AWS (e.g., Lambda, ECS, ECR, CloudWatch, MSK, SNS, SQS).
- Infrastructure as Code: 3+ Years Proficiency in IaC, particularly Terraform.
- Kubernetes Expertise: 5+ years of hands-on experience with managing clusters and deploying services.
- Data Orchestration: 5+ years of Experience with ML orchestration tools (e.g., Flyte, Airflow, Kubeflow, Luigi, or Prefect).
- CI/CD: 5+ Years of Expertise in pipelines, especially GitHub Actions and Jenkins.
- Networking: Knowledge of concepts and implementation.
- Streaming: Experience with Kafka and other streaming technologies.
- ML Monitoring: Familiarity with observability tools (e.g., Arize AI, Weights and Biases).
- NLP/LLMs: Experience with NLP, LLMs, and RAG systems in production, or strong desire to learn.
- CLI & Shell Scripting: Proficiency in scripting and command-line tools.
- APIs: Experience with deploying and managing production APIs.
- Software Engineering Best-Practices: Knowledge of industry standards and practices.
Preferred Qualifications:
- AWS AI Services: Hands-on experience with AWS SageMaker and/or AWS Bedrock.
- Data Processing: Experience with high-volume, unstructured data processing.
- ML Applications: Familiarity with NLP, Computer Vision, and traditional ML applications.
- System Migration: Previous work in refactoring and migrating complex systems.
- AWS Certification: AWS Solution Architect Professional or Associate certification.
- Advanced Degree: Master's degree in ML / AI / Computer Science.
Personal Qualities:
- Passionate about building developer-friendly platforms and tools.
- Thrives in a terminal-based development environment.
- Enthusiastic about creating production-grade, robust, reliable, and performant systems.
- Not afraid to dive into and improve complex existing solutions.
- Team player who works well with ML Engineers, Data Scientists, and management.
- Strong technical mentoring skills.
- Excellent problem-solving and communication skills.
Skills Required
- Strong Python proficiency for developing, deploying, and maintaining machine learning systems
- 5+ years of experience with statically typed or JVM languages
- 5+ years of experience with cloud platforms and services, ideally AWS
- 3+ years of proficiency with Infrastructure as Code, particularly Terraform
- 5+ years of hands-on experience managing Kubernetes clusters and deploying services
- 5+ years of experience with ML orchestration tools such as Flyte, Airflow, Kubeflow, Luigi, or Prefect
- 5+ years of expertise with CI/CD pipelines, especially GitHub Actions and Jenkins
- Knowledge of networking concepts and implementation
- Experience with Kafka and other streaming technologies
- Familiarity with ML observability and monitoring tools such as Arize AI or Weights & Biases
- Production experience with NLP, LLMs, and RAG systems, or strong willingness to learn
- Proficiency in CLI tools and shell scripting
- Experience deploying and managing production APIs
- Knowledge of software engineering best practices
- Hands-on experience with AWS SageMaker and/or AWS Bedrock
- Experience processing high-volume, unstructured data
- Familiarity with NLP, computer vision, and traditional machine learning applications
- Experience refactoring and migrating complex systems
- AWS Solutions Architect Professional or Associate certification
- Master’s degree in machine learning, artificial intelligence, or computer science
Bazaarvoice Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Bazaarvoice and has not been reviewed or approved by Bazaarvoice.
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Leave & Time Off Breadth — Time off is considered flexible, with unlimited PTO and paid volunteer time available alongside a hybrid/flexible-remote setup. Paid sabbaticals after a tenure milestone are highlighted as part of the package.
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Parental & Family Support — Paid parental leave for primary caregivers is consistently presented as a strong component of the offering. Family medical leave options are also documented.
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Wellbeing & Lifestyle Benefits — Mental health support via a dedicated platform, recognition programs, ERGs, and a companywide volunteering tradition are emphasized. Company events and regular all-hands reinforce a wellbeing and community focus.
Bazaarvoice Insights
What We Do
Each month in the Bazaarvoice Network, more than a billion consumers create, view, and share authentic user-generated content including reviews, questions and answers, and social photos across more than 11,500 global brand and retailer websites. From search and discovery to purchase and advocacy, Bazaarvoice’s solutions help brands and retailers reach in-market shoppers, personalize their experiences, and give them the confidence to buy.
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