In this role, you'll:

• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
• Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling.
• Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
• Monitor, test, and improve data quality, model performance, latency, and cost.
• Partner with product, data science, and security teams to ship resilient, compliant services.
• Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices.
• Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply.

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This is a hands-on, builder-focused engineering role that bridges Product, Design, and Engineering to develop, evaluate, and maintain scalable, low-latency, ML-based systems for real-time applications. You will lead rapid research-to-production cycles that translate business ideas into solutions for complex problems,such as streaming anomaly detection, recommendation systems, predictive modeling, and agentic AI frameworks,with the goal of delivering personalized customer experiences.

In this role, you'll:

Partner with product, UX, and technical stakeholders to analyze business problems, clarify requirements, define scope, and translate them into measurable ML problem statements.

Design, implement, and maintain scalable, enterprise-grade ML solutions in production.

Build reproducible ML workflows for data preparation, training, evaluation, and inference using modern orchestration and MLOps tooling.

Implement monitoring and evaluation frameworks to continuously improve data quality, model performance, latency, and cost through feedback loops.

Partner cross-functionally with Product, Data Science/ML, Engineering, and Security to deliver resilient, scalable, and compliant ML-powered services.

Demonstrate end-to-end systems understanding and articulate the 'why' behind model and system design choices.

Own operational excellence: SLAs, on-call, incident response, customer feedback triage, and blameless post-mortems.

Drive engineering excellence via AI-assisted SDLC, code reviews, automated testing, MLOps best practices, knowledge-sharing, and mentoring.

Actively adopt AI-assisted practices to improve implementation and collaboration efficiency.

Qualifications:

Strong foundation in ML/AI (statistics, probability, optimization) with the ability to apply these concepts to real-world problems.

5+ years of experience building, deploying, and operating data and ML systems in production.

Proficient in Python, Java, and SQL; strong software engineering fundamentals (system design, testing, version control, code reviews).

Hands-on experience with workflow orchestration and data pipelines (e.g., Airflow, Kubeflow) and cloud data platforms/storage (e.g., SageMaker Feature Store, Snowflake, DynamoDB, OpenSearch).

Experience with the ML lifecycle and MLOps tooling (e.g., MLflow, Metaflow, SageMaker; LLM/agent frameworks such as LangChain/LangGraph; model evaluation/observability tools such as Galileo or similar).

Working knowledge of containerization and cloud infrastructure, including Docker and Kubernetes, GitOps/CI/CD tools (e.g., Argo CD), and at least one major cloud platform (AWS, GCP, or Azure).

Understanding of data modeling and scalable systems, including distributed computing and streaming frameworks (e.g., Spark/EMR, Flink, Kafka Streams); familiarity with GPU-based implementation is a plus.

Demonstrated ability to ramp up quickly and operate effectively in new application/business domains.

Strong written and verbal communication skills: able to document and present designs and decisions, and comfortable giving/receiving feedback in an Agile environment.

Desired:

Familiarity with ML problem areas and techniques, including recommendation systems (e.g., graph-based approaches, two-tower models), time-series modeling (classical and deep learning), representation learning (e.g., embeddings), anomaly detection, and causal inference.

Practical experience with LLMs and generative AI workflows, including foundation model fine-tuning, RAG, and vector databases.

Evidence of technical leadership/impact, such as contributions to open-source data/ML projects and/or published technical presentations, blog posts, papers, or research.

Domain experience (plus) in communications, marketing automation, or customer engagement analytics.

Familiarity with AI-assisted development tools (e.g., Claude, GitHub Copilot/Codex, Cursor, etc.).

Advanced degree preferred (M.S. or Ph.D.) in a relevant field.

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We are hiring a Machine Learning Engineer to join our Localization Data & AI team, reporting to the Localization Data & AI Manager. The Loc Data & AI team's mission is to empower EA Localization through intelligent, data-driven solutions,building scalable AI systems, streamlining ML operations, and creating tools that enhance the quality and efficiency of localized content.

This role focuses on designing, deploying, and maintaining ML models and infrastructure, collaborating closely with Data Engineers and Data Scientists.

Responsibilities

• Design, build, and maintain scalable and production-ready ML pipelines to support AI-driven localization workflows.
• Collaborate with cross-functional teams to understand business needs and translate them into ML solutions.
• Train, evaluate, and fine-tune models for NLP, Computer Vision, and other ML use cases.
• Deploy and monitor ML models in different environments, ensuring performance, scalability, and reliability.
• Develop preprocessing pipelines tailored to ML/DL tasks by working with large structured and unstructured datasets in multiple languages.
• Leverage MLOps best practices for versioning, testing, CI/CD, and monitoring of models (e.g., MLflow, Sagemaker, or VertexAI).
• Design, develop, and maintain API REST services using languages such as Python, .NET, and/or Node.js.
• Partner with Data Engineers and Data Scientists to ensure efficient data access and optimized feature engineering processes.
• Contribute to continuous model and system improvement through experiment tracking, feedback loops, and performance analysis.
• Conduct code reviews and ensure high-quality coding standards.
• Optimize applications for maximum speed and scalability.
• Collaborate with cross-functional teams to define, design, and ship new features.
• Ensure adherence to ethical AI and data governance standards.

Qualifications

• 2+ years of hands-on experience in Machine Learning Engineering.
• Bachelor’s degree in Computer Science, Engineering, Applied Mathematics, or related discipline.
• Strong Python programming skills, with experience in ML libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face).
• Proficiency in building and deploying ML models in real-world applications.
• Familiarity with data processing frameworks (Pandas, NumPy) and orchestration tools (Airflow, Prefect).
• Solid understanding of model lifecycle management and MLOps tools (e.g., MLflow, VertexAI, SageMaker, AzureML).
• Experience working with APIs, RESTful services, and microservice-based architecture.
• Knowledge of NLP and Computer vision techniques and tools for multilingual data is a strong plus.
• Experience with cloud services (AWS, Azure, or GCP) for ML/DL development and deployment.
• Experience with WebAPI and RESTful services.
• Knowledge of software engineering best practices and tools (Gitlab and Github), such as Continuous Integration and Version Control (Git).
• Oversee and contribute to the underlying infrastructure that powers ML systems (e.g, Terraform) ensuring robust, maintainable, and secure foundations for scalable deployment.
• Strong debugging skills and fluent in reading code.
• Strong problem-solving skills, and ability to communicate technical concepts clearly with stakeholders.
• Excellent communication and collaboration skills, with the ability to translate data insights into business impact.

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Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.

Samba sits at the heart of Spotify’s personalization engine, powering experiences like autoplay, radio, and personalized mixes. We work on complex sequencing and optimization problems,balancing what users love with how Spotify supports creators and the business.

Our team blends machine learning, backend engineering, and data expertise, and collaborates across North America and Europe to deliver impactful, real-time personalization at scale.

Responsibilities

• Design and build machine learning systems that optimize ranking and sequencing across personalized surfaces

• Develop multi-objective optimization strategies that balance user satisfaction with business outcomes

• Collaborate closely with cross-functional partners including product, data science, and engineering teams to align on goals, share context, and deliver impactful solutions

• Work across ML, backend, and data layers to bring models into production

• Contribute to scalable infrastructure supporting high-volume user interactions

• Run experiments and use insights to continuously improve performance

• Help shape technical direction and raise the bar for engineering excellence within the team

Requirements

• 5+ years of experience in machine learning, data, or backend engineering

• Experienced with production-grade systems and scalable architectures

• Worked on recommendation systems, ranking, or optimization problems

• T-shaped skillset across ML, data, and backend domains

• Comfortable navigating ambiguity and solving complex problems

• Care about user experience and measurable impact

• Enjoy collaborating across disciplines and geographies

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Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.

Samba sits at the heart of Spotify’s personalization engine, powering experiences like autoplay, radio, and personalized mixes. We work on complex sequencing and optimization problems,balancing what users love with how Spotify supports creators and the business.

Our team blends machine learning, backend engineering, and data expertise, and collaborates across North America and Europe to deliver impactful, real-time personalization at scale.

What You'll Do

• Design and build machine learning systems that optimize ranking and sequencing across personalized surfaces

• Develop multi-objective optimization strategies that balance user satisfaction with business outcomes

• Collaborate closely with cross-functional partners including product, data science, and engineering teams to align on goals, share context, and deliver impactful solutions

• Work across ML, backend, and data layers to bring models into production

• Contribute to scalable infrastructure supporting high-volume user interactions

• Run experiments and use insights to continuously improve performance

• Help shape technical direction and raise the bar for engineering excellence within the team

Who You Are

• You have 5+ years of experience in machine learning, data, or backend engineering

• You are experienced with production-grade systems and scalable architectures

• You have worked on recommendation systems, ranking, or optimization problems

• You bring a T-shaped skillset across ML, data, and backend domains

• You are comfortable navigating ambiguity and solving complex problems

• You care about user experience and measurable impact

• You enjoy collaborating across disciplines and geographies

Where You'll Be

• This role is based in London or Stockholm

• We offer you the flexibility to work where you work best! There will be some in-person meetings, but still allows for flexibility to work from home.

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We are looking for a Machine Learning Engineer to join the Personalization team. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction.

Responsibilities

• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development
• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
• Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems

Requirements

• Strong background in machine learning, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes
• Hands-on experience with large cross-collaborative machine learning projects and managing stakeholders
• Hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with PyTorch, Ray, Hugging Face and related tools is required
• Some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS
• Care about agile software processes, data-driven development, reliability, and disciplined experimentation

Where You'll Be

• We offer you the flexibility to work where you work best! For this role, you can be within the North America and EMEA region as long as we have a work location
• This team operates within the Eastern Standard time zone for collaboration

Additional Information

The United States base range for this position is $227,495-$324,993 equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave.

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We are looking for a Machine Learning Engineer to join the Personalization team. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction.

Responsibilities

• Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development
• Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization
• Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems

Requirements

• Strong background in machine learning, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes
• Hands-on experience with large cross-collaborative machine learning projects and managing stakeholders
• Hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages. Experience with PyTorch, Ray, Hugging Face and related tools is required
• Some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS
• Care about agile software processes, data-driven development, reliability, and disciplined experimentation

Where You'll Be

• We offer you the flexibility to work where you work best! For this role, you can be within the North America and EMEA region as long as we have a work location
• This team operates within the Eastern Standard time zone for collaboration

Additional Information

The United States base range for this position is $227,495- $324,993 equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave.

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As a Machine Learning Engineer on our team, you'll design, build, evaluate, and ship LLM-based solutions that give users more adaptive control over their listening experience. You'll work on prompted playlist experiences with a focus on music fulfillment and session generation. You'll collaborate with cross-functional partners across user research, design, data science, product, and engineering. You'll prototype new ML approaches and bring them into production at global scale. You'll build and improve systems that connect artists and fans in personalized and meaningful ways. You'll contribute to the development of scalable ML systems serving hundreds of millions of users. You'll promote best practices in ML system design, testing, evaluation, and deployment across the organization. You'll actively contribute to a strong community of machine learning practitioners at Spotify.

We're looking for experienced machine learning engineers who enjoy solving complex real-world problems in collaborative environments. You should have a strong background in machine learning, natural language processing, and generative AI. You should be comfortable applying theory to build real-world, production-ready applications. You should have hands-on experience building and deploying end-to-end ML systems at scale. You should be familiar with LLM-based systems and techniques for improving them using human feedback such as reinforcement fine-tuning, DPO, or similar approaches. You should have experience designing modular ML architectures and writing technical specifications in partnership with product teams. You should be experienced with large-scale distributed data processing tools such as Apache Beam or Apache Spark. You should have worked with cloud platforms like GCP or AWS.

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In this role, you'll:

• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
• Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling.
• Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
• Monitor, test, and improve data quality, model performance, latency, and cost.
• Partner with product, data science, and security teams to ship resilient, compliant services.
• Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices.
• Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions.

Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply.

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Our Minesweeper squad produces Human Understandable Language Knowledge to enrich music and talk content understanding. We use AI and ML techniques, including Large Language Models, to understand music, podcasts and audiobooks, building reliable, scalable systems to distribute that knowledge to Spotify internal teams, users, and creators.

We are looking for a Machine Learning Engineer to join our team and help build the future of music, podcast and audiobook listening experiences for millions of listeners at Spotify. This is a unique opportunity to help develop and shape Spotify content enrichment, and recommendations.

As a Machine Learning Engineer, you will:

• Utilize in-house and 3rd party LLMs to solve language understanding problems
• Employ techniques such as fine-tuning and RAG to improve models
• Contribute to designing, building, evaluating, shipping, and refining Spotify’s product by hands-on ML development
• Help drive optimization, testing, and tooling to improve quality of our content enrichment assets
• Collaborate with cross-functional teams of MLEs, data and backend engineers, and other stakeholders including tech research, data science, and product to develop new features and technologies
• Perform data analysis to establish baselines and inform product decisions
• Stay up-to-date on the latest machine learning algorithms and techniques

You will be part of a motivated and supportive team that values agile software processes, data-driven development, reliability, and disciplined experimentation.

If you have a strong background in machine learning, especially experience with Large Language Models, and are passionate about fostering collaborative teams, we encourage you to apply.

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