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senior lead machine learning engineer

Salesforce

<div><p><i>To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
</i></p><p></p><p>Job Category
</p>Software Engineering
<p></p><p>Job Details
</p><p></p><p><b>About Salesforce
</b></p><p>Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn&rsquo;t a buzzword &mdash; it&rsquo;s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
</p><p></p><p>Ready to level-up your career at the company leading workforce transformation in the agentic era? You&rsquo;re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
</p><p></p><p><b>About the Role
</b></p><p></p><p>We are seeking a highly skilled Machine Learning Engineer to design, build, and productionalize models that drive customer growth, engagement and retention. This role will focus specifically on attrition prediction and mitigation - identifying customers at risk of churn and surfacing proactive interventions that improve customer satisfaction and lifetime value.
</p><p></p><p>You will work closely with data scientists, software engineers, product managers, and business stakeholders to build scalable ML systems that power attrition predictions, risk and mitigation explanations and next best action recommendations.
</p><p></p><p><b>Key Responsibilities:
</b></p><ul><li><p>Design predictive models for user and customer attrition using supervised, unsupervised, deep learning and generative techniques.
</p></li><li><p>Design scalable data pipelines for feature generation from both structured and unstructured sources of product adoption, sales activity, and customer engagement data (e.g. product telemetry, usage logs, CRM, sales activity, etc.)
</p></li><li><p>Build and maintain production-grade ML services , integrating models into APIs or decision systems that support real-time and batch use cases.
</p></li><li><p>Continuously monitor and improve model performance through drift detection, retraining automation and impact measurement.
</p></li><li><p>Collaborate with product and engineering teams to integrate models into production systems and agentic experiences, ensuring scalability, robustness and efficiency.
</p></li><li><p>Mentor junior engineers and data scientists and provide technical leadership in model architecture, experimentation, and deployment best practices.
</p></li></ul><p></p><p><b>What We&rsquo;re Looking For
</b></p><ul><li><p>Demonstrated ability to take models from research to production
</p></li><li><p>Strong software engineering proficiency in Python and data manipulation skills like SQL.
</p></li><li><p>Experience using third-party and in-house Machine learning tools and infrastructure to develop reusable, high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.
</p></li><li><p>Exposure to architectural patterns of a large, high-scale software application (e.g. well-designed APIs, high volume data pipelines, efficient algorithms, etc.)
</p></li><li><p>Familiarity with ML libraries such as scikit-learn, XGBoost, Pytorch, or TensorFlow
</p></li><li><p>Experience with feature engineering on big data (Spark, Trino, Snowflake, etc.)
</p></li><li><p>Experience with ML lifecycle management tools (ML Flow, Airflow, Kubeflow or equivalents).
</p></li><li><p>Experience with containerization technologies (Docker) and orchestration (Kubernetes).
</p></li><li><p>Strong grasp of model evaluation, drift monitoring and explainability best practices.
</p></li><li><p>Experience with Agile development methodology, Test-Driven Development, incremental delivery, and CI/CD
</p></li><li><p>Experience owning and operating services throughout the software development lifecycle including design, development, release and maintenance.
</p></li><li><p>Experience communicating technical vision, mentoring junior engineers and managing projects.
</p></li><li><p>Experience developing and evaluating AI Agents that integrate with traditional ML models, (e.g. combining predictive scoring systems with generative or agentic workflows to automate customer engagement flows and recommendations.
</p></li></ul><p></p><p><b>Preferred Qualifications (Bonus Points):
</b></p><ul><li><p>Familiarity with retention modeling or next best action recommendation systems.
</p></li><li><p>Experience developing or contributing to shared ML frameworks or internal ML Ops platforms.
</p></li><li><p>Experience with Feature Stores like Feast
</p></li></ul><p></p><p>Unleash Your Potential
</p><p>When you join Salesforce, you&rsquo;ll be limitless in all areas of your life. Our benefits and resources support you to find balance and <i>be your best </i>, and our AI agents accelerate your impact so you can <i>do your best </i>. Together, we&rsquo;ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future &mdash; but to redefine what&rsquo;s possible &mdash; for yourself, for AI, and the world.
</p><p></p><p>Accommodations
</p><p>If you require assistance due to a disability applying for open positions please submit a request via this Accommodations Request Form .
</p><p></p><p>Posting Statement
</p><p>Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that&rsquo;s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications &ndash; without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
</p><p></p>In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.
<p></p>For New York-based roles, the base salary hiring range for this position is $137,100 to $334,600.</div><p></p>

Senior/Lead Machine Learning Engineer

Spotify

<div><div><div><div><div><div>Engineering</div></div></div><div><div>
Machine Learning</div></div></div><div><div>
Permanent</div></div><div><div>
New York</div></div><div><div>
Our mission on the Advertising Product &amp; Technology team is to build a next generation advertising platform that aligns with our unique value proposition for audio and video. We work to scale the user experience for hundreds of millions of fans and hundreds of thousands of advertisers. This scale brings unique challenges as well as tremendous opportunities for our artists and creators.</div></div><div><div><div><br>
</div><div>We are seeking a Senior Machine Learning Engineer to join the Ad Engagement squad. Ad Engagement focuses on using machine learning to accurately predict how Spotify listeners will react to ads, helping advertisers minimize their costs while delivering a more relevant and enjoyable ad experience for listeners. Our core innovations include Multi-Task Learning models (MTL), and we are expanding into scalable sequence modeling with complex transformer architectures. Recently, we presented a paper about this in KDD Toronto and you can check out the latest details in the blog post here.</div><div><br>
</div><div>We are also seeking a Senior Machine Learning Engineer to join the Supply Personalization squad. Supply Personalization focuses on optimizing the volume, timing, and types of ad loads a user receives. By leveraging data, machine learning, causal inference, and large scale online experimentation, we aim to uncover and learn the most effective strategies for enhancing user experiences and driving business outcomes.</div><div><br>
</div><div>We are looking for someone with strong expertise in data analysis, online experimentation techniques, and large-scale ML and engineering systems; someone who is motivated by user and business problems as much as they are by technical problems, and who thrives under ambiguity, experimentation, and iteration. You will work directly on an array of product features that drive the optimal user experience for our ads. You will collaborate with our cross-functional teams to ideate, develop, and own complex technical solutions on our ad services technology platforms. As someone who shares our passion for building innovative ad experiences, you'll have a direct impact on how the world uses Spotify.</div></div></div></div><div><div><p>
What You'll Do</p><ul><li>
Design and implement machine learning systems for ad performance optimization.
</li><li>Research and apply ML optimization strategies to balance multiple objectives effectively.</li><li>
Work on a paradigm shift modeling work for Ads that involves working on Sequence Transformer model for User-Ad Interaction, that works along a MTL model</li><li>
Analyze data and use machine learning techniques to understand user behavior and improve ad experiences.</li><li>
Collaborate with backend engineers, data scientists, data engineers, and product managers to establish baselines, inform product decisions, and develop new technologies.</li></ul></div></div><div><div><p>
Who You Are</p><ul><li>
You have professional experience in applied machine learning.</li><li>
You have strong technical expertise in software engineering, data analysis, and machine learning.</li><li>
You are proficient in programming languages such as Python, Java, or Scala.</li><li>
Experienced in Tensorflow or PyTorch and working with various aspects of the ML lifecycle</li><li>
You have expertise in developing data pipelines using tools like Apache Beam or Spark.</li><li>
As a plus, you may have experience with any of the following - LLMs, Ray, Adtech, or Recommender Systems.</li></ul></div></div><div><div><p>
Where You'll Be</p><ul><li>
We offer you the flexibility to work where you work best! For this role, you can be within the Americas region as long as we have a work location.</li><li>
This team operates within the U.S. Eastern time zone for collaboration.</li></ul></div></div><div><div><p>
Learn about life at Spotify</p></div><div><div>
The United States base range for this position is $176,166.00 - $251,666.00, plus 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. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.</div><div></div><div><br>
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what&rsquo;s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It&rsquo;s in our differences that we will find the power to keep revolutionizing the way the world listens.</div><div></div><div><br>
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we&rsquo;re here to support you in any way we can.</div><div></div><div><br>
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world&rsquo;s most popular audio streaming subscription service.</div></div></div><div></div><div><div><p><br>
Our global benefits</p></div><div></div><div><div>
Extensive learning opportunities, through our dedicated team, GreenHouse.</div></div><div></div><div><div>
Flexible share incentives letting you choose how you share in our success.</div></div><div></div><div><div>
Global parental leave, six months off - for all new parents.</div></div><div></div><div><div>
All The Feels, our employee assistance program and self-care hub.</div></div><div></div><div><div>
Flexible public holidays, swap days off according to your values and beliefs.</div></div></div></div><div></div>

Senior Machine Learning Engineer, Ads R&D

<div><div><div><div><div><div>Engineering</div></div></div><div><div>
Machine Learning</div></div></div><div><div>
Permanent</div></div><div><div>
New York</div></div><div><div>
We&rsquo;re part of Spotify&rsquo;s Podcast Mission, focused on giving podcast creators tools to grow their audiences and connect more deeply with listeners. The team is building the systems that power interactive podcast experiences, enabling everything from polls, comments, and Q&amp;A to emerging agent-driven features.</div></div><div><div><div><br>
</div><div>As a Senior Machine Learning Engineer, from integrating foundational models to crafting feedback loops for real-time interactivity, you'll shape how creators and consumers connect in more dynamic and intelligent ways. This is a product-oriented role where you&rsquo;ll spend time understanding roadmaps and advising leadership on the trade-offs of implementing ML solutions. You&rsquo;ll also play a key role in build-vs-buy decisions, assessing the engineering effort required, and connecting with ML teams across Spotify to find opportunities for collaboration with the Podcast mission.</div></div></div></div><div><div><p>
What You'll Do</p><ul><li>
Tech lead ML initiatives for podcast and video features while helping define and maintain a clear roadmap for the Spotify for Creators and Megaphone apps.</li><li>
Advise leadership on ML initiatives, guiding prioritization and shaping decisions on the development and rollout of ML features.</li><li>
Serve as the liaison ML efforts within the Podcast mission and ML engineers/PMs across Spotify, ensuring deep understanding of existing systems and facilitating their seamless integration into the Spotify for Creators product.</li><li>
Mentor engineers in ML practices to level up Podcast Mission&rsquo;s capabilities in the space.</li><li>
Work closely with backend, data, and client engineers as well as PMs and designers to ship features that directly impact podcast engagement metrics.</li><li>
Prototype novel agentic workflows (e.g., multi-component pipelines or tool-using agents) and contribute to experimentation around Creator-Consumer interactivity.</li><li>
Own and evolve ML model lifecycle: data annotation, data pipeline construction, feature engineering, model training, deployment, and monitoring.</li><li>
Design, develop, fine-tune, and deploy machine learning systems that power podcast growth.</li><li>
Optimize models for scale and reliability using modern ML infra tools like Ray, Apache Beam, and Google Cloud Platform (GCP).</li><li>
Lead with an experimentation attitude. Implement A/B tests and contribute to continuous model evaluation and improvement loops to productionize solutions at scale for our millions of active podcast users.</li><li>
Participate in Spotify&rsquo;s ML community: share findings, explore new tools and paradigms, and contribute to scaling ML standards across the company.</li></ul></div></div><div><div><p>
Who You Are</p><ul><li>
You have 5+ years of professional experience in machine learning, with expertise in building and productionize ML systems at scale.</li><li>
You&rsquo;re fluent in Python; experience with PyTorch or TensorFlow is a strong plus.</li><li>
You&rsquo;ve worked with large-scale data systems and owning end-to-end ML workflows (data ingestion to serving).</li><li>
You have hands-on experience with cloud platforms like GCP or AWS, and with ML infra tools such as Ray, Apache Beam, or Airflow.</li><li>
You have experience, or strong interest, in agent-based systems and LLM integrations.</li><li>
You thrive in agile environments, care deeply about product impact, and bring a user-centered outlook to ML development.</li><li>
Bonus: Experience with content recommendation, interactive media formats, or real-time systems.</li><li>
Bonus: You&rsquo;ve led initiatives within an organization, expertly balancing trade-offs in large-scale systems, engaging with collaborators, and crafting clear, actionable roadmaps.</li></ul></div></div><div><div><p>
Where You'll Be</p><ul><li>
We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location.</li><li>
This team operates within the Eastern Standard time zone for collaboration.</li></ul></div></div><div><div><p>
Learn about life at Spotify</p></div><div><div>
The United States base range for this position is $ 176,166 - $ 251,666, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, a monthly meal allowance, 23 paid days off, 13 paid flexible holidays. These ranges may be modified in the future.</div></div></div><div><div><p>
Our global benefits</p></div><div></div><div><div>
Extensive learning opportunities, through our dedicated team, GreenHouse.</div></div><div></div><div><div>
Flexible share incentives letting you choose how you share in our success.</div></div><div></div><div><div>
Global parental leave, six months off - for all new parents.</div></div><div></div><div><div>
All The Feels, our employee assistance program and self-care hub.</div></div><div></div><div><div>
Flexible public holidays, swap days off according to your values and beliefs.</div></div></div></div><div></div>

Senior Machine Learning Engineer, Podcast

<div><div><div><div><div><div>Engineering</div></div></div><div><div>
Machine Learning</div></div></div><div><div>
Permanent</div></div><div><div>
New York</div></div><div><div>
The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music and podcasts better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search, original playlists like Discover Weekly and Daylist, and are at the forefront of new innovations like AI DJ and AI Playlists.</div></div><div><div><div><br>
</div><div>Generative AI is transforming Spotify&rsquo;s product capabilities and technical architecture. Generative recommender systems, agent frameworks, and LLMs present huge opportunities for our products to serve more user needs and use cases and unlock richer understanding of our content and users.</div><div><br>
</div><div>This Senior Staff Machine Learning Engineer will focus on recommender systems modeling at the intersection of generative recommenders and foundational understanding of personalization across music and talk content formats. You will work closely with a cross-functional team to define and execute the machine learning technical strategy for the product area, building the next generation of Spotify content and user representations and the technical architecture to support it. You will work as an individual contributor, offering the opportunity to shape the direction of Home loading paradigms, page serving, content filtering and content storage.</div><div><br>
</div><div>Join us and you&rsquo;ll keep millions of users listening to great recommendations every day!</div></div></div></div><div></div><div><div><p><br>
What You'll Do</p><ul><li>
Contribute to defining the machine learning technical strategy at the intersection of generative recommenders and foundational user modeling</li><li>
Collaborate with a cross functional agile team spanning user research, design, data science, product management, and engineering to build new product features that connect fans and artists in personalized, meaningful ways</li><li>
Provide expert technical leadership and direction to accelerate development, ensure scalability and push the boundaries of current methods</li><li>
Contribute to designing, building, evaluating, shipping, and refining Spotify&rsquo;s personalization products by hands-on ML developmentPrototype new modeling approaches and productionize solutions at scale for our hundreds of millions of active users</li><li>
Promote and role-model best practices of ML model development, testing, evaluation, etc., both inside the team as well as throughout the organization</li><li>
Engage with the broader ML community within Spotify and stay current with ML research to inspire and evolve our approaches</li><li>
Partner closely with teams to translate their needs into foundational systems that enable each step of the core content lifecycle.
</li><li>Mentor engineers and influence technical strategy by setting high standards in methodology, reproducibility, and collaboration.</li></ul></div></div><div><div><p>
Who You Are</p><ul><li>
You have a strong background in machine learning and recommender systems, and you know how to bridge research and end-user impact</li><li>
You have production experience developing large-scale machine learning systems in Java, Scala, Python, or similar languages. Experience with PyTorch, Tensorflow, JAX is a strong plus</li><li>
You have hands-on experience training and operating transformer models in production settings, or a strong interest in doing so</li><li>
You enjoy leading projects from start to finish working closely with your team and peers</li><li>
You are comfortable dealing with ambiguity on high impact projects</li><li>
You&rsquo;re a strong communicator and systems thinker who can drive alignment and influence across technical and product stakeholders</li><li>
You care about agile software processes, data-driven development, reliability, and disciplined experimentation</li><li>
You stay current on ML trends and are eager to apply emerging ideas to Spotify&rsquo;s challenges</li><li>
You&rsquo;re passionate about the opportunity to enrich the listening experience for users around the world</li><li>
You have extensive experience in designing system architectures that include machine learning models as key components in enabling the product experiences.
</li><li>You have a strong bias to action by building MVPs, prototypes and illustrating ideas through concise documents to drive initiatives forward.
</li><li>Team-first approach with developed techniques to ensure teams are happy, motivated, and productive</li><li>
You enjoy leading projects from start to finish working closely with your team and peers. You are comfortable dealing with ambiguity on high impact projects</li><li>
Accountable to senior tech leadership for meeting our product and technology objectives and managing expectations if those are at risk</li><li>
Demonstrated success leading technical initiatives and shaping strategic directions through cross-functional collaboration.</li><li>
Excellent communication skills and stakeholder management abilities; comfortable operating at the intersection of science and engineering</li></ul></div></div><div><div><p>
Where You'll Be</p><ul><li>
We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location</li><li>
This team operates within the Eastern Standard time zone for collaboration</li></ul></div></div><div><div><p>
Learn about life at Spotify</p></div><div><div>
The United States base range for this position is $264,641-$378,058 plus 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. These ranges may be modified in the future.</div><div></div><div><br>
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what&rsquo;s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It&rsquo;s in our differences that we will find the power to keep revolutionizing the way the world listens.</div><div></div><div><br>
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we&rsquo;re here to support you in any way we can.</div><div></div><div><br>
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world&rsquo;s most popular audio streaming subscription service.</div></div></div><div><div><p>
Our global benefits</p></div><div></div><div><div>
Extensive learning opportunities, through our dedicated team, GreenHouse.</div></div><div></div><div><div>
Flexible share incentives letting you choose how you share in our success.</div></div><div></div><div><div>
Global parental leave, six months off - for all new parents.</div></div><div></div><div><div>
All The Feels, our employee assistance program and self-care hub.</div></div><div></div><div><div>
Flexible public holidays, swap days off according to your values and beliefs.</div></div></div></div><div></div>

Senior Staff Machine Learning Engineer

Amazon.com

<div><h2 class="jobSectionHeader"><b>DESCRIPTION</b></h2><div>
About Sponsored Products and Brands<br>
<br>
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.<br>
<br>
Key job responsibilities<br>
As a Software Development Engineer in Machine Learning, you will:
<br>
<ul><li>Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems.</li><li>Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.</li><li>Work closely with applied scientists to optimize the performance of machine-learning models and infrastructure, and implement end-to-end solutions.</li></ul><br>
About the team<br>
SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are looking for a talented Software Engineer with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide.</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>
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company&rsquo;s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.<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.<br>
<br>
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $151,300/year in our lowest geographic market up to $261,500/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.</div>

Sr. Software Dev Engineer, Sponsored Products and Brands Ads Response Prediction

<div><div><div><div><div><div>Engineering</div></div></div><div><div>
Machine Learning</div></div></div><div><div>
Permanent</div></div><div><div>
New York</div></div><div><div>
The Personalization team at Spotify aims to make content discovery effortless and enjoyable for every listener. By deeply understanding music, podcasts, audiobooks, and videos, we deliver exceptional recommendations that keep millions of people worldwide engaged with our products daily. Our work spans experiences like Home, Search, curated playlists such as Discover Weekly and Daylist, and cutting-edge innovations including AI DJ and AI Playlists.</div></div><div><div><div><br>
</div><div>Home plays a pivotal role in shaping the technical vision and architecture of recommendation systems that power the Spotify Home page. You&rsquo;ll work on foundational components that enable the larger Home experience, user activation, listening habits across content types, and expanding its technology to take advantage of LLMs and other Generative Recommender products. As a Staff Machine Learning Engineer in Home, you&rsquo;ll focus on recommender systems modeling at the intersection of generative recommenders and a foundational understanding of user taste across music and talk content.
</div><div></div><br>
<div>You will define and execute the ML technical strategy for Home, building the next generation of Spotify&rsquo;s recommendation systems, user representations, and supporting technical architecture. Join us and you&rsquo;ll help millions of users discover and connect with the world&rsquo;s audio content every day.</div></div></div></div><div><div><p>
What You'll Do</p><ul><li>
Define and drive the ML technical strategy for Home, focusing on generative recommendation approaches</li><li>
Build models that improve user representations, personalization, and relevance across Spotify&rsquo;s experiences</li><li>
Collaborate with a cross-functional agile team spanning user research, design, data science, product management, and engineering</li><li>
Prototype and productionize new modeling approaches at scale, serving hundreds of millions of users worldwide</li><li>
Lead high-impact projects from ideation through deployment, setting best practices for ML development, testing, evaluation, and experimentation</li><li>
Partner with tech leaders and stakeholders to influence priorities and ensure long-term scalability and impact</li><li>
Stay engaged with the broader ML and Search research community, applying emerging trends to Spotify&rsquo;s challenges</li></ul></div></div><div><div><p>
Who You Are</p><ul><li>
You have a strong background in machine learning and recommender systems, bridging research and user impact</li><li>
You have hands-on experience training and operating transformer models in production, or strong interest in doing so</li><li>
You have production experience developing large-scale ML systems in Java, Scala, Python, or similar languages. Experience with PyTorch or TensorFlow is a strong plus</li><li>
You are comfortable navigating ambiguity and leading high-impact projects from start to finish</li><li>
You&rsquo;re a systems thinker and strong communicator who can align and influence technical and product stakeholders</li><li>
You care deeply about agile processes, data-driven development, and reliability.You&rsquo;re eager to apply emerging ML trends, particularly in LLMs and generative recommenders, to Spotify&rsquo;s challenges</li></ul></div></div><div><div><p>
Where You'll Be</p><ul><li>
We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.</li><li>
This team operates within the Eastern Standard time zone for collaboration.</li></ul></div></div><div><div><p>
Learn about life at Spotify</p></div><div><div>
The United States base range for this position is $215,136- $307,337 plus 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. These ranges may be modified in the future.</div></div></div><div><div><p>
Our global benefits</p></div><div></div><div><div>
Extensive learning opportunities, through our dedicated team, GreenHouse.</div></div><div></div><div><div>
Flexible share incentives letting you choose how you share in our success.</div></div><div></div><div><div>
Global parental leave, six months off - for all new parents.</div></div><div></div><div><div>
All The Feels, our employee assistance program and self-care hub.</div></div><div></div><div><div>
Flexible public holidays, swap days off according to your values and beliefs.</div></div></div></div><div></div>

Staff Machine Learning Engineer

Software Engineer, Computer Vision

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