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Senior Machine Learning Engineer

Attentive® is the AI-powered mobile marketing platform transforming the way brands personalize consumer engagement. Attentive enables marketers to craft tailored journeys for every subscriber, driving higher recurring revenue and maximizing campaign performance. Activating real-time data from multiple channels and advanced AI, the platform personalizes content, tone, and timing to help brands deliver 1:1 messages that truly resonate.


With a top-rated customer success team recognized on G2, Attentive partners with marketers to provide strategic guidance and optimize SMS and email campaigns. Trusted by leading global brands like GUESS, Urban Outfitters, and Steve Madden, Attentive ensures enterprise-grade compliance and deliverability, supporting trillions of interactions across more than 70 industries. To learn more or request a demo, visit www.attentive.com or follow us on LinkedIn, X (formerly Twitter), or Instagram.


Attentive’s growth has been recognized by Deloitte’s Fast 500Linkedin’s Top Startups and Forbes Cloud 100 all thanks to the hard work from our global employees!


Who we are

Our engineering department consists of 200+ people across multiple teams, such as application development, infrastructure, data platform, machine learning, and security. We believe our company will win in the long run through product innovation. To get there, we obsess over iteratively delivering customer value through rapid prototyping and data-driven decision-making. 


We are seeking a self-driven and highly motivated Machine Learning Engineer to join our growing machine learning teams. As an early hire, you will contribute to the development of machine learning models and infrastructure needs across the Attentive platform and work with Product Management and Engineering to implement end-to-end modeling use cases.


Why Attentive needs you
  • You have a proven track record of building systems that maintain a high bar of quality
  • You deeply loathe regressions and take proactive steps to protect against them through a variety of testing techniques
  • You are a collaborator, technical leader, and a great communicator
  • You are constantly improving the quality of the project you are working on, both via direct contributions as well as long-term advocacy for larger-scale changes
  • You are enthusiastic about the high impact, fast-paced work environment of an late-stage startup


About you
  • You have worked professionally building systems for 6+ years with experience on a single system long enough to see the consequences of your decisions
  • Experience with TensorFlow/Pytorch, xgboost, pandas, matplotlib, SQL, Spark or similar tools
  • You have proficiency or experience with Python
  • You have extensive experience using machine learning and data analysis, or similar, to build scalable systems and data-driven products, working with cross-functional teams
  • You have a proven track record of building scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation from modern research


Our scale
  • 8,000 brands powered by Attentive sent over 2.2 billion text messages over Cyber Week 2023 (Black Friday/Cyber Monday) representing a growth of 31% from 2022
  • We sent 32 billion SMS messages in 2023, up 32% YoY. That’s an average of 87 million per day
  • Our production cluster contains over 18,000 containers which serve 200+ services
  • Our streaming services process over 80 billion events per month


What we use
  • Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
  • Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
  • Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
  • Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
  • Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas


You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.


For US based applicants:

- The US base salary range for this full-time position is $180,200 - $280,000 annually + equity + benefits

- Our salary ranges are determined by role, level and location


#LI-EF1



Attentive Company Values

Default to Action - Move swiftly and with purpose

Be One Unstoppable Team - Rally as each other’s champions

Champion the Customer - Our success is defined by our customers' success

Act Like an Owner - Take responsibility for Attentive’s success


Learn more about AWAKE, Attentive’s collective of employee resource groups.


If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches your skills and experience.


At Attentive, we know that our Company's strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. Our policy is to provide equal employment opportunities for all employees, applicants and covered individuals regardless of protected characteristics. We prioritize and maintain a fair, inclusive and equitable workplace free from discrimination, harassment, and retaliation. Attentive is also committed to providing reasonable accommodations for candidates with disabilities. If you need any assistance or reasonable accommodations, please let your recruiter know. 

Average salary estimate

$230100 / YEARLY (est.)
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$180200K
$280000K

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What You Should Know About Senior Machine Learning Engineer, Attentive

Join Attentive as a Senior Machine Learning Engineer and dive into the exciting world of AI-powered mobile marketing! At Attentive, we’re transforming how brands engage with their consumers. You’ll work with a talented team focused on delivering personalized journeys for subscribers, driving impressive growth in campaign performance for big-name clients like GUESS and Urban Outfitters. Your expertise in machine learning will be vital as you craft state-of-the-art models and infrastructure, collaborating closely with product management and engineering to implement impactful solutions. As a member of our growing machine learning team, you’ll play a pivotal role in defining the future of our platform while maintaining high standards of quality. Here, every day brings new challenges as you proactively tackle regressions and advocate for long-term improvements. If you thrive in fast-paced environments and love finding creative solutions to complex problems, you’ll fit right in at Attentive. With over 8,000 brands relying on our technology, this is your chance to contribute significantly to projects that scale massively and directly impact our clients’ success. Plus, you’ll enjoy competitive perks, an inclusive culture, and the chance to grow your skills with advanced tools like TensorFlow and PyTorch. If you’re ready to be part of something bigger and embark on an exciting career journey, we can’t wait to meet you!

Frequently Asked Questions (FAQs) for Senior Machine Learning Engineer Role at Attentive
What does a Senior Machine Learning Engineer do at Attentive?

At Attentive, a Senior Machine Learning Engineer plays a crucial role in designing and building machine learning models that enhance our AI-powered mobile marketing platform. You'll work with cross-functional teams to ensure that our campaigns are personalized and data-driven, directly impacting client engagement and performance.

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What are the qualifications needed for the Senior Machine Learning Engineer position at Attentive?

Candidates applying for the Senior Machine Learning Engineer position at Attentive should have 6+ years of professional experience in building systems, proficiency in Python, and a deep understanding of machine learning frameworks like TensorFlow and PyTorch. Additionally, experience in large-scale data processing and working with diverse teams is essential.

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What is the work culture like for a Senior Machine Learning Engineer at Attentive?

The work culture for a Senior Machine Learning Engineer at Attentive is dynamic and collaborative. Employees are encouraged to take initiative, innovate, and actively contribute to team discussions, fostering an environment where creativity and support flourish.

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How does Attentive support professional growth for Senior Machine Learning Engineers?

Attentive is committed to the professional growth of its Senior Machine Learning Engineers by offering numerous learning opportunities, mentorship programs, and access to the latest technologies and tools in the field to enable long-term career development.

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What technologies will I work with as a Senior Machine Learning Engineer at Attentive?

In the Senior Machine Learning Engineer role at Attentive, you'll work with cutting-edge technologies including TensorFlow, PyTorch, SQL, and AWS-based infrastructures. Our team also utilizes advanced data processing tools to optimize performance and scalability.

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What is the expected salary range for a Senior Machine Learning Engineer at Attentive?

The salary range for a Senior Machine Learning Engineer at Attentive is between $180,200 and $280,000 annually, depending on experience and qualifications. There are also competitive equity and benefits to support a well-rounded work-life balance.

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Is Attentive open to applicants from diverse backgrounds for the Senior Machine Learning Engineer position?

Absolutely! Attentive values diversity and inclusion, welcoming applicants from all backgrounds for the Senior Machine Learning Engineer position. We prioritize creating an equitable workplace that supports all employees in achieving their best.

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Common Interview Questions for Senior Machine Learning Engineer
Can you describe your experience with machine learning frameworks such as TensorFlow or PyTorch?

In your response, provide specific examples of projects where you've utilized TensorFlow or PyTorch. Highlight how you implemented these frameworks to solve problems, the results of your contributions, and any challenges you faced.

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How do you ensure the quality of your machine learning models?

Discuss your approach to quality assurance, including testing techniques you use to prevent regressions, and how you monitor model performance over time. Give examples of tools or methods you implement to maintain high standards.

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Have you worked on cross-functional teams? How did you collaborate with different roles?

Share your experience working in diverse teams and how you maintained open communication with product management and engineering. Highlight specific projects where collaboration led to successful outcomes.

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What strategies do you use to handle large-scale data processing?

Explain your experience with data processing tools and methods. Describe how you’ve optimized data pipelines for machine learning tasks, and mention any specific technologies you've used.

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Can you provide an example of a challenging machine learning problem you've solved?

Detail a specific problem you faced, the steps you took to analyze and address it, and the innovative solutions you implemented. Emphasize the impact of your work and any measurable results.

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How do you stay updated with the latest machine learning trends and technologies?

Discuss your methods for continuous learning, such as following industry publications, attending conferences, or participating in online courses. Share any relevant communities or groups you’re part of for networking.

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Describe a time when you had to advocate for a significant change in a project.

Share a story where you identified an improvement opportunity and how you communicated your vision to the team. Include the outcomes resulting from your advocacy and any changes that were successfully implemented.

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What tools do you prefer for visualizing and analyzing model performance?

Mention the specific tools you’ve used for visualization, like Matplotlib or other libraries, and explain how these tools helped you better understand model performance and make informed decisions.

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What are the most important metrics you track for model performance?

Explain the key metrics that are critical for evaluating model performance, such as accuracy, precision, recall, and F1 score, and discuss how you balance different metrics to ensure overall effectiveness.

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How would you approach building a new machine learning model from scratch?

Outline your systematic approach to building a machine learning model, including defining the problem, data collection, feature engineering, training, validation, and deployment. Provide insights into how you prioritize each step.

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Create magical conversations. DEFAULT TO ACTION - Speed is our best offense and defense -We don't wait for opportunities to come to us, we create them through our proactive mindset. -We believe in making quick decisions, executing strategies, and...

123 jobs
MATCH
VIEW MATCH
CULTURE VALUES
Passion for Exploration
Dare to be Different
Customer-Centric
Diversity of Opinions
Inclusive & Diverse
SENIORITY LEVEL REQUIREMENT
TEAM SIZE
EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
November 25, 2024

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