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Senior Machine Learning Ops Engineer

DailyPay is transforming the way people get paid. As a worktech company and the industry’s leading On-demand paysolution, DailyPay uses an award-winning technology platform to help America’s top employers build stronger relationships with their employees. This voluntary employee benefit enables workers everywhere to feel more motivated to work harder and stay longer on the job while supporting their financial well-being outside of the workplace. DailyPay is headquartered in New York City, with operations throughout the United States as well as in Belfast. For more information, visit DailyPay's Press Center.

The Role:

We are seeking a highly skilled and motivated Senior MLOps Engineer to join our growing team in Belfast.  You will play a crucial role in maturing and scaling our machine learning infrastructure and processes, ensuring the reliability, scalability, and performance of our ML models in production. This role requires a strong background in MLOps principles, cloud technologies (AWS), and a passion for building robust and efficient systems. You will collaborate closely with data scientists, engineers, and product teams to deliver high-quality ML solutions that directly impact our business.

The mission of the Data Science Team is critical for the continuous development and success of our product, understanding the needs of our customers and partners, and maintaining DailyPay’s leading role in the early wage access industry. As a staff MLOps Engineer, you will be instrumental in helping to realize this vision for DailyPay by enabling efficient and effective deployment and management of machine learning models.

How You Will Make an Impact:

  • Design and Implement MLOps Solutions: Design and implement scalable and efficient ML pipelines and workflows, encompassing model training, deployment, monitoring, and retraining. Contribute to the development and implementation of MLOps best practices

  • Cloud Infrastructure Management: Manage and optimize our AWS cloud infrastructure for machine learning, ensuring cost-effectiveness, security, and high availability

  • CI/CD Pipeline Development: Develop and maintain robust CI/CD pipelines for continuous integration and deployment of ML models and related infrastructure

  • Monitoring and Observability: Build and maintain comprehensive monitoring and alerting systems for our ML infrastructure and models, leveraging tools like DataDog to ensure system health and performance

  • Collaboration and Mentorship: Collaborate effectively with data scientists, engineers, and other stakeholders. Provide guidance and support to junior team members

  • Performance Optimization: Continuously optimize ML model performance, data pipelines, and infrastructure to meet evolving business needs

  • Security and Compliance: Ensure the security and compliance of our machine learning applications and data pipelines, adhering to industry best practices and regulations

  • Problem Solving and Troubleshooting: Proactively identify and resolve issues related to ML model performance, data integrity, and infrastructure

What You Bring to The Team:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field

  • 3+ years of experience in machine learning operations, data engineering, or related roles

  • AWS Proficiency: Strong understanding of AWS services (e.g., EC2, S3, Lambda, SageMaker, ECS) and cloud infrastructure management

  • Programming and ML Frameworks: Proficiency in Python and experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch

  • CI/CD Experience: Experience with CI/CD tools and practices (e.g., GitHub Actions, Jenkins) and the ability to design and implement efficient CI/CD pipelines

  • Containerization and Orchestration: Knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes)

  • Monitoring and Logging: Experience with monitoring and logging tools like DataDog, Prometheus, or Grafana

  • Data Engineering Skills: Knowledge of event streaming platforms (e.g., Apache Kafka) and SQL database management

  • Strong Communication and Collaboration: Excellent communication skills and the ability to work effectively in a remote, collaborative environment

Nice to Haves:

  • Experience with infrastructure-as-code tools like Terraform or CloudFormation.

  • Familiarity with microservices architecture and RESTful API design.

  • Experience leading small projects and managing stakeholders.

  • Contributions to open-source projects or participation in the MLOps community.

What We Offer:

  • Competitive compensation

  • Opportunity for equity ownership

  • Private health insurance option

  • Employee Resource Groups

  • Fun company outings and events

  • Generous PTO Allowance

  • 5% Pension contribution

 

DailyPay does not accept and will not review unsolicited resumes from search firms.

DailyPay is committed to fostering an inclusive, equitable culture of belonging, grounded in empathy and respect, which values openness to opinions, awareness of lived experiences, fair treatment and access for all. We strive to build and develop diverse teams to create an organisation where innovation thrives, where the full potential of each person is engaged, and their views, beliefs and values are integrated into our ways of working.


We are an equal opportunities employer and welcome applications from all sections of the community.

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What You Should Know About Senior Machine Learning Ops Engineer, DailyPay

At DailyPay, we're transforming the way people get paid, and we're on the lookout for a talented Senior Machine Learning Ops Engineer to join our dynamic team. Located primarily in Belfast but with a footprint across the U.S. and beyond, DailyPay is a leader in on-demand pay solutions, and your role will be crucial in ensuring our machine learning infrastructure is top-notch. In this exciting position, you'll be responsible for designing and implementing efficient ML pipelines, managing our AWS cloud infrastructure, and building CI/CD pipelines that will enhance our ML models' reliability and performance. You'll work closely with data scientists and engineers, sharing your know-how and supporting the continuous optimization of our systems. Your expertise will help us build robust solutions that propel our commitment to early wage access and improve the financial well-being of workers everywhere. If you're passionate about MLOps and are eager to make a meaningful impact in a collaborative environment, we’d love to have you on board. Your contributions will directly influence our mission at DailyPay, enabling us to provide innovative solutions to our clients and partners. Join us in shaping the future of payroll technology while enriching your own career with growth opportunities and a fantastic team culture.

Frequently Asked Questions (FAQs) for Senior Machine Learning Ops Engineer Role at DailyPay
What are the main responsibilities of a Senior Machine Learning Ops Engineer at DailyPay?

As a Senior Machine Learning Ops Engineer at DailyPay, your main responsibilities include designing and implementing scalable ML pipelines, managing AWS cloud infrastructure for optimal performance, developing CI/CD pipelines for model deployment, and ensuring the reliability of ML systems. You'll also collaborate with data scientists and engineers, mentor junior colleagues, continuously optimize ML model performance, and oversee security and compliance measures.

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What qualifications are needed to apply for a Senior Machine Learning Ops Engineer position at DailyPay?

To apply for the Senior Machine Learning Ops Engineer position at DailyPay, candidates should have a Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. Additionally, a minimum of three years of experience in machine learning operations or data engineering, proficiency with AWS services, and knowledge of CI/CD practices and ML frameworks are essential.

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What programming languages and tools are used by Senior Machine Learning Ops Engineers at DailyPay?

At DailyPay, Senior Machine Learning Ops Engineers primarily utilize Python for programming, along with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch. Familiarity with tools like Docker for containerization, AWS for cloud services, and CI/CD tools such as GitHub Actions or Jenkins is also crucial for the role.

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How does collaboration work for a Senior Machine Learning Ops Engineer at DailyPay?

Collaboration for a Senior Machine Learning Ops Engineer at DailyPay is integral and involves working closely with data scientists, product teams, and other engineers. The role emphasizes strong communication skills and the ability to effectively collaborate in a remote, and diverse environment to ensure that all ML solutions meet the business's evolving needs.

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What opportunities for growth are available for Senior Machine Learning Ops Engineers at DailyPay?

DailyPay offers a vibrant environment for personal and career growth for Senior Machine Learning Ops Engineers. You’ll have opportunities to lead projects, mentor junior staff, and participate in shaping company practices. With exposure to advanced technologies and techniques, plus a supportive team and culture, you’ll be positioned to expand your skills and elevate your career.

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Common Interview Questions for Senior Machine Learning Ops Engineer
Can you describe your experience with MLOps principles?

When answering this question, focus on your previous roles that involved designing and implementing MLOps solutions. Highlight how you established ML pipelines and integrated continuous deployment practices to enhance model performance and reliability.

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What AWS services have you used in your previous MLOps roles?

Discuss specific AWS services you've utilized, like EC2, S3, and SageMaker, and explain how you applied these in your MLOps projects. Mention any challenges you faced and how you overcame them.

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How do you ensure the security and compliance of machine learning models?

Explain your familiarity with security protocols and compliance regulations relevant to ML operations. Discuss practices you implemented to secure data and models, such as data encryption and access controls.

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What are the best CI/CD practices you have implemented for ML models?

Share your insights on CI/CD best practices you have adopted, focusing on automation, testing, and deployment strategies that ensure smooth transitions from model development to production without downtimes.

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Can you give an example of how you've optimized a machine learning pipeline?

Provide a specific example where you identified performance bottlenecks in an ML pipeline and describe the steps you took to enhance efficiency, leveraging metrics and optimization tools.

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How do you approach troubleshooting ML model performance issues?

Discuss your systematic approach to troubleshooting, including how you analyze performance metrics, identify root causes, and implement solutions to resolve issues with model accuracy or data integrity.

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What tools do you use for monitoring and observability of ML models?

Mention the monitoring tools you are familiar with, such as DataDog or Grafana, and elaborate on how you used them to ensure that model performance remained optimal, discussing any key insights gained from this data.

Join Rise to see the full answer
How do you keep abreast of the latest developments in MLOps?

Talk about methods you use to stay updated on MLOps trends and technologies, such as reading industry blogs, attending conferences, or participating in relevant community discussions.

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How do you foster collaboration among team members in a remote setting?

Share specific strategies you've implemented or experienced that facilitate effective collaboration despite physical distance, emphasizing tools used and communication practices that enhance teamwork.

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What does successful mentoring look like for you in an engineering environment?

Describe your approach to mentoring junior team members, focusing on providing guidance, sharing knowledge, and helping them navigate challenging situations in their MLOps journey.

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DailyPay, powered by its industry-leading technology platform, is on a mission to build a new financial system. Partnering with America's best-in-class employers, including Dollar Tree, Berkshire Hathaway and Adecco, DailyPay is the recognized gol...

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DATE POSTED
April 13, 2025

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