Senior Machine Learning Engineer

To qualify for the Senior Machine Learning Engineer position at Influur, candidates should have at least 6 years of experience in machine learning engineering, particularly in deploying ML models within production environments. Proficiency in Python and familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn is necessary. A solid understanding of data processing frameworks such as Spark and MLOps best practices is also essential, along with strong problem-solving abilities and excellent communication skills.

As a Senior Machine Learning Engineer at Influur, you will design, develop, and deploy machine learning models to improve influencer-brand matching and enhance core platform features. Key responsibilities include building production-ready ML pipelines, ensuring model performance through rigorous testing, and leading initiatives to integrate ML objectives across teams. You will also mentor future ML hires and collaborate with data engineers to ensure the availability of high-quality training data.

Influur offers a unique, people-oriented culture that fosters collaboration and personal growth. As a Senior Machine Learning Engineer, you will thrive in a diverse, fast-paced startup environment that encourages you to push boundaries. With a strong focus on mentorship and professional development, Influur ensures that you have the tools and opportunities to succeed in your role while also enjoying the collective success of the team.

At Influur, the Senior Machine Learning Engineer will work extensively with Python and ML frameworks like TensorFlow, PyTorch, or Scikit-learn. You'll also leverage cloud platforms such as AWS or GCP to deploy and scale machine learning solutions. Familiarity with data engineering tools, including Airflow, dbt, or BigQuery, is a bonus, especially if experience with graph-based or recommendation systems exists.

The Senior Machine Learning Engineer at Influur will be involved in projects that focus on enhancing influencer-brand matching, developing content recommendations, and streamlining other core platform features. You'll analyze extensive influencer marketing data to glean insights that inform ML strategies and work on optimizing workflows for efficiency and cost-effectiveness, contributing to the overall innovative spirit of the company.

In my previous role, I successfully deployed a recommendation system for an e-commerce platform. The model was built using TensorFlow and was designed to analyze user behavior to suggest products. I collaborated with data engineers to ensure data quality and pipeline efficiency, and coordinated with the front-end team to integrate the model seamlessly into the user interface.

I prioritize rigorous testing when developing machine learning models. This includes using techniques like cross-validation, monitoring accuracy metrics, and continuously evaluating model performance against a validation dataset. I also implementA/B testing in live environments to gauge real-world effectiveness and gather user feedback to make necessary adjustments.

I have experience working with AWS and GCP, utilizing their powerful ML tools and resources. For instance, using AWS SageMaker has streamlined the development process, enabling faster model training and testing with built-in features. The scalability offered by cloud platforms also ensures that our models can handle varying levels of data input without compromising performance.

I have a solid understanding of MLOps practices, including CI/CD for ML models. I implement automation in the model deployment process to facilitate quick rollouts and easy rollback if necessary. This minimizes downtime and maintains a consistent performance level across all deployed models.

I regularly follow leading machine learning blogs, participate in webinars, and engage in online communities such as GitHub and LinkedIn. Additionally, I attend relevant conferences and workshops to network with industry professionals and exchange insights on cutting-edge trends and technologies.

I believe in maintaining clear and open communication with cross-functional teams. I often set up regular check-ins to discuss progress and alignment on project objectives. My goal is to understand the needs of other teams, whether they are product managers or data engineers, and to integrate their feedback into our machine learning initiatives to drive cohesive results.

A particularly challenging problem I encountered was developing a fraud detection system for financial transactions. By applying anomaly detection techniques and ensemble learning methods, I was able to significantly reduce false positives while maintaining robust detection rates. Collaboration with domain experts was essential in defining our feature set based on business knowledge.

Feature engineering is crucial as it directly influences model performance. I leverage domain knowledge to identify and create relevant features that the model can learn effectively from. Continuous analysis of feature importance and impact on model accuracy helps in iterating and refining the feature set over time.

I approach mentoring by creating a supportive and collaborative environment. I believe in setting clear expectations and providing guidance through hands-on projects. Encouraging independence while being available for quick feedback allows new engineers to build confidence in their skills and understanding of our ML practices.

To analyze influencer marketing data, I start by cleaning and preprocessing the data to ensure its quality. I then apply exploratory data analysis (EDA) to identify trends and patterns. This insight informs the development of predictive models tailored to optimize influencer-brand interactions, ultimately driving better marketing outcomes.