As a Machine Learning Engineering Manager, you will lead a team focused on the foundational ML & Data layers to power the ranking & recommendation systems in scope. You will drive the development of robust data & ML pipelines at scale, lead the implementation of the tools for ML scientists to test and productionize advanced ML RecSys solutions.
As a technical manager of Machine Learning Engineers and Data engineers, you should be passionate about technology, keep up to date with recent breakthroughs in the field, define and shape the teams ML and platforms roadmap, and not be afraid to get your hands dirty with code when needed.
You are expected to be the focal point for all technical aspects, make sure your team members deliver on their tasks, and work together with other stakeholders to define and shape the roadmap of our products. You will work independently and will also be responsible for making technical decisions within your team.
Key Job Responsibilities and Duties:
Lead and develop a high-performing team, fostering individual growth and collaboration.
Manage and mentor ML engineers and Data engineers, ensuring their professional development and effectiveness.
Develop scalable ML infrastructure and pipelines for efficient data processing and evaluations deployment.
Evaluate architecture solutions based on cost, business needs, and emerging technologies.
Collaborate closely with software engineers to ensure seamless deployment and model inference.
Monitor application health, set and track relevant metrics, and implement effective maintenance strategies.
Collaborate with stakeholders to translate business requirements into viable ML solutions.
Evaluate and integrate new ML technologies to enhance productivity and performance.
Drive continuous improvement through model retraining, performance monitoring, and optimization.
Develop robust ML and AI solutions that meet business objectives while considering production constraints.
3+ years leading an ML engineering team of a minimum of 4 people in a fast-paced production environment.
Relevant work or academic experience (MSc + 5 years of working experience, or PhD + 3 years of working experience), involved in the application of Machine Learning to business problems.
Masters degree, PhD or equivalent experience in a quantitative field (e.g. Computer Science, Engineering Mathematics, Artificial Intelligence, Physics, etc.).
Strong knowledge in areas like e.g. Recommender Systems, Deep Learning, Information Retrieval, Causal Inference, scaling ML models, etc.
Experience designing and executing end-to-end solutions for deploying different ML models.
Experience with cloud frameworks like AWS sagemaker for training, evaluation and serving models using TensorFlow, PyTorch, or scikit-learn.
Experience with big data processing frameworks such, Pyspark, Apache Flink, Snowflake or similar frameworks.
Demonstrable experience with MySQL, Cassandra, DynamoDB or similar relational/NoSQL database systems.
Deep understanding of machine learning algorithms, statistical models, and data structures.
Experience collaborating cross functionally in the development of machine learning products (e.g. Developers, UX specialists, Product Managers, etc.).
Strong working knowledge of Python, Java, Kafka, Hadoop, SQL, and Spark or similar technologies. Working experience with version control systems.
Excellent English communication skills, both written and verbal.
Successfully driving technical, business and people related initiatives that improve productivity, performance and quality while communicating with stakeholders at all levels.
Leading by example, gaining respect through actions, not your title. Developing your team and motivating them to achieve their goals. Providing feedback timely and managing your key team performance indicators.
