Lead Machine Learning Engineer

You will be joining Founding Teams, a stealth AI Tech Incubator & Talent platform that provides support to the next generation of AI startup founders by offering resources in engineering, product development, sales, marketing, and operations. As a Lead Machine Learning Engineer, you will play a crucial role in developing, deploying, and optimizing machine learning solutions. Your responsibilities will include collaborating with data scientists, software engineers, and product managers to bring cutting-edge ML models into production at scale. You will also be involved in shaping the AI strategy and mentoring the machine learning team.

• *Key Responsibilities:** - Lead the end-to-end development of machine learning models, from prototyping to production deployment. - Architect scalable ML pipelines and infrastructure. - Collaborate with data scientists to transition research models into robust production systems. - Integrate ML models into applications and services in collaboration with engineering teams. - Manage and mentor a team of machine learning and data engineers. - Establish best practices for model development, evaluation, monitoring, and retraining. - Design experiments, analyze results, and iterate rapidly to improve model performance. - Stay updated with the latest research and developments in machine learning and AI. - Define and enforce ML model governance, versioning, and documentation standards.
• *Qualifications Required:** - Bachelor's or Masters degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field (PhD preferred but not required). - 5+ years of professional experience in machine learning engineering. - 2+ years of leadership or technical mentoring experience. - Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). - Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. - Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). - Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). - Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. - Deep understanding of MLOps concepts: monitoring, logging, CI/CD for ML, reproducibility. - Experience with Docker and container orchestration (e.g., Kubernetes). This role also involves working with the following tools & technologies: - Languages: Python, SQL (optionally: Scala, Java for large-scale systems) - ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM - MLOps: MLflow, Weights & Biases, Kubeflow, Seldon Core - Data Processing: Pandas, NumPy, Apache Spark, Beam - Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask - Cloud Platforms: AWS (SageMaker, S3, EC2), Google Cloud AI Platform, Azure ML - Orchestration: Docker, Kubernetes, Airflow - Databases: PostgreSQL, BigQuery, MongoDB, Redis - Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases - Version Control: Git (GitHub, GitLab) - Communication: Slack, Zoom - Project Management: Jira, Confluence You will be joining Founding Teams, a stealth AI Tech Incubator & Talent platform that provides support to the next generation of AI startup founders by offering resources in engineering, product development, sales, marketing, and operations. As a Lead Machine Learning Engineer, you will play a crucial role in developing, deploying, and optimizing machine learning solutions. Your responsibilities will include collaborating with data scientists, software engineers, and product managers to bring cutting-edge ML models into production at scale. You will also be involved in shaping the AI strategy and mentoring the machine learning team.
• *Key Responsibilities:** - Lead the end-to-end development of machine learning models, from prototyping to production deployment. - Architect scalable ML pipelines and infrastructure. - Collaborate with data scientists to transition research models into robust production systems. - Integrate ML models into applications and services in collaboration with engineering teams. - Manage and mentor a team of machine learning and data engineers. - Establish best practices for model development, evaluation, monitoring, and retraining. - Design experiments, analyze results, and iterate rapidly to improve model performance. - Stay updated with the latest research and developments in machine learning and AI. - Define and enforce ML model governance, versioning, and documentation standards.
• *Qualifications Required:** - Bachelor's or Masters degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field (PhD preferred but not required). - 5+ years of professional experience in machine learning engineering. - 2+ years of leadership or technical mentoring experience. - Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). - Experience with deep learni