Junior Machine Learning Engineer - AI startup

Job description

Founding Teams is a stealth AI Tech Incubator & Talent platform. We are supporting the next generation of AI startup founders with the resources they need including engineering, product, sales, marketing and operations staff to create and launch their product.

The ideal candidate will have a passion for next generation AI tech startups and working with great global startup talent.

About the Role:

We are looking for an experienced and highly motivated Lead Machine Learning Engineer to drive the development, deployment, and optimization of machine learning solutions. As a technical leader, you will collaborate closely with data scientists, software engineers, and product managers to bring cutting-edge ML models into production at scale. You'll play a key role in shaping the AI strategy and mentoring the machine learning team.

Responsibilities:

Lead the end-to-end development of machine learning models, from prototyping to production deployment.
Architect scalable ML pipelines and infrastructure.
Work closely with data scientists to transition research models into robust production systems.
Collaborate with engineering teams to integrate ML models into applications and services.
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 current with the latest research and developments in machine learning and AI.
Define and enforce ML model governance, versioning, and documentation standards.

Required Skills & Qualifications:

Bachelor's or Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field (PhD preferred but not required).
3+ 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).
Experience building and maintaining ML pipelines and data pipelines.
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).

Preferred Skills:

Experience with feature stores (e.g., Feast, Tecton).
Knowledge of distributed training (e.g., Horovod, distributed PyTorch).
Familiarity with big data tools (e.g., Spark, Hadoop, Beam).
Understanding of NLP, computer vision, or time series analysis techniques.
Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases).
Experience with model explainability techniques (e.g., SHAP, LIME).
Familiarity with reinforcement learning or generative AI models.

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