Junior Machine Learning Engineer

We are a fast-growing U.S. technology group whose cross-functional squads deliver machine learning solutions for Fortune 500 and high-growth startups alike. An evidence-driven mindset fuels our sprint cycles, and knowledge sharing lies at the core of every retrospective.  

Day-to-Day Responsibilities  

- Engineer clean, modular pipelines that ingest, transform, and validate structured or unstructured datasets.  

- Train, fine-tune, and benchmark classification, regression, and recommendation models.  

- Debug algorithmic glitches, trace performance bottlenecks, and iterate toward production-grade code.  

- Document experimental protocols, hyper-parameter decisions, and reproducibility steps for peer review.  

- Integrate model artifacts into microservices or batch workflows through RESTful APIs or message queues.  

- Pair closely with product managers and UX designers to translate customer pain points into measurable ML metrics.  

- Maintain continuous integration checks and monitor drift once models are deployed.  

Must-Have Skills  

- Solid grasp of Python 3.x syntax, data classes, virtual environments, and packaging.  

- Coursework or hands-on projects using scikit-learn plus either TensorFlow or PyTorch.  

- Familiarity with pandas, NumPy, and Jupyter notebooks for exploratory analysis.  

- Comfort interpreting precision-recall curves, confusion matrices, and ROC-AUC.  

- Analytical mindset, proven debugging tenacity, and concise technical communication.  

- Authorization to work in the United States.  

Nice-to-Have Extras  

- Exposure to cloud tooling (AWS SageMaker, Google Vertex AI, or Azure ML).  

- Experience with Docker images, GitHub Actions, or similar CI/CD pipelines.  

- Knowledge of SQL or NoSQL stores for feature retrieval.  

- Participation in Kaggle competitions or published academic research.