Junior AI/ML Engineer – Python, TensorFlow

An independent tech-driven organization delivers predictive solutions for finance, healthcare, retail, and public services. The multidisciplinary teams value knowledge-sharing, clean code, and measurable impact. Innovation budgets, peer mentoring, and dedicated learning hours keep skills razor-sharp.

What You Will Do  

- Engineer reproducible ML pipelines in Python 3.12 using TensorFlow 2.x, PyTorch 2.x, and scikit-learn.  

- Collect, cleanse, and augment structured and unstructured datasets to improve model robustness.  

- Train supervised and unsupervised algorithms, then benchmark accuracy, precision, recall, and AUC.  

- Debug model drift, memory leaks, and numerical instability; document findings in concise READMEs.  

- Deploy experiments through Docker, Git, and automated CI/CD workflows on AWS SageMaker.  

- Monitor live inference endpoints, tune hyper-parameters, and cut latency through vectorized operations.  

- Collaborate with software, product, and UX teams to integrate predictions into user-facing applications.  

- Present insights, graphs, and trade-offs to non-technical stakeholders in clear business language.

Must-Have Qualifications  

- Bachelor’s degree in Computer Science, Data Science, Statistics, or related US program.  

- 0-2 years of hands-on coding with Python, Numpy, Pandas, Matplotlib.  

- Academic or internship projects applying TensorFlow or PyTorch to classification or regression.  

- Working knowledge of algorithms (linear regression, CNNs, RNNs, gradient boosting).  

- Familiarity with Linux, Git workflows, and unit testing.  

- Analytical mindset, curiosity, and the confidence to ask why.  

- Strong written and verbal communication; able to explain math intuitively.  

- Eligibility to work in the United States without sponsorship.

Nice-to-Have Extras  

- Exposure to AWS, GCP, or Azure ML services.  

- Experience with experiment tracking tools (MLflow, Weights & Biases).  

- Knowledge of data privacy frameworks such as HIPAA or PCI-DSS.  

- Participation in Kaggle competitions or open-source contributions.  

- Basic understanding of REST APIs or GraphQL.

What You Gain  

- Rapid skills expansion through paired programming and weekly tech talks.  

- Visibility into full product life-cycle from ideation to production.  

- Influence over model architecture decisions despite junior title.  

- Remote flexibility plus optional coworking stipends for on-site collaboration.  

- Performance-based advancement paths toward Machine Learning Engineer II within 12-18 months.