Spearhead the full journey of deep learning models from initial conceptualization to large-scale production deployment
Elevate our technical standards by implementing robust containerization, versioning, and monitoring practices
Collaborate with domain experts to translate ambiguous business needs into actionable machine learning tasks
Research and adapt breakthrough methods into our production environment
Serve as a pivotal technical resource, providing informal guidance and sharing best practices

Requirements

Bachelor's degree in Computer Science, Math, Engineering, Statistics, or a related quantitative field with 3-5 years of relevant experience, or a Master's degree with 1-3 years of relevant experience
In-depth conceptual and practical knowledge of machine learning and deep learning principles, including model selection, training, and evaluation methodologies
Proven track record as a Machine Learning Engineer or similar role, with evidence of production-ready ML work, including shipped projects, open-source contributions, or strong GitHub portfolios
Fluency in Python and deep learning frameworks (PyTorch is a must, TensorFlow is welcome)
Strong familiarity with NumPy, pandas, and scikit-learn
Experience with Linux and bash scripting
Solid grounding in data structures, data modeling, and software architecture
Practical statistical sense, with the ability to run experiments with rigor
Strong algorithmic and problem-solving skills

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdeep learningmodel selectiontraining methodologiesevaluation methodologiesPythonPyTorchNumPypandasscikit-learn

Soft Skills

collaborationproblem-solvingguidancebest practices

Certifications

Bachelor's degreeMaster's degree