Python Machine Learning Engineer
Overview
Turing pairs frontier AI labs with data, training pipelines, and specialized researchers, and helps enterprises move AI from prototype to production systems that deliver measurable business results. This remote contract role focuses on machine learning solution delivery using Python, with ownership across data pipelines, model design, deployment, and monitoring. The engineer sets technical direction, mentors peers, and keeps ML initiatives aligned with business priorities. Hands-on experience in Kaggle competitions or ML benchmarks is a strong signal for this position.
What You'll Do6
- 1Own the full machine learning lifecycle, including data pipeline construction, model architecture, deployment, and post-launch monitoring.
- 2Convert business goals into ML system designs that reflect the underlying logic and context of each use case.
- 3Partner with Product, Engineering, and Business teams to shape problem statements and define measurable success criteria.
- 4Assess and refine models for accuracy, scalability, and performance using current best practices.
- 5Track new developments in AI research and integrate useful techniques into ongoing projects.
- 6Set technical direction and mentor other developers on ML best practices.
Requirements9
- 1Hold a Bachelor's or Master's degree in computer science, machine learning, AI, statistics, or a related quantitative discipline.
- 2Bring at least 4 years of hands-on experience building and shipping data science and ML solutions.
- 3Work across core ML disciplines: supervised and unsupervised learning, time-series forecasting, natural language processing, computer vision, and statistical inference.
- 4Use Python with core libraries like Pandas, NumPy, and Scikit-learn for data manipulation, modeling, and evaluation.
- 5Apply strong data preprocessing, feature engineering, model tuning, and metric selection to real-world problems.
- 6Design and deliver scalable, production-grade ML systems.
- 7Deep learning expertise with architectures such as CNNs, RNNs, or transformers gives a strong advantage.
- 8Experience with cloud data platforms like Databricks or AWS, plus hands-on PySpark work, adds weight to your application.
- 9Competitive experience in Kaggle or benchmark suites such as MLEBench is a plus.
Who Should Apply
The ideal candidate has 4+ years of hands-on ML development, strong Python skills, and a track record of shipping production-grade systems. They should be comfortable across supervised and unsupervised learning, time-series, NLP, and computer vision, with the ability to lead technical direction and mentor peers. This role is less suitable for engineers who prefer pure research without deployment responsibilities or who are not prepared to work under a strict time commitment with PST overlap. Common reasons candidates score low include lacking demonstrated experience with scalable ML architectures, or having no evidence of competitive or benchmark-driven work such as Kaggle.
Location
Required Skills
Application Tip
In your application, present one project where you owned the entire ML lifecycle, and quantify the business impact with metrics like model accuracy, latency, or cost savings. If you have competitive results on Kaggle or benchmark suites like MLEBench, include your rankings or scores; these are explicit strong pluses for this role.
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1Application received
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