Python Machine Learning Engineer

Artificial intelligence has evolved past experimental proofs of concept; it is now the foundational engine driving global enterprise profitability and technological supremacy. However, scaling complex machine learning systems requires exceptional engineering rigor, advanced algorithmic understanding, and robust data pipelines. World-class frontier labs and global enterprises turn to Turing to accelerate research, train advanced AI agents, and build reliable production-grade intelligence.

Headquartered in San Francisco, California, Turing operates as the world’s leading research accelerator for frontier AI labs. By connecting elite global engineers with high-impact projects, Turing bridges the gap between theoretical AI research and measurable enterprise impact. Currently, Turing is seeking expert Python Machine Learning Engineers to drive sophisticated data science initiatives.

About Turing: Powering the Intelligence Layer

Turing supports the global AI ecosystem in two fundamental ways. First, it accelerates frontier research by providing high-quality data, advanced training pipelines, and elite AI researchers specializing in coding, reasoning, multimodality, and autonomous agents. Second, Turing applies this deep expertise to help global enterprises transform AI from theoretical concepts into proprietary, reliable intelligence systems.

Joining Turing as a Machine Learning Engineer places you at the epicenter of this technological revolution, working alongside industry pioneers to shape how next-generation AI systems learn and reason.

Role Overview & Day-to-Day Responsibilities

As a Machine Learning Developer at Turing, your core mandate is to drive the design, development, and delivery of advanced machine learning solutions. You act not just as an individual contributor, but as a technical leader capable of guiding architectural direction and mentoring multidisciplinary teams.

Required Skills & Technical Qualifications

This engagement demands deep technical fluency across core machine learning disciplines and software engineering standards. Candidates must meet the following baseline criteria:

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field.
• Experience: 4+ years of hands-on data science and machine learning development experience.
• Core Frameworks & Languages: Expert-level proficiency in Python and essential data libraries (Pandas, NumPy, Scikit-Learn).
• Domain Mastery: Solid grasp of Supervised and Unsupervised Learning, Time-Series Forecasting, Natural Language Processing (NLP), Computer Vision (CV), and Statistical Modeling.
• Production Engineering: Proven ability to design scalable, production-grade ML systems with rigorous data preprocessing and feature engineering.

Preferred Qualifications & Bonus Expertise

Candidates possessing advanced specializations will find exceptional alignment with Turing’s project requirements:

• Deep learning expertise, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer architectures.
• Hands-on experience with cloud data platforms (AWS, Databricks) and PySpark.
• Competitive ML background (e.g., Kaggle competitions, MLEBench benchmarking).

Engagement Details, Commitment & Location

Designed for flexibility and professional autonomy, this 3-month contractor assignment offers fully remote work. Eligible geographic locations include India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, and Mexico.

Commitment Options: Choose between 20, 30, or 40 hours per week (at least 4 hours per day), requiring a 4-hour daily overlap with Pacific Standard Time (PST).

Onboarding & Stability: To ensure long-term stability and unlock access to advanced tasks, engineers complete initial onboarding and their first 10 hours of project work promptly upon selection.

The Evaluation Process

Turing maintains a streamlined yet rigorous evaluation process designed to validate technical excellence within approximately 75 minutes:

1. Round 1 (Technical Interview — 60 mins): Deep dive into core algorithms, Python proficiency, system design, and ML architecture.
2. Round 2 (Onboarding & Cultural Discussion — 15 mins): Alignment on project expectations, workflow integration, and cultural fit.