Immediate Start: Associate ML Engineer

Job Description

Overview

The Associate Machine Learning Engineer will support the design, development, and delivery of ML solutions that improve business outcomes. You'll contribute to model experimentation, data preparation, and deployment tasks under the guidance of senior team membershelping ship reliable, well-tested ML features that make a measurable impact.

Prodege

A cutting-edge marketing and consumer insights platform, Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands, marketers, and agencies uncover the answers to their business questions, acquire new customers, increase revenue, and drive brand loyalty & product adoption. Bolstered by a major investment by Great Hill Partners in Q4 2021 and strategic acquisitions of Pollfish, BitBurst & AdGate Media in 2022, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences.

As an organization, we go the extra mile to Create Rewarding Moments every day for our partners, consumers, and team. Come join us today

Primary Objectives

- Assist with Applied ML Model Development & Evaluation
- Data Preparation, Basic Feature Engineering & Experimentation
- Support Model Deployment, Monitoring & Iteration
- Cross-Functional Collaboration with Data, Product & Engineering
- Code Quality, Testing, and Documentation
- Practical Problem Solving with an 80/20 mindset

Qualifications - To perform this job successfully, an individual must be able to perform each job duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Detailed Job Duties: (typical monthly, weekly, daily tasks which support the primary objectives)

- Implement baseline ML models (e.g., classification, regression, simple ranking) in Python using common libraries (scikit-learn, XGBoost; basic familiarity with PyTorch or TensorFlow).
- Prepare datasets: clean/transform data, create simple features, run exploratory analysis, and document assumptions.
- Run experiments with clear metrics; compare models; record results, trade-offs, and recommendations.
- Package models for deployment (batch jobs or simple APIs) with guidance from software/data engineering; add basic logging/telemetry.
- Write clean, testable code; contribute unit tests; participate in code reviews and follow version control standards.
- Monitor model performance (accuracy, latency, data/label drift) and help troubleshoot issues; propose incremental improvements.
- Translate requirements into small ML tasks; communicate progress, blockers, and risks to partners.
- Convert exploratory notebooks into reproducible scripts/jobs and contribute to lightweight utilities that improve team velocity.

What does SUCCESS look like

Success means contributing reliable ML components that reach production and measurably improve team KPIs (e.g., accuracy, conversion lift, reduced false positives). You're recognized for dependable execution, clear documentation, thoughtful experimentation, and collaborative communication that helps the team deliver value faster.

The MUST Haves: (ex: job cannot be done without these skills, education, experience, certifications, licenses)

- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent practical experience).
- One or more (1+) years of hands-on experience applying ML (industry, internship, research, or significant projects).
- Proficiency in Python and fundamentals of software engineering (testing, modular code, version control); familiarity with SQL.
- Practical experience with core ML techniques (e.g., tree-based models, linear/logistic regression, basic clustering) and evaluation methods.
- Basic familiarity with a deep learning framework (PyTorch or TensorFlow) and understanding of when classical ML may be preferable.
- Exposure to building simple data pipelines or training/serving jobs; familiarity with Spark or similar is a plus.
- Exposure to deploying models (batch or simple real-time) and monitoring basic health/performance metrics.
- Clear written and verbal communication; collaborative working style.
- Strong analytical/problem-solving skills and a bias toward practical, timely solutions (80/20).

The Nice to Haves: (preferred additional skills, education, experience, certifications, licenses)

- Experience with cloud ML tooling (AWS, GCP, or Azure), MLflow/model registries, or introductory MLOps practices.
- Basic understanding of APIs/microservices and comfort containerizing workloads (Docker).
- Familiarity with NLP/LLM tooling (e.g., Hugging Face, embeddings, retrieval) and prompt or fine-tuning workflows.
- Relevant certifications or advanced coursework in ML, data science, or cloud.