Senior Data/Machine Learning Engineer

The Coca-Cola Company•Atlanta, GA

2d•$171,000 - $198,000

About The Position

As a Tech Lead specializing in Machine Learning and Data Engineering, you will guide the technical direction of end-to-end machine learning capabilities embedded in digital products, while ensuring that their data foundations—including events, pipelines, feature tables, and governance—are reliable and scalable. This is a hands-on technical leadership role without formal people-management responsibilities. You will work with Product, Design, Data Science/Analytics, and platform teams to frame problems, define success metrics, and guide solutions from data modeling and feature engineering through model training, deployment, monitoring, and iteration. You will build ML-powered data products that model transaction drivers and surface optimized actions as insights in integrated internal and external digital experiences across retail, foodservice, and digital channels. Product success is tied to measurable transaction lift at the point of sale. You will lead technical decisions and execution across the ML and data stack, set standards, mentor engineers, and help teams deliver reliable, measurable product outcomes. The role is based in Atlanta, Georgia, with 0%–25% travel and relocation provided. The stated U.S. base pay range is $171,000–$198,000, with an annual incentive reference value of 30%. Coca-Cola will not sponsor employment-based visas for this position; applicants must already be authorized to work full-time in the United States without requiring the company’s sponsorship.

Requirements

At least 6 years of experience in machine learning engineering, data engineering, or software engineering, including leading technical direction for ML or data systems.
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Understanding of supervised learning, evaluation metrics, and common failure modes.
Strong Python programming skills and ability to write production-quality code, including testing, readability, and performance.
Strong Python and SQL engineering fundamentals and familiarity with production practices such as testing, reviews, and CI/CD.
Ability to analyze datasets with SQL and/or Python, identify issues, and reason about bias and data leakage.
Demonstrated ownership of model development and evaluation, including metric selection, error analysis, and disciplined experimentation.
Experience shipping and operating production ML systems, including model monitoring, rollback or retraining strategies, and coordination with upstream data and feature pipelines.
Familiarity with data warehouse or lakehouse concepts and exposure to orchestration or ETL tools.
Product mindset focused on measurable impact, guardrails, and user experience as well as model metrics.
Ability to collaborate with Product, Data Science, and Engineering to ship and iterate on ML features.
Fluency with MLOps and data platforms, including deployment, monitoring, reproducibility, and the pipelines, warehouses, or streams that feed models.
Ability to lead through influence, communicate clearly across disciplines, align stakeholders on definitions and tradeoffs, mentor engineers, and uphold technical standards.
Must be currently authorized to work full-time in the United States and must not require Coca-Cola sponsorship to continue working legally in the United States.

Nice To Haves

Familiarity with ML frameworks such as PyTorch or TensorFlow and data tools such as Spark, dbt, Airflow, or Dagster.
Experience building product ML systems such as personalization, recommendations, ranking, forecasting, or NLP.
Experience with experimentation and measurement, including A/B testing, uplift or impact analysis, and online guardrails.
Experience with feature pipelines or feature stores and training-serving consistency patterns.
Experience designing and operating batch and streaming data pipelines for ML, with clear freshness and quality SLAs.
Experience with lakehouse or warehouse modeling for analytics and ML, including dimensional or event models, backfills, schema evolution, and data contracts.
Experience with model and data observability, including drift detection, performance monitoring, dashboards, and alerting.
Familiarity with responsible AI and data privacy considerations, including PII handling, access controls, and model risk.
Experience with production infrastructure such as Docker or Kubernetes, or workflow tools such as Airflow or Dagster for running ML jobs.
Familiarity with modern engineering practices such as CI/CD, testing, and observability.
Experience demonstrating tech lead behaviors, including driving design reviews, setting standards, mentoring engineers, and aligning stakeholders on tradeoffs.

Responsibilities

Lead technical direction for a product ML domain, including problem framing, approach selection, evaluation strategy, and iteration.
Define and develop data and feature foundations, including event and telemetry definitions, transformation logic, feature and label tables, and training-serving consistency.
Build model baselines and iterate on approaches suited to product problems, such as gradient boosting, deep learning, and ranking.
Lead feature engineering, define entities and joins, validate labels, and ensure training-serving consistency.
Run experiments and evaluate models using appropriate methodology, including train/validation splits, cross-validation where appropriate, and error analysis.
Document findings and recommendations for technical and non-technical audiences.
Deploy production models in batch and/or real time, balancing latency, reliability, and cost.
Implement monitoring for upstream data and feature freshness and quality, model drift, and model performance; define alerts and response playbooks.
Automate repeatable training and evaluation workflows, including versioning, reproducibility, and artifact tracking.
Participate in incident response and post-incident reviews when model behavior affects customers or operations.
Establish reusable patterns for feature pipelines, backfills, and schema evolution, and raise engineering standards through design reviews.
Define and reinforce data governance and responsible ML standards, including PII handling, access controls, data contracts, and bias and fairness considerations.
Partner with platform teams on data platforms and MLOps tooling, including warehouses or lakehouses, streaming, orchestration, feature stores, training infrastructure, deployment, and monitoring.
Conduct and contribute to design reviews and code reviews, improve documentation, and establish reusable patterns for ML and data workflows.
Mentor and enable engineers, coach them through complex work, and unblock delivery across teams.
Collaborate with Product, Design, Data Science/Analytics, Engineering, and platform teams to define problems, success metrics, solutions, and tradeoffs.
Operate and continuously improve ML systems and their supporting data pipelines, including quality, freshness, monitoring, and operational readiness.