Senior Data/Machine Learning Engineer

The Coca-Cola Company•Atlanta, GA

1d•$171,000 - $198,000•Onsite

About The Position

Digital products play a central role in how we create value for customers, support the teams who serve them, and shape the consumer experience. Our product organization brings together small, empowered teams that move with clarity, speed, and purpose, enabling digital to be a meaningful source of advantage across Coca-Cola’s North America Operating Unit. Our work spans customer journeys, service delivery, sales workflows, and the platforms that connect them. We are raising our standards for product craft and rebuilding the systems behind these experiences. As a Tech Lead specializing in Machine Learning and Data Engineering, you will lead the technical direction for end-to-end ML capabilities that ship as part of our product, while also ensuring the data foundations (events, pipelines, feature tables, and governance) are reliable and scalable. You’ll partner 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. This is a hands-on leadership role for engineers who can set standards, unblock teams, and drive execution across the ML and data stack without formal people-management responsibilities.

Requirements

Applied ML fundamentals: Understands supervised learning, evaluation metrics, and common failure modes
Strong programming skills: Comfortable in Python and writing production-quality code (testing, readability, performance)
Data intuition: Able to analyze datasets with SQL and/or Python, spot issues, and reason about bias/leakage
Product mindset: Cares about measurable impact, guardrails, and user experience—not just model metrics
Cross-functional collaboration: Partners with Product, Data Science, and Engineering to ship and iterate on ML features
MLOps + data platform fluency: Comfortable with deployment, monitoring, reproducibility, and the pipelines/warehouses/streams that feed models
6+ years of experience in machine learning engineering, data engineering, or software engineering, including leading technical direction for ML/data systems
Demonstrated ownership of model development and evaluation, including metric selection, error analysis, and experimentation discipline
Strong engineering fundamentals in Python (and SQL) with production practices (testing, reviews, CI/CD); familiarity with ML frameworks (e.g., PyTorch/TensorFlow) and data tooling (e.g., Spark, dbt, Airflow/Dagster) is preferred
Experience shipping and operating ML systems in production, including model monitoring, rollback/retraining strategies, and coordination with upstream data/feature pipelines
Familiarity with data platforms (data warehouse/lakehouse concepts), and exposure to orchestration/ETL tools (e.g., Microsoft fabric, Airflow, dbt, Spark)
Bachelor’s degree in Computer Science, Engineering, or a related field
Equivalent practical experience is equally valued

Nice To Haves

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

Responsibilities

Technical direction for a product ML domain: problem framing, approach selection, evaluation strategy, and iteration
Data and feature foundations: event/telemetry definitions, transformation logic, feature/label tables, and training/serving consistency
Production ML systems: deployment patterns (batch/online), model performance/latency tradeoffs, and operational readiness
Quality and reliability: data quality checks, model monitoring (drift/performance), alerting, and runbooks
Engineering standards: design reviews, code review quality, documentation, and reusable patterns for ML + data workflows
Mentorship and enablement: coaching engineers through complex work and unblocking delivery across teams
Develop, Train & Evaluate Models: Build baselines and iterate on model approaches appropriate to the product problem (e.g., gradient boosting, deep learning, ranking)
Lead feature engineering with strong data discipline: define entities and joins, validate labels, and ensure training/serving consistency
Run experiments and evaluate models using sound methodology (train/validation splits, cross-validation as appropriate, error analysis)
Document findings and recommendations clearly for technical and non-technical audiences
Deploy & Operate Models in Production: Deploy models to production (batch and/or real-time) with attention to latency, reliability, and cost
Implement monitoring for upstream data and feature freshness/quality, drift, and model performance; define alerting and response playbooks
Automate repeatable training and evaluation workflows (versioning, reproducibility, and artifact tracking)
Participate in incident response and post-incident reviews when model behavior impacts customers or operations
Establish reusable patterns for feature pipelines (batch/stream), backfills, and schema evolution; raise the bar through design reviews
Define and reinforce standards for data governance and responsible ML (PII handling, access controls, data contracts, bias/fairness considerations)
Partner with platform teams on the data stack (warehouse/lakehouse, streaming, orchestration) and MLOps tooling (feature stores, training infrastructure, deployment, monitoring)