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Added 17 mins ago
Executive Software Engineer
ManpowerGroup
South San Francisco, CA
We are seeking a Software Engineer to join our team.
Added 17 mins ago
Senior Data Scientist - LLMs for Fraud Detection
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Mountain View, CA
Engineer features from huge, noisy, imbalanced data (billions of events) using Spark and SQL.
Added 17 mins ago
Senior Software Engineer, Data and Infrastructure, Professional Intelligence, DeepMind
Google
Mountain View, CA
Share Senior Software Engineer, Data and Infrastructure, Professional Intelligence, DeepMind
Added 17 mins ago
Senior Machine Learning Engineer - ML Training Infrastructure
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…advanced AI research and model development initiatives. As a Senior ML Engineer, you will collaborate closely with machine learning engineers, research scientists, and other partners to…
Added 17 mins ago
Senior Systems Architect, Precision Stage V - (E5)
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As a Mechanical Engineer, you’ll leverage your strong foundation in physics, mathematics, and materials science to process and resolve mechanical problems, design and implement custom…
Added 18 mins ago
Lead embedded Software Engineer (Board Bring Up)
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We are looking for a Senior/Lead Embedded Software Engineer to drive the iRDK bring-up and platform enablement on MediaTek C-X1 SoC platforms. In this highly visible technical leadership…
Added 18 mins ago
Senior Mechatronics Engineer
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Cisco
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As a Principal Engineer leading software security, you will be a primary technical authority responsible for defining and driving the security architecture across our software platforms and…
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Principal Network Engineer
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- $70 to $80 USD / hour
The Principle Network Engineer reports within the Professional Services (PS) team and is typically dedicated to a single customer. This role requires fluency in Juniper Networks…
Added 18 mins ago
CTO, Anthropic Practice
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You will be the practice's senior technical voice in front of clients. You shape the solution, defend it in the room, and make sure what we sell is what we can deliver. You will work…
Added 18 mins ago
Senior Software Engineer I, Data
Charles Schwab
San Francisco, CA
As our Senior Data Engineer focusing on data engineering, you will play a crucial role delivering a high-quality data platform that is responsible for maintaining and delivering Forge data…
Added 18 mins ago
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Principal Network Engineer
TEKsystems
San Diego, CA
- $70 to $80 USD / hour
The Principle Network Engineer reports within the Professional Services (PS) team and is typically dedicated to a single customer. This role requires fluency in Juniper Networks…
Added 19 mins ago
T
Principal Network Engineer
TEKsystems
Los Angeles, CA
- $70 to $80 USD / hour
The Principle Network Engineer reports within the Professional Services (PS) team and is typically dedicated to a single customer. This role requires fluency in Juniper Networks…
Added 19 mins ago
EG
Dev Lead
Eliassen Group
Sacramento, CA
- $110 to $120 USD / hour
2+ years as a senior or principal engineer with design, implementation, and operational ownership.
Added 33 mins ago
S
Senior Full Stack Engineer, AI Products - Stout Strategy
Stout
Denver, CO
- $87,000 to $195,000 USD / year
As a Senior Full-Stack Engineer in our Applied Innovation Group, you get the best of both worlds: working at the frontier of AI and knowledge work, inside a firm with the clients, expertise…
Added 54 mins ago
Senior Solution Engineer - Confluent
IBM
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Added 55 mins ago
EG
Dev Lead
Eliassen Group
Nashville, TN
- $110 to $120 USD / hour
2+ years as a senior or principal engineer with design, implementation, and operational ownership.
Added 1 hour ago
EG
Dev Lead
Eliassen Group
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- $110 to $120 USD / hour
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Added 1 hour ago
Senior Manager Data Engineer (Databricks, Pyspark, Snowflake)
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Maintain hands-on technical engagement by reviewing architecture designs, data models and pipeline code across Python, Spark, Databricks, and Snowflake environments
Added 1 hour ago
VU
Software Development Engineer- Product Reliability Engineering
Visa USA Inc
Denver, CO
- $40,123 USD / year
…product, and automation as a strategic advantage. You'll write Python, build agentic AI tools, manage data platforms, and contribute to the distributed systems that process billions of…
Added 1 hour ago
EG
Dev Lead
Eliassen Group
Springfield, IL
- $110 to $120 USD / hour
2+ years as a senior or principal engineer with design, implementation, and operational ownership.
Added 1 hour ago
Senior Specify Software Developer
University of Kansas
Lawrence, KS
Specify 7 is a mature, open-source web application built on Python, Django, TypeScript, and React. As a senior developer, take responsibility for significant features and technical…
Added 1 hour ago
EG
Dev Lead
Eliassen Group
Topeka, KS
- $110 to $120 USD / hour
2+ years as a senior or principal engineer with design, implementation, and operational ownership.
Added 1 hour ago
EG
Dev Lead
Eliassen Group
Salem, OR
- $110 to $120 USD / hour
2+ years as a senior or principal engineer with design, implementation, and operational ownership.
Added 2 hours ago
BN
Senior Motion Controls Engineer
Brookhaven National Laboratory
UPTON, NY
- $109,150 to $182,033 USD / year
…technologies * Demonstrated programming experience in C, C++, or Python * Experience in open-source software development using best practices, such as version control tools for…

The Coca-Cola Company

Senior Data/Machine Learning Engineer

Atlanta, GA, United States

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Job details

Experience: 6+years of experience in machine learning engineering, data engineering, or software engineering, including leading technical direction for ML/data systems
Posting language: English
Posted: Sep 22, 2026

Original job description

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.

What You Will Work On:

Build ML-powered data products that model transaction drivers and surface optimized actions as insights to be embedded within integrated internal and external digital experiences that shape how our beverage brands activate across retail, foodservice, and digital channels. The success of our products is tied directly to measurable transaction lift at the point of sale, a primary objective of the North America Operating Unit and The Coca-Cola Company as a whole.

How We Work

You’ll be part of a dedicated, cross-functional team (Product, Design, Engineering) that is:

Empowered to solve problems, not just build features
Accountable for outcomes, not output
Collaborative by default, from discovery through delivery
Continuously learning, using data and customer insight to improve

Key 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 approachesappropriate tothe product problem (e.g., gradient boosting, deep learning, ranking)
Lead feature engineering with strong data discipline: define entities and joins,validatelabels, and ensure training/serving consistency
Run experiments and evaluate models using soundmethodology(train/validation splits, cross-validation asappropriate, 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 behaviorimpactscustomers 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) andMLOpstooling (feature stores, training infrastructure, deployment, monitoring)

What We’re Looking For

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

Key Qualifications

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/lakehouseconcepts), and exposure to orchestration/ETL tools (e.g., Microsoft fabric, Airflow,dbt, Spark)

Preferred Qualifications

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 withlakehouse/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)

Education

Bachelor’s degree in Computer Science, Engineering, or a related field
Equivalent practical experience is equally valued

Who Thrives Here

Enjoy leading through influence - turning ambiguous problems into clear ML + data plans and helping others execute
Communicate clearly across Product, Data Science, Analytics, and Engineering - especially around definitions, tradeoffs, and risk
Take pride in raising the bar: reliable models and data pipelines, strong documentation, and operational follow-through

Who This Role Is Not For

This role may not be the right fit if you:

Want to focus only on research prototypes or only on data pipelines (instead of owning end-to-end product ML systems)
Avoid leading through influence (design reviews, alignment, mentorship) and prefer not to set or uphold technical standards
Prefer to avoid operational responsibility for model and data health (monitoring, incidents, data quality/freshness, and continuous improvement)

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class.

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More about this job

Skills

Machine learning
Data engineering
Data modelling
Point of sale
Customer insight
ML
Statistical quality control
Documentation
Coaching
Develop models
Incident response
Python
SQL
Collaboration
CI/CD
PyTorch
TensorFlow
Apache Spark
dbt
Apache Airflow
Data warehouse
Forecasting
Natural language processing
Docker
Kubernetes
Workflow
Computer science
Research
Process improvement

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