Senior Staff / Technical Lead Machine Learning Engineer

Harnham; Plano (TX); Remote; USD 130,000 - 350,000; Full time

Job summary

Harnham is seeking a Senior Staff / Technical Lead Machine Learning Engineer to join a mission-driven tech company specializing in behavioral modeling. This fully remote role involves designing machine learning models for ad optimization and personalization while collaborating with R&D and product teams. Ideal candidates have a strong AdTech background and experience in delivering ML solutions.

Benefits

Meaningful Equity & Financial Upside

Fully Remote (with hybrid options in select cities)

Qualifications

• Proven experience building and deploying machine learning systems in a commercial setting.
• Strong background in AdTech or recommender systems with knowledge of personalization and targeting.
• Hands-on experience with orchestration tools and data infrastructure.

Responsibilities

• Design, build, and deploy end-to-end machine learning models focused on ad optimization and recommendation systems.
• Develop ML pipelines to drive user value and business ROI.
• Lead experimentation and model evaluation in a fast-paced, data-rich environment.

Skills

Machine Learning Engineering

AdTech

Personalization

Recommender Systems

MLOps

CI / CD

Airflow

Spark

Python

AWS

Education

MSc or PhD in a STEM field

Tools

Airflow

Spark

AWS

Databricks

MLFlow

TensorFlow

Kubernetes

Job description

Senior Staff / Technical Lead Machine Learning Engineer

Remote

Up to $350,000 + Equity

Company :

A fast-growing, mission-driven tech company in the behavioral modeling and personalization space is seeking a Senior Staff / Technical Lead Machine Learning Engineer to join and lead a team. Their platform leverages one of the largest consented behavioral datasets in the US to deliver private-by-design AI solutions for top global brands and platforms.

The team is made up of seasoned ML professionals from Top Companies, and they are partnered with major cloud providers to bring novel AI products to market. This role will be central to building and scaling ML-powered products, with a focus on ad optimization and recommendation systems

Role :

• Design, build, and deploy end-to-end machine learning models focused on ad optimization, personalization use cases and recommendation systems
• Develop ML pipelines and production systems that leverage rich behavioral signals to drive user value and business ROI.
• Partner with product and R&D teams to ideate and execute on high-impact, ML-first product strategies.
• Lead experimentation and model evaluation in a fast-paced, data-rich environment.
• Contribute to the development of scalable infrastructure using tools like Airflow Spark , and CI / CD platforms.
• Work cross-functionally to bring zero-to-one ML products to market and continuously refine them post-launch.
• Stay on the pulse of emerging ML techniques in RecSys, behavioral modeling, and model optimization.

Requirements :

• MSc or PhD in a STEM field.
• Proven experience building and deploying machine learning systems in a commercial setting.
• Strong background in AdTech or recommender systems (RecSys) – with a clear understanding of personalization, targeting, or user intent modeling.
• Hands-on experience with orchestration tools ( Airflow Bazel ), data infrastructure ( Spark SQL Scala , or Python
• DevOps knowledge including CI / CD best practices and production model monitoring.
• Familiarity with cloud platforms (AWS, Databricks) and ML tooling (MLFlow, TensorFlow, Kubernetes).
• Strong collaboration and communication skills, with a product-oriented mindset.
• A bias toward action and a "roll up your sleeves" attitude—ideal for a dynamic startup environment.

Salary and Benefits :

• Meaningful Equity & Financial Upside
• Fully Remote (with hybrid options in Boston, SF Bay Area, Seattle, NYC)

How to Apply :

Please register your interest by submitting your CV via the Apply link on this page.

Desired Skills and Experience :

Machine Learning Engineering, AdTech, Personalization, Recommender Systems, MLOps, CI / CD, Airflow, Spark, Python, AWS

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