Senior Staff/Technical Lead Machine Learning Engineer
Harnham — San Francisco Bay Area, US
- Published on
About the Role
Harnham is seeking a Senior Staff/Technical Lead Machine Learning Engineer to join their growing team focused on behavioral modeling and personalization. This role will involve:
- Designing, building, and deploying end-to-end machine learning models tailored for ad optimization and recommendation systems.
- Developing ML pipelines that leverage rich behavioral signals to enhance user value and business ROI.
- Collaborating with product and R&D teams to execute high-impact, ML-first product strategies.
- Leading experimentation and model evaluation in a fast-paced, data-rich environment.
- Contributing to scalable infrastructure development using tools like Airflow, Spark, and CI/CD platforms.
About the Candidate
The ideal candidate will have:
- An MSc or PhD in a STEM field.
- Proven experience in building and deploying machine learning systems commercially.
- A strong background in AdTech or recommender systems, with a solid understanding of personalization, targeting, or user intent modeling.
- Hands-on experience with orchestration tools (Airflow, Bazel) and data infrastructure (Spark, SQL, Scala, or Python).
- Knowledge of DevOps practices including CI/CD and model monitoring.
- Familiarity with cloud solutions (AWS, Databricks) and ML tools (MLFlow, TensorFlow, Kubernetes).
- Strong collaboration and communication skills with a product-oriented approach and a proactive, hands-on attitude.
About the Company
Harnham is a fast-growing, mission-driven tech company based in the San Francisco Bay Area. They specialize in leveraging one of the largest consented behavioral datasets in the US to deliver AI solutions for top global brands. The team consists of seasoned ML professionals who work with leading cloud providers to introduce innovative AI products to the market.
Salary and Benefits
- Competitive salary range of $300,000 - $350,000.
- Meaningful equity and financial upside.
- Fully remote work options are available, along with hybrid options in Boston, San Francisco Bay Area, Seattle, and New York City.
How to Apply
Candidates interested in this role should submit their CV via the application link provided.