Senior Staff Machine Learning Engineer
Harnham — Fremont, US
- Published on
About the Role
Harnham is seeking a Senior Staff Machine Learning Engineer who will lead a team and play a crucial role in building and scaling ML-powered products, particularly in ad optimization and recommendation systems. This position is fully remote, allowing you to work from any location.
Responsibilities
- 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 utilize behavioral signals to maximize user value and drive business ROI.
- Collaborate with product and R&D teams to implement and execute on impactful, ML-first product strategies.
- Lead experimentation and model evaluation in a fast-paced, data-rich environment.
- Contribute to creating scalable infrastructure with tools such as Airflow, Spark, and CI/CD platforms.
- Work cross-functionally to launch brand new ML products and continuously refine them post-launch.
- Stay updated on emerging ML techniques in RecSys, behavioral modeling, and model optimization.
About the Candidate
Candidates should 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 robust understanding of personalization and targeting.
- Hands-on experience with orchestration tools like Airflow and Bazel, and data infrastructure tools such as Spark, SQL, Scala, or Python.
- DevOps knowledge related to CI/CD best practices and production model monitoring.
- Familiarity with cloud platforms like AWS and ML tooling like MLFlow and TensorFlow.
- Excellent collaboration and communication skills, with a product-oriented mindset, and a proactive, action-oriented approach suitable for startups.
Company Culture and Benefits
Harnham offers a competitive salary between $300,000 and $350,000, meaningful equity, and financial growth opportunities. This is a fully remote role, with hybrid options available in major cities like Boston, San Francisco Bay Area, Seattle, and New York City.
How to Apply
Interested candidates are encouraged to apply by submitting their CV through the application link provided on this page.