Build and deploy ML models at scale, working on cutting-edge AI-driven solutions for programmatic advertising. Collaborate with data science and engineering teams to create a new software layer in ad-tech.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Senior Machine Learning Engineer
Location: Fully Remote US based (Hybrid option in NYC)
Compensation: $190,000–$200,000 + Equity
Type: Full-Time
About the Company
We are partnering with a fast-growing, profitable SaaS company that is redefining programmatic advertising through AI-driven solutions. Their platform empowers brands to deploy custom algorithms across major DSPs, enabling smarter, real-time ad-buying decisions. Backed by leading industry investors, this company is building cutting-edge ML infrastructure and solving problems that don’t have off-the-shelf solutions.
Why Join?
- Work on distributed ML infrastructure using Ray + PyTorch on Databricks.
- Build solutions that create a new software layer in ad-tech.
- Flexible working hours, fully remote, and equity participation.
- Opportunity to grow into Principal ML Engineer later this year.
Role Overview
As a Senior Machine Learning Engineer, you’ll own the ML lifecycle end-to-end, focusing on productionization and robust MLOps practices. You’ll work closely with data science and engineering teams to deploy models at scale and build automation for training, inference, and observability.
Key Responsibilities
- Deploy ML models into production using CI/CD best practices.
- Monitor and manage ML drift; retrain models as needed.
- Build automation for ML lifecycle (training, inference, observability).
- Contribute to internal API development for future projects.
- Collaborate across teams to productionize models for client and internal use cases.
Must-Have Skills
- Strong experience with CI/CD (GitHub Actions, build automation, packaging).
- Expertise in MLOps (MLflow, model versioning, monitoring).
- Hands-on with Databricks (Delta Lake, Unity Catalog, Asset Bundles).
- Proficient in Python and PySpark.
- 3–6 years of relevant experience.
Nice-to-Have Skills
- Kubernetes and containerized environments.
- API development exposure.
- Distributed training (Ray) and observability tools (Prometheus, Grafana).
- Familiarity with embedding models and Databricks Clean Room.
Screening Process
- Intro Call (with Director of ML or Senior Engineer) + short live coding exercise.
- Take-home exercise (CI/CD-focused).
- Final interview with leadership.
- CEO check-in.
Interested?
Apply now to join a team building brand-new solutions in ad-tech and work with cutting-edge technologies like PyTorch, Ray, Databricks, and MLflow.