Job description
We’re seeking a Senior AI Engineer to join our San Francisco-based AI studio. You will architect and deploy scalable machine learning systems, lead model development from concept to production, and collaborate with product and data teams to drive real customer impact. This role blends research, engineering, and operations to deliver dependable AI features at scale.
As part of our team, you’ll own end-to-end ML initiatives, including data pipelines, feature stores, model training, evaluation, deployment, and monitoring. You will mentor junior engineers, contribute to architecture decisions, and help shape our ML roadmap.
What you’ll do:
- Design and implement end-to-end AI solutions from problem framing to production deployment.
- Build scalable data pipelines, feature engineering workflows, and model monitoring systems.
- Collaborate with data engineers and software engineers to integrate ML into products.
- Experiment, evaluate, and select models with rigorous methodologies.
- Optimize models for latency, reliability, and cost; implement MLOps best practices.
- Lead cross-functional teams and mentor junior AI engineers.
- Communicate complex technical concepts to non-technical stakeholders.
Responsibility
- Design and implement end-to-end AI solutions from problem framing to production deployment.
- Build scalable data pipelines, feature engineering workflows, and model monitoring systems.
- Collaborate with data engineers and software engineers to integrate ML into products.
- Experiment, evaluate, and select models with rigorous methodologies.
- Optimize models for latency, reliability, and cost; implement MLOps best practices.
- Lead cross-functional teams and mentor junior AI engineers.
- Communicate complex technical concepts to non-technical stakeholders.
Qualification
- MS or PhD in CS, AI, ML, or related field; or equivalent practical experience.
- 5+ years of AI/ML engineering with hands-on deep learning.
- Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
- Experience deploying ML models to cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
- Strong background in ML fundamentals: evaluation, generalization, bias, and deployment considerations.
- Experience with MLOps tools (MLflow, Kubeflow) and monitoring/observability.
- Excellent communication skills and ability to lead cross-functional initiatives.
- Publications or demonstrable impact in AI/ML projects is a plus.