Build and operate production machine learning systems at an early-stage AI recruiting and talent technology company. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that support the core product.
What You'll Do- Design, train, and evaluate machine learning models for production use cases.
- Build end-to-end ML pipelines, including data preprocessing, model serving, and monitoring.
- Partner with product and engineering teams to turn business needs into ML solutions.
- Debug and improve production model performance using monitoring and real-world feedback.
- Write maintainable code and contribute to ML infrastructure and tooling.
- Participate in code reviews and share knowledge with teammates.
What We're Looking For- At least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.
- Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation.
- Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
- Experience implementing end-to-end ML pipelines and maintaining deployed systems at scale.
- Familiarity with MLOps and cloud platforms or tools such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
- Experience with production A/B testing or experimentation, and debugging models based on operational feedback.
- Background in a startup or fast-moving, product-focused environment, plus completed university studies and professional employment after graduation.
Compensation & BenefitsBase salary starts at $140,000 USD annually. The upper end of the range was not provided. Visa sponsorship policy was not provided.
LocationOn-site in San Francisco, California, United States.
