Top Hybrid Machine Learning Engineer Jobs

Work on production features for an AI-native fleet management platform: write Python/JavaScript/SQL code, design data pipelines and analytics, and gain hands-on experience with web development and AI/ML workflows.

Develop and deploy AI-enabled computational methods for small-molecule hit and lead generation. Responsibilities include selecting and optimizing machine learning, deep learning, cheminformatics, molecular generation, affinity prediction, pharmacological property prediction, and retrosynthetic analysis workflows. The scientist collaborates with medicinal chemistry, structural biology, biochemistry, and computational teams to define project criteria, communicate methodology, and support early-stage drug discovery campaigns.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Founding engineer responsible for architecting and building core product systems, writing scalable Python backend code, designing and maintaining AI/ML infrastructure, instituting best development practices (testing, CI/CD), collaborating on product roadmap, and mentoring future engineers.

Lead end-to-end machine learning architecture, production systems, forecasting, probabilistic modeling, experimentation, and agentic AI development. Build models for acquisition cost, lifetime value, ROAS, demand, and market outcomes; establish reproducible data and model pipelines; create evaluation harnesses for LLM agents; and own calibration, feedback loops, measurement, and cost controls. Mentor cross-disciplinary engineers and influence technical strategy across teams.

Develop and improve search quality through personalized ranking signals, machine learning models, domain-adapted language models, and LLM-powered search experiences. Responsibilities include designing, training, evaluating, and deploying production-ready models, writing maintainable code, collaborating with customers and cross-functional teams, and mentoring or learning from engineers. The role is hybrid in San Francisco, requiring four office days weekly.