Senior ML Engineer - AI for Investing & Production Systems

Poesis; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Medical, dental, vision coverage

Catered lunches

Commuter benefits

Job summary

Poesis, an AI-native investment firm, seeks an exceptional Machine Learning Engineer to build systems powering alpha discovery, decision-making and risk management. You’ll own models, signals and evaluation frameworks across the full ML lifecycle, from experimentation to deployment.

Hybrid role with on-site collaboration in Menlo Park, CA. You’ll work with large financial datasets, apply modern AI systems, and contribute to production-grade ML workflows and agent-based signals.

Qualifications

• 5+ years experience as a Machine Learning Engineer, or related role.
• Experience developing, evaluating and deploying ML models in production environments.
• BS/MS/PhD in Computer Science or a related field, or equivalent practical experience.

Responsibilities

• Rapidly implement and iterate on ML models, signals and research ideas.
• Design and run experiments to evaluate and improve model performance and investment impact.
• Build reproducible workflows for feature generation, training, validation and evaluation.
• Work with large-scale financial datasets to identify predictive signals and improve model performance.

Skills

Machine Learning

Python

SQL

Production ML

Experimentation

Reproducible Workflows

LLMs

Codex/Claude

Education

CS degree or equivalent

Tools

PyTorch

TensorFlow

Codex

Claude Code

Job description

Poesis, an AI-native investment firm, seeks an exceptional Machine Learning Engineer to build systems powering alpha discovery, decision-making and risk management. You’ll own models, signals and evaluation frameworks across the full ML lifecycle, from experimentation to deployment.

Hybrid role with on-site collaboration in Menlo Park, CA. You’ll work with large financial datasets, apply modern AI systems, and contribute to production-grade ML workflows and agent-based signals.