Senior ML Engineer (AI)

TL;DR

Senior ML Engineer (AI): Building and optimizing ML- and agent-driven infrastructure for scientific simulations with an accent on autonomously constructing, executing, and interpreting complex physics simulations. Focus on architecting agentic frameworks, developing pipelines for autonomous computational tasks, and productionizing agent behaviors for exploring chemical and materials landscapes.

Location: Onsite in Cambridge, MA USA

Salary: $176,000 – $304,000 USD per year

Company

hirify.global is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science, pioneering a new age of boundless discovery by applying AI to the scientific method.

What you will do

• Architect and implement agentic frameworks that support dynamic, multi-stage simulation workflows for scientific tasks.
• Develop pipelines enabling agents to autonomously plan, schedule, execute, and interpret computational tasks at scale.
• Build integration layers and APIs that connect ML models, large-scale simulation engines, databases, and heterogeneous compute platforms.
• Work with AI researchers to productionize agent behaviors, including tool-use strategies, simulation-aware decision loops, and adaptive task planning.
• Improve the robustness, modularity, performance, and reproducibility of agent-driven computational workflows.

Requirements

• MS/PhD or equivalent experience in Computer Science, ML/AI, Scientific Computing, or a related technical field.
• Strong experience building ML-driven pipelines, workflow systems, or tool-use frameworks, ideally for complex or scientific applications.
• Proficiency in Python and ML ecosystems.
• Familiarity with large-scale scientific or engineering software, including integrating external tools into automated computational workflows.
• Experience with distributed systems, HPC environments, cloud platforms, or accelerator-based computing.
• Deep understanding of modern ML architectures and their deployment in production systems.
• Strong engineering fundamentals: reproducibility, testing, modular design, CI/CD, and scalable ML operations.

Nice to have

• Experience developing or integrating agentic frameworks, autonomous ML pipelines, or multi-step tool-using agents, particularly for scientific applications.
• Background in large-scale simulation frameworks, scientific workflow orchestration, or automated computational experiment platforms.
• Contributions to open-source ML infrastructure, workflow engines, agent frameworks, or scientific software ecosystems.
• Familiarity with data-centric engineering practices.
• Experience with workflow orchestration systems (e.g., Flyte, Prefect, Airflow) and container orchestration (Kubernetes).
• Expertise in advanced ML techniques relevant to agentic reasoning (planning models, self-improving systems, multi-tool agents, RLHF/RLAIF workflows).

Culture & Benefits

• Committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
• Generous early equity along with bonus potential.
• Focus on solving humankind's greatest challenges in human health, climate, and sustainability.