Software Engineer- Workflows

Next Phase Recruitment; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Next Phase Recruitment is assisting a San Francisco-based deep tech company in recruiting a Software Engineer to advance AI-driven molecular discovery. You will join a team merging software, scientific research, and scalable infrastructure to enable synthetic data generation, model training, and experiments that accelerate drug discovery and physics-based simulation.

You will design and operate production workflows in Python, build asynchronous data pipelines, and create clear APIs for

Qualifications

• 2+ years of professional software engineering in data, scientific, or ML workflows.
• Experience designing and operating production workflows with Temporal, Airflow, Flyte, Dagster, or Prefect.
• Strong Python proficiency; PyTorch experience desirable.
• Experience collaborating with research scientists or data teams.

Responsibilities

• Build workflow layers powering data generation and model training.
• Modernize scientific tooling into scalable software.
• Design clean Python APIs for experiments.
• Ensure reproducibility, observability, and traceability across compute boundaries.
• Collaborate with infrastructure on scheduling, capacity, and cost optimization.
• Apply knowledge of distributed computing and asynchronous execution.
• Design APIs with clarity, consistency, and minimalism.
• Debug distributed systems and integrate vendor technologies.
• Work with scientific computing codebases.

Skills

Python

PyTorch

Distributed systems

Asynchronous workflows

API design

Research collaboration

Tools

Temporal

Airflow

Flyte

Dagster

Prefect

Job description

This is a unique opportunity for an accomplished Software Engineer to play a key role in shaping the future of AI-driven molecular discovery. This position sits at the intersection of cutting-edge software engineering, scientific research, and scalable infrastructure, empowering world-class scientists to generate synthetic data, train models, and run complex experiments that accelerate drug discovery and physics-based simulation.

The Employer

Our client is a pioneering deep technology company using geometric deep learning, physics, and quantum chemistry to build advanced molecular foundation models. Their work enables next-generation drug discovery by combining AI and physical simulation to create faster, more interpretable, and reliable insights in the chemical and biological sciences. Their mission is to bridge the worlds of AI and quantum mechanics, modernizing how molecular dynamics are conducted and transforming pharmaceutical R&D. With vibrant hubs in San Francisco and New York City, the company offers a collaborative, intellectually stimulating environment where science and engineering truly intersect.

Qualifications & Experience

• Minimum 2 years of professional software engineering experience in data, scientific, or ML workflow systems
• Proven ability to design and operate production workflows using tools such as Temporal, Airflow, Flyte, Dagster, or Prefect
• Deep proficiency in Python; experience in PyTorch environments is highly desirable
• Demonstrated success working collaboratively with research scientists or data-intensive teams

Responsibilities

As a Software Engineer embedded within a scientific team, you’ll build the workflow and application layers that power synthetic data generation and model training pipelines.

• Develop robust, multi-stage asynchronous workflows for data generation and evaluation
• Modernize existing scientific tooling into scalable, maintainable software
• Design clean, intuitive Python APIs that empower scientists to build experiments efficiently
• Ensure workflows are reproducible, observable, and traceable across compute and model boundaries
• Collaborate with infrastructure engineers on scheduling, capacity, and cost optimization
• Strong knowledge of distributed computing and asynchronous execution
• Skilled in API design, with emphasis on clarity, consistency, and minimalism
• Experience debugging distributed systems and integrating vendor technologies
• Comfort working within or alongside scientific computing codebases

Nice to Haves

• Experience designing professional Python libraries for data or research workflows
• Background or exposure to quantum chemistry, molecular simulation, or related computational pipelines

This position offers a rare chance to work at the crossroads of AI, physics, and large-scale computation, helping to shape the foundation of molecular research of the future.