Forward Deployed Scientific Software Engineer

LabACT; Boston (MA); On-site; USD 120,000 - 180,000; Full time

Job summary

LabACT seeks a Forward Deployed Scientific Software Engineer to work directly with biotech and pharmaceutical teams, turning complex scientific workflows into practical software.

You will embed with clients, understand data and constraints, and design and build software that improves workflows, from discovery through production deployment, in a consulting engagement model.

Qualifications

• Strong software engineering fundamentals and experience shipping production systems.
• Proficiency in Python and experience with modern application development.
• Experience working with relational databases and complex data.
• Practical cloud experience, particularly AWS, Azure, or GCP.
• Experience with containers, CI/CD, source control, automated testing, and production deployment.
• Ability to move between frontend, backend, data, infrastructure, and integration work as needed.
• Strong debugging skills and persistence with unfamiliar systems.
• Ability to communicate clearly with technical and non-technical stakeholders.
• Comfort working directly with clients and asking questions to understand problems behind requests.
• Good technical judgment on when to build sophisticated vs simple solutions.

Responsibilities

• Embed with biotech and pharmaceutical teams to understand scientific workflows and data systems.
• Translate ambiguous problems into clear technical requirements and implementation plans.
• Design, prototype, build, test, and deploy production software.
• Build scientific web applications, APIs, data pipelines, workflows, and internal tools.
• Connect systems across data sources, cloud infrastructure, and enterprise apps.
• Apply AI and ML where advantageous, without overreliance on AI.
• Work with scientists, informatics teams, engineers, and business stakeholders.
• Investigate unfamiliar domains and become productive quickly.
• Modernize or replace fragile spreadsheets, scripts, and legacy apps.
• Design systems with data provenance, reproducibility, security, auditability, and validation in mind.
• Troubleshoot across code, data, infra, networking, and deployment environments.
• Document systems well for client teams to operate and extend them.
• Identify reusable patterns across engagements.

Skills

Python

Relational databases

Cloud experience

Containers

CI/CD

Frontend/backend

Communication

Tools

PostgreSQL

AWS

React

Git

Job description

LabACT helps biotech and pharmaceutical teams turn complex scientific and operational problems into working software.

We work directly with scientists, informatics teams, data teams, and business leaders to understand how work actually gets done, identify where technology can make a meaningful difference, and build practical systems that fit into real scientific environments.

We are looking for a Forward Deployed Scientific Software Engineer who can operate at that intersection.

The Role

This is a hands-on software engineering role for someone who likes solving messy, important problems.

You will work directly with biotech and pharmaceutical teams to understand scientific workflows, data, systems, and constraints. You will then design and build software that improves those workflows.

Some problems will start with a clear specification. Many will not.

You may begin with a scientist describing a repetitive process, an Excel workbook that has become critical infrastructure, a collection of disconnected data sources, a legacy scientific application, or an idea for using AI that nobody has turned into a reliable system yet.

Your job is to figure out what to build and then help build it.

You will stay close to the people using the software, iterate quickly, and take solutions from initial discovery through production deployment.

This is a consulting role where the primary output is working software.

Job Function

• Embed with biotech and pharmaceutical teams to understand scientific, technical, and operational workflows.
• Translate ambiguous problems into clear technical requirements and practical implementation plans.
• Design, prototype, build, test, and deploy production software.
• Build scientific web applications, APIs, data pipelines, workflow automation, integrations, and internal tools.
• Connect systems across scientific data sources, databases, cloud infrastructure, vendor platforms, and existing enterprise applications.
• Apply AI and machine learning where they provide a real advantage, while recognizing when conventional software is the better solution.
• Work directly with scientists, computational researchers, informatics teams, software engineers, and business stakeholders.
• Investigate unfamiliar scientific domains and become productive quickly.
• Modernize or replace fragile spreadsheets, scripts, manual processes, and legacy applications.
• Design systems with appropriate attention to data provenance, reproducibility, security, auditability, and validation.
• Troubleshoot problems across application code, data, infrastructure, networking, and deployment environments.
• Document systems well enough that client teams can understand, operate, and extend them.
• Identify patterns across engagements that can become reusable LabACT tools, architectures, and engineering practices.

What We're Looking For

We care more about what you can build and how you approach problems than whether your background follows a specific path.

Strong candidates will have:

• Strong software engineering fundamentals and experience shipping production systems.
• Proficiency in Python and experience with modern application development.
• Experience working with relational databases and complex data.
• Practical cloud experience, particularly AWS, Azure, or GCP.
• Experience with containers, CI/CD, source control, automated testing, and production deployment.
• The ability to move comfortably between frontend, backend, data, infrastructure, and integration work as needed.
• Strong debugging skills and the persistence to work through unfamiliar systems.
• The ability to communicate clearly with both highly technical and non-technical people.
• Comfort working directly with clients and asking enough questions to understand the problem behind the request.
• Good technical judgment. You know when to build something sophisticated and when a simple solution is better.

Scientific Experience

Experience in biotech, pharmaceutical R&D, scientific informatics, computational biology, chemistry, clinical development, translational research, or another scientific software environment is highly valuable.

You should be comfortable sitting with a scientist, learning how their work is done, understanding enough of the science and data to ask good questions, and translating that knowledge into software.

AI

AI is increasingly part of the systems we build, but this is far from an AI-only engineering role.

You may work with LLMs, agents, retrieval systems, scientific models, evaluation frameworks, and AI-assisted workflows. You may also solve a problem with Python, PostgreSQL, React, AWS, and a well-designed API.

We care about choosing the right technology for each solution.

What Makes This Role Different

A traditional software engineer is often given a defined problem and builds against a product roadmap.

A consultant may analyze a problem and recommend what to do.

A Forward Deployed Scientific Software Engineer does both discovery and execution.

You might spend the morning working with scientists to understand why an existing workflow takes four hours, the afternoon tracing data through three systems, and the next several days building and deploying the application that reduces that workflow to minutes.

You are expected to understand the problem, make technical decisions, write the code, and help get the solution into people's hands.

You May Be a Great Fit If

• You enjoy walking into situations where the answer is not obvious.
• You like talking to users as much as you like building solutions.
• You can work independently without disappearing into a technical silo.
• You are comfortable saying, "I do not understand that yet," and then learning.
• You care about shipping useful systems that people use to improve their work.
• You like the idea that one day you may be learning a new scientific workflow, and a short time later scientists are using software you built to do their work differently.