ProFound Therapeutics: AI/ML Engineering - SAGE Platform Co-Op

Cambridge, MA USA

Actively hiring, first seen by us 11 days ago

Pay: $25–35/hour — pay source
Flagship Pioneering is 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. The pay range for this role is $25 - $35 per hour. This role is a short-term engagement that is not eligible for health and welfare benefits. Retirement benefits may be available after completing a set number of hours. Compensation and benefits information is based on ProFound's good faith estimate as of the date of publication and may be modified in the future. Privacy Notice for Applicants: When you apply for a role at Flagship Pioneering, we collect and use personal information you provide (such as your name, contact details, work history, and application materials) to evaluate your application, communicate with you, and comply with legal obligations. Your application data is processed through Greenhouse, our applicant tracking system, and may also be reviewed using AI-assisted screening tools. We do not sell your personal information. California residents have rights under the CCPA/CPRA including to know, delete, and opt out of the sharing of their personal information. If you are located in the EU or UK, we process your data under GDPR and you have rights to access, rectify, and erase your data. To exercise your rights or for questions, contact privacy@flagshippioneering.com.
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Before you apply

New graduates: For current studentsJob title: ProFound Therapeutics: AI/ML Engineering - SAGE Platform Co-Op

What you’ll work on

Full posting

ProFound’s AI/ML team builds SAGE, an internal multi-agent LLM platform that helps our scientists reason over knowledge graphs, biological databases and the scientific literature.

As an AI/ML Engineering Co-Op, you will help extend what SAGE can do and make sure it performs reliably.

Design, build and ship new SAGE tools connecting the agent to external data sources and MCP servers
Extend SAGE to ingest and reason over user-uploaded documents, including parsing, chunking and retrieval
Build features for the SAGE chat interface, such as sharing conversations and displaying sources
Develop evaluation pipelines measuring SAGE's answer quality, accuracy and hallucination rate
Build monitoring for SAGE usage, performance, reliability and LLM cost

From the employer’s posting

ProFound’s AI/ML team builds SAGE, an internal multi-agent LLM platform that helps our scientists reason over knowledge graphs, biological databases and the scientific literature. SAGE is a central part of how ProFound applies agentic AI to understand novel proteins and accelerate drug discovery.

As an AI/ML Engineering Co-Op, you will help extend what SAGE can do and make sure it performs reliably. You will own real features from design through deployment, working closely with our ML engineers and with the scientists who use SAGE every day. The Co-Op is designed to complement your academic training with hands-on experience building and operating a production AI system in an industry drug discovery setting.

Design, build and ship new SAGE tools that connect the agent to external data sources, APIs and MCP servers.

Extend SAGE’s ability to ingest and reason over documents that users upload, including parsing, chunking and retrieval.

Build features that make the SAGE chat interface easier to use and collaborate in (e.g., sharing conversations, displaying sources and citations).

Develop evaluation and benchmarking pipelines that measure SAGE’s answer quality, accuracy and hallucination rate.

Build monitoring for SAGE usage, performance, reliability and LLM cost.

What you’ll bring

All qualifications

Core experience

Hands-on experience building with LLMs (e.g., LLM APIs, RAG, agents, or MCP) through coursework, projects, or prior internships
Experience with web or backend development (e.g., FastAPI, React or TypeScript).
Familiarity with Git and software engineering best practices, such as testing, code review and documentation.
Experience with LLM evaluation frameworks or observability and tracing tools.
Familiarity with cloud environments (e.g., AWS), containers (e.g., Docker) and CI/CD tooling (e.g., GitHub Actions).

Qualification wording

Hands-on experience building with LLMs (e.g., LLM APIs, RAG, agents, or MCP) through coursework, projects, or prior internships

Experience with web or backend development (e.g., FastAPI, React or TypeScript).

Familiarity with Git and software engineering best practices, such as testing, code review and documentation.

Experience with LLM evaluation frameworks or observability and tracing tools.

Familiarity with cloud environments (e.g., AWS), containers (e.g., Docker) and CI/CD tooling (e.g., GitHub Actions).

Benefits in the posting

Full benefits wording

The pay range for this role is $25 - $35 per hour. This role is a short-term engagement that is not eligible for health and welfare benefits. Retirement benefits may be available after completing a set number of hours. Compensation and benefits information is based on ProFound's good faith estimate as of the date of publication and may be modified in the future.

From the employer’s posting.

About Flagship Pioneering Co-Op Program

You'll join fellow peers across our ecosystem of companies and learning firsthand how we build first-in-category companies.

In the employer’s words · Read in context

Apply at Flagship Pioneering Co-Op Program

Posting record

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Actively hiringFirst seen by us 11 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Oct 24, 202611 days ago
Recorded sightings: 13
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 11

Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Flagship Pioneering Co-Op Program is hiring

3 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
TypeScript
AWS
Docker
FastAPI
React

Where the posting names them

- Currently pursuing a B.S. or M.S. in Computer Science, Artificial Intelligence, Data Science, or a related field, and available for a full-time, 4–6 month co-op term. - Strong Python skills. - Hands-on experience building with LLMs (e.g., LLM APIs, RAG, agents, or MCP) through coursework, projects, or prior internships

Preferred - Experience with web or backend development (e.g., FastAPI, React or TypeScript). - Experience with LLM evaluation frameworks or observability and tracing tools.

- Exposure to knowledge graphs, vector databases, or scientific and biological data sources. - Familiarity with cloud environments (e.g., AWS), containers (e.g., Docker) and CI/CD tooling (e.g., GitHub Actions). - Prior internship or co-op experience in software or ML engineering.

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

This position is open exclusively to current Northeastern University Co-Op students.

Location: Watertown, MA
Work Period: January 2027 to June 2027. This is a full - time, 40 hours per week position with hybrid schedule.

About ProFound Therapeutics

ProFound™ Therapeutics is a biotechnology company discovering novel proteins across the expanded human proteome to unlock first-in-class medicines and transformative drug targets. The company’s proprietary ProFoundry™ Platform integrates state-of-the-art protein detection, validation, and computational technologies to systematically identify and characterize previously inaccessible proteins and validate their therapeutic potential. This approach has generated a rapidly expanding portfolio of novel targets and drug discovery programs advancing toward the clinic, enabled by strategic collaborations with leading pharmaceutical partners. Together, these efforts support the development of breakthrough treatments across a broad range of disease areas.

The company is now poised to define the disease-relevant function of these proteins at scale using advanced proteomics technologies, high throughput experimental biology, and causal agentic AI, enabling rapid identification and characterization of the most promising protein drugs and drug targets.

Founded by Flagship Pioneering, ProFound works with pharmaceutical partners including Novartis, Pfizer and GSK, and with the Gates Foundation, which has committed up to $35 million to a ProFoundprogram focused on preeclampsia.

About Flagship Pioneering

ProFound Therapeutics was founded by Flagship Pioneering's venture creation engine that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the company’s Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture. Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

Flagship Pioneering Co-Op Program

Welcome to Flagship Pioneering Co-Op Program for the cohort dreaming big and taking courageous leaps. You'll join fellow peers across our ecosystem of companies and learning firsthand how we build first-in-category companies. You’ll have access to enrichment sessions, a dedicated professional development track, and a mentor that help guide you toward your next step. By the end, you will carry on a meaningful project outcome, a genuine network, and a front-row view of how Flagship pioneer new ventures that change the world.

Position Summary

ProFound’s AI/ML team builds SAGE, an internal multi-agent LLM platform that helps our scientists reason over knowledge graphs, biological databases and the scientific literature. SAGE is a central part of how ProFound applies agentic AI to understand novel proteins and accelerate drug discovery.

As an AI/ML Engineering Co-Op, you will help extend what SAGE can do and make sure it performs reliably. You will own real features from design through deployment, working closely with our ML engineers and with the scientists who use SAGE every day. The Co-Op is designed to complement your academic training with hands-on experience building and operating a production AI system in an industry drug discovery setting.

Scope

Agent tools and integrations, including external data sources, MCP servers and user-uploaded documents
User experience and collaboration features in the SAGE chat interface
LLM evaluation and benchmarking (answer quality, accuracy and hallucination rate)
Monitoring of usage, performance and cost, and quality checks in CI/CD

Key Responsibilities

Responsibilities include, but are not limited to, the following.

Tools and capabilities
- Design, build and ship new SAGE tools that connect the agent to external data sources, APIs and MCP servers.
- Extend SAGE’s ability to ingest and reason over documents that users upload, including parsing, chunking and retrieval.
- Contribute to agent orchestration and retrieval-augmented generation (RAG) components that work across knowledge graphs, biological databases and the scientific literature.
User experience
- Build features that make the SAGE chat interface easier to use and collaborate in (e.g., sharing conversations, displaying sources and citations).
- Gather feedback directly from scientists and turn it into practical, prioritized improvements.
Evaluation and quality
- Develop evaluation and benchmarking pipelines that measure SAGE’s answer quality, accuracy and hallucination rate.
- Help build and curate benchmark datasets with input from scientists, using automated (e.g., LLM-as-judge) and human-review approaches.
- Help integrate automated quality checks and regression tests into our CI/CD pipeline.
Monitoring and engineering practice
- Build monitoring for SAGE usage, performance, reliability and LLM cost.
- Write clean, tested and documented code, and take part in code reviews.
- Present progress and results to the AI/ML team and scientific stakeholders, including a final co-op presentation.

What Success Looks Like by the End of the Co-Op

At least one new SAGE capability designed, built and deployed to production, and in use by scientists.
An evaluation and benchmarking pipeline running regularly, giving the team a clear baseline for answer quality and hallucination rate.
Better visibility into SAGE usage, performance and cost.
Well-documented work that the team can maintain and build on after the co-op ends.

Qualifications

Required

Currently pursuing a B.S. or M.S. in Computer Science, Artificial Intelligence, Data Science, or a related field, and available for a full-time, 4–6 month co-op term.
Strong Python skills.
Hands-on experience building with LLMs (e.g., LLM APIs, RAG, agents, or MCP) through coursework, projects, or prior internships
Familiarity with Git and software engineering best practices, such as testing, code review and documentation.
Curious and self-driven, with the ability to communicate clearly with technical and non-technical colleagues.

Preferred

Experience with web or backend development (e.g., FastAPI, React or TypeScript).
Experience with LLM evaluation frameworks or observability and tracing tools.
Exposure to knowledge graphs, vector databases, or scientific and biological data sources.
Familiarity with cloud environments (e.g., AWS), containers (e.g., Docker) and CI/CD tooling (e.g., GitHub Actions).
Prior internship or co-op experience in software or ML engineering.
Interest in biology or drug discovery; no biology background required.

Why Join Us?

You’ll work on a production AI platform used by real scientists. You’ll own projects end to end, with mentorship from experienced ML engineers, and contribute to the discovery of new medicines.

What We Offer

Build alongside founders. You’ll work inside the same hypothesis-driven process Flagship uses to conceive and launch first-in-category companies.
This can be the start of a longer story. Strong co-ops are considered for future opportunities across Flagship and its portfolio companies, not just for this one role.
Work alongside people who left the safe path. Your colleagues are scientists, engineers, and operators who chose to build something new rather than join something already established.
Take on real ownership. You're contributing to ventures that are still being defined. Your work can shape real decisions, not just support them.
Be paid like the contributor you are. Compensation reflects the real work you take on.

Flagship Pioneering is 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.

The pay range for this role is $25 - $35 per hour. This role is a short-term engagement that is not eligible for health and welfare benefits. Retirement benefits may be available after completing a set number of hours. Compensation and benefits information is based on ProFound's good faith estimate as of the date of publication and may be modified in the future.

Privacy Notice for Applicants: When you apply for a role at Flagship Pioneering, we collect and use personal information you provide (such as your name, contact details, work history, and application materials) to evaluate your application, communicate with you, and comply with legal obligations. Your application data is processed through Greenhouse, our applicant tracking system, and may also be reviewed using AI-assisted screening tools. We do not sell your personal information. California residents have rights under the CCPA/CPRA including to know, delete, and opt out of the sharing of their personal information. If you are located in the EU or UK, we process your data under GDPR and you have rights to access, rectify, and erase your data. To exercise your rights or for questions, contact privacy@flagshippioneering.com.

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