Principal Scientist, Machine Learning (Protein Design)

Company Overview: Ampersand Biomedicines is an innovative, privately held biotechnology company leading the way in developing multi-specific medicines that direct a wide range of therapeutic molecules to specific cells and biological contexts (www.Ampersand.bio).  Ampersand was founded in Flagship Pioneering’s venture creation engine, where companies such as Moderna Therapeutics (NASDAQ: MRNA) and Seres Therapeutics (NASDAQ: MCRB) were conceived and created.  With a strong emphasis on innovation, Ampersand cultivates a highly dynamic and entrepreneurial environment.

Position Summary: We are seeking a creative and motivated scientist with expertise in machine learning-driven antibody binder design to join Ampersand’s Computational Science team. This role demands a strong foundation in machine learning/deep learning and sequence/structure-based protein design.

As a Principal Scientist Machine Learning, you will be responsible for developing the frameworks and core models that propel our antibody design process forward. This is an opportunity to be part of a cutting-edge biotech company at the forefront of computational antibody design. You will play a pivotal role in shaping our AND-Body platform, working closely with expert biologists, protein scientists, and computational biologists to push the boundaries of antibody discovery.  To facilitate interactions with other team members and to make the most significant impact on our scientific endeavors, this position requires full-time, on-site presence.

Responsibilities:

• Design and conduct independent machine learning research to promote antibody binder design.
• Deliver robust, production-quality models that will drive the next generation of antibody design, advancing both engineering and de novo
• Collaborate closely with experimental teams to align with their capabilities, thereby enabling the creation of advanced hybrid platforms.
• Communicate insights and conclusions to colleagues in the scientific and leadership teams.
• Manage members of the Machine Learning team to drive advancements in our AND-Body platform.
• Develop a high-performing and ambitious team by providing mentorship, growth opportunities, and clear career development pathways for direct reports.
• Foster a culture of collaboration and accountability through strong cross-functional communication, ensuring alignment with key stakeholders and driving business success.
• Contribute to building a positive, team-oriented biotech culture.

Qualifications:

• A PhD in biochemistry, biophysics, computational chemistry, computational biology, computer science, machine learning, or a related discipline solving biological or chemical problems using computational approaches.
• Strong background and demonstrated experience in machine learning/deep learning, modeling, simulation, and design, particularly in sequence and structure-based antibody design.
• Demonstrated experience with the state-of-the-art suite of tools for protein design and machine learning approaches (AlphaFold, ESM, ProteinMPNN, RF-Diffusion, Rosetta, etc)
• Demonstrated experience in the Python programming language in addition to standard machine learning tools (PyTorch, TensorFlow, JAX, PyG, PyMC, etc).
• Experience with developing novel AI, structure, or sequence-based methods for biomolecular design.

Preferred:

• 5+ years of industry experience that includes significant work in machine learning, especially in antibody or protein binder design.
• Strong communication skills, with the ability to translate complex computational findings into actionable insights within a collaborative group setting.
• Experience working in an interdisciplinary environment, effectively collaborating with bench scientists, with the ability to think independently and contribute to a dynamic intellectual environment.
• Expertise with common software development tools and best practices: Git, AWS or other cloud experience, testing frameworks, etc.
• Proven track record of successfully managing protein design relevant machine learning projects with multiple team members.
• Experience with workflow tools such as Dask, Prefect, MLFlow, etc.
• Ability to multi-task and prioritize in a fast-paced start-up setting.

About Flagship

Flagship Pioneering is a bioplatform innovation company 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 companies 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 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.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.