Build Models Where None Exist Yet.

We are forming a machine learning team inside a newly launched venture, Flagship Labs 120. Our work focuses on extracting latent structure from information-rich measurements of complex physical systems-often requiring mechanism-informed modeling, thoughtful inductive bias design, and principled approaches to inverse problems.

This is a zero-to-one role focused on modeling innovation rather than routine optimization. You'll design, prototype, test, and refine new approaches that help define the technical foundation of a platform from day one.

What You'll Do

• Develop and iterate on ML models for complex measurement data, from representation design through validation
• Design objectives and architectures that respect known constraints, symmetries, or latent structure in the data
• Explore and compare modeling strategies, balancing strong baselines with more experimental approaches when appropriate
• Investigate model behavior and failure modes to improve robustness and interpretability
• Collaborate closely with experimental and technical teammates to align modeling with data generation
• Contribute to shaping the long-term ML strategy and technical direction of a new venture

Who You Are

You may come from physics, applied mathematics, engineering, computer science, or another quantitative field. You have hands-on experience developing machine learning models-ideally in deep learning, representation learning, probabilistic modeling, or related areas. You are comfortable implementing and modifying models, training them end-to-end, and working directly with real data.

You likely:

• Think algorithmically and reason from underlying structure
• Are comfortable adapting or extending model architectures when needed
• Have built and debugged meaningful ML systems or research prototypes
• Enjoy operating in dynamic, early-stage environments
• Read papers, build prototypes to test ideas, and translate concepts into working systems

What matters most is your ability to reason across data, models, and the systems they represent.

Technical Background

Required

• Strong hands-on experience building and training modern ML models
• Fluency in Python and at least one major ML framework (e.g., PyTorch or equivalent)
• Experience working with real-world or experimentally generated data
• Ability to design, run, and interpret ML experiments
• Comfort working in practical development environments (e.g., cloud infrastructure, experiment tracking, reproducible workflows)

Helpful

• Experience with inverse problems, latent-variable inference, or structured generative modeling (e.g., diffusion or flow-based methods)
• Familiarity with geometric or symmetry-aware architectures
• Experience incorporating physical or structural constraints into learning systems
• Experience working with time-series or high-dimensional signal data
• Exposure to biology, chemistry, physics, or related sciences

The salary range for this role is $130,000 - $230,000. Compensation for the role will depend on a number of factors, including a candidate's qualifications, skills, competencies, and experience. FL120 currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on FL120's good faith estimate as of the date of publication and may be modified in the future.