Member of the Technical Staff - AI/ML Engineer

Transfyr is building physical AI systems for science that capture and interpret real-world scientific execution. The AI/ML Engineer will build end-to-end machine learning systems using messy, multimodal laboratory data, integrate models with perception and software systems, and develop reliable insights, feedback, and automation for scientists and robots.

What you'll do:

• **Learn from Messy Reality:** Build ML systems that learn from real-world scientific execution data where feedback is delayed, labels are incomplete, and outcomes are confounded by how work was actually done
• **Fuse the World:** Collaborate with perception engineers to design multimodal learning pipelines that combine vision, audio, sensor data, metadata, and outcomes into coherent representations of scientific workflows
• **Provide Explainability:** Develop models that can reason about *why* an experiment succeeded or failed when intent, execution, environment, and outcome are tightly entangled
• **Know When the Model Is Unsure:** Build systems that surface model confidence / uncertainty, enabling scientists to understand when to trust a recommendation and when to intervene
• **Close the Loop:** Integrate models into real workflows where outputs influence both human decisions and robotic actions, and model behavior must remain robust as protocols, operators, and environments change
• **Generalize, Don’t Memorize:** Ensure models learn transferable structure rather than lab- or site-specific artifacts, enabling insights to carry across experiments, teams, and geographies
• **Lay the Groundwork for Automation:** Enable future physical AI systems by ensuring models learn from execution-level data, not just outcomes, building foundations for automation that can work in the real world

What they're looking for:

• This role is in-person in Cambridge, MA (other locations may open in the future, feel free to reach out even if Boston is not currently an option for you)
• **High agency.** You don't wait for perfect datasets or well-posed problems. You identify what needs to be learned, build the right scaffolding, and push work forward
• **Biased toward action.** You prototype quickly, test assumptions against real data, and iterate based on failure rather than waiting for theoretical certainty
• **Successful in ambiguity.** You can make progress when labels are incomplete, feedback is delayed, and success criteria evolve over time
• **Thoughtful.** You understand when sophistication helps and when it obscures, and you make deliberate tradeoffs between model complexity, robustness, and operational cost
• **Clear, direct communicator.** You can explain model performance and limitations to collaborators across engineering, science, and operations
• **Intense.** You care deeply about the mission, work hard when it matters, and help keep the team oriented toward what actually moves the needle
• **Great programmer:** Strong programming expertise with experience in software engineering, data systems, and AI/ML product development
• **ML Fundamentals:** Strong grounding in machine learning, with experience building models that learn from noisy, real-world data rather than clean, static datasets
• **Multimodal Learning:** Experience working with or reasoning about multimodal systems (e.g., vision, audio, sensor data, metadata, text), and an intuition for how different signals complement or confound each other
• **Python & Frameworks:** Fluency in Python and modern ML frameworks (e.g., PyTorch), with experience training, evaluating, and iterating on models in real systems
• **Data & Pipelines:** Experience designing data pipelines and training/evaluation infrastructure that evolve over time as new data arrives and assumptions change

Benefits:

• Equity
• Low/no-cost health insurance options
• HSA
• 401K with matching
• Lunch subsidy
• In-person work arrangement in Cambridge, MA