AI​/ML Engineer; Member Technical Staff

Overview

AI/ML Engineer (Member of the Technical Staff) – Transfyr Bio

Location:

Cambridge, MA, USA

Posted on Jan 26, 2026

• 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.
• Solve Credit Assignment:
  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.

• 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 (vision, audio, sensor data, metadata, text).
• 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.
• Production Awareness:
  Understanding of what it takes to move from prototype to production, including monitoring, iteration, and maintaining models as environments drift.
• Systems Mindset:
  Ability to work across the stack in close collaboration with software and perception engineers, understanding that model performance depends on the surrounding system.
• Learning Velocity:
  Strong fundamentals, curiosity, and the ability to quickly learn new tools, models, or domains as the problem demands.

• A passion for and experience in science
• A passion for and experience with AI
• Demonstrated experience working in fast-moving/ambiguous environments (like startups!)

• Competitive compensation (cash + equity)
• Full benefits (low/no-cost health insurance options, HSA, 401K with matching, lunch subsidy, etc.)

How to apply:

Apply now

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