Applied Scientist

One of our partner companies is not just building AI—they’re redefining what’s possible at the nexus of physics, programming, and machine learning. As an Applied Scientist on their team, you’ll tackle real-world scientific and coding challenges—developing novel reinforcement-learning algorithms and physics-informed models that accelerate breakthroughs in logic, coding, and semiconductor design. What you'll do Prototype and validate new algorithms. Design, implement, and empirically evaluate novel RL, supervised, and unsupervised methods—especially those that leverage physics priors or logical constraints. Bridge research and production. Collaborate closely with ML Engineers and infrastructure teams to integrate experimental models into scalable pipelines, ensuring reproducibility, observability, and sub-second performance. Build robust experimentation platforms. Develop and maintain data-processing pipelines, training orchestration, and evaluation frameworks to accelerate iteration on large-scale experiments. Analyze and iterate. Dive deep into large datasets—experimental logs, simulation outputs, and user interaction traces—to diagnose model behavior, surface failure modes, and drive evidence-based improvements. Publish and present. Document findings in technical reports, contribute to open-source projects, and present work internally (and externally, when appropriate) to help shape the broader AI research community. What You Bring PhD or Master’s in Computer Science, Applied Mathematics, Physics, Electrical Engineering, or a closely related field, with a strong focus on machine learning, reinforcement learning, or scientific computing. Hands-on experience developing and evaluating ML/RL algorithms—ideally demonstrated via publications, open-source contributions, or applied research projects. Proficiency in Python and deep-learning frameworks (PyTorch or TensorFlow), with solid coding practices for collaborative research. Excellent communication skills: able to distill complex technical ideas into clear narratives for both technical and non-technical audiences. Bonus Points Prior work on physics-informed neural networks, scientific simulations, or logic-based learning. Demonstrated expertise in reinforcement learning for combinatorial optimization, theorem proving, or code generation. Background in semiconductor design flows, EDA tools, or hardware-in-the-loop experimentation. What it's like here We’re a rapidly growing, VC-backed startup where “raw talent and collaborative spirit over ego” isn’t just a motto—it’s how we work every day. You’ll be surrounded by teammates who have built companies from zero to IPO, led teams at Google and Amazon, and earned medals in international science Olympiads. We value relentless curiosity, humble problem-solving, and a bias toward action. Logistics Redwood Shores, CA (U.S.) and Toronto, ON (Canada) Schedule: four days in our beautiful office, one day remote Visa Sponsorship: We welcome H-1B transfers and other employment-based visas

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