ML Research Engineer, Foundation Models – Senior / Staff / Principal

Role Overview

• Drive the R&D and scaling of our foundation models, taking ownership of the engineering and experimentation for key research initiatives.
• Make cutting-edge foundation model research a reality at scale. Implement, optimize, and build novel foundation models from the initial research prototypes to high-performance production models. You will constantly engage with deep learning literature, building upon novel architectures and training methods to create new capabilities.
• Own the experimental lifecycle with scientific rigor. You'll design experimental plans, own their execution on our large-scale compute infrastructure, and drive the deep analysis of results to inform the next research cycle and to validate most promising approaches.
• Engineer our models for state-of-the-art performance, optimizing the scalability and efficiency of every part of the training and inference pipeline.
• Ship state-of-the-art models to production, working closely with the broader team to integrate your models into our drug discovery platform.
• Collaborate intensely with a multidisciplinary team to forge a tight, fast-moving loop between idea, implementation, and discovery.
• Contribute to the global research community by publishing some of your work and representing Genesis at top tier AI/ML conferences and workshops.
• Mentor and guide other researchers and engineers, fostering a culture of high-quality code, rigorous experimentation, and continuous innovation.

Requirements

• An exceptional research engineer with deep expertise in building scalable, high-performance foundation models, pretraining, and posttraining methods, and systems around them.
• A master of the modern ML engineering stack, striving for technical excellence with a passion for writing clean, high-performance, and reusable code (Python, PyTorch, etc.).
• An experienced practitioner of ML at scale, with a strong background in distributed training and data parallelism.
• Thrive in the ambiguity of deep learning research, comfortable designing and iterating on novel model architectures and training algorithms.
• An independent, first-principles thinker for both research and engineering problems, who takes pride in your projects and strives to build robust impactful models and systems from first-principles-based conceptualization to state-of-the-art realization.
• A curious, problem-oriented mind, excited to dive into the emerging field at the intersection of AI, physics, chemistry, and biology and make foundational contributions and discoveries. No prior experience in biology or chemistry is necessary – only willingness to learn.
• A true team player with strong communication skills who thrives in highly collaborative, mission-driven environments where science and engineering are deeply intertwined.
• Nice to haves: A MS or PhD in machine learning, computer science, other computational sciences or equivalent research or engineering experience (3+ years) demonstrated by a track record of building complex ML systems.
• A publication record in top-tier ML venues (NeurIPS, ICML, ICLR, etc.).
• Hands-on experience with our core libraries: PyTorch, PyTorch Lightning, and Ray Distributed Training, PyTorch Geometric, etc.
• Experience with novel research in one or more of the following domains: LLMs, diffusion, reinforcement learning or other cutting edge generative or predictive machine learning models.
• Familiarity with molecular data (proteins, small molecules), physics-informed ML, or 3D point cloud data.

Tech Stack

• Cloud
• Python
• PyTorch
• Ray

Benefits

• Comprehensive health benefits: Medical, Dental, and Vision (covered 100% for the employees).
• 401(k) plan.
• Open (unlimited) PTO policy.
• Free lunches and dinners at our offices.
• Paid family leave (maternity and paternity).
• Life and long
• and short-term disability insurance.