ML Research Engineer, Foundation Models - San Mateo - Genesis Therapeutics

    Genesis Therapeutics
    Genesis Therapeutics San Mateo

    1 week ago

    Description
    About the Team
    Join a world-class team at the forefront of AI and biochemistry.
    At Genesis Molecular AI, we're a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that will unlock groundbreaking therapies for patients with severe diseases.
    We don't just apply machine learning to biology; we are conducting fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field. The Genesis AI team is building an engine for this revolution. You will work side by side with the top multidisciplinary researchers to design and build generative foundation models at scale from the entire spectrum of molecular data, having access to ample compute and large-scale simulations.
    About the Role
    This role is for a highly-skilled ML Research Engineer who thrives at the intersection of fundamental research and production-grade engineering. As a core member of our Genesis AI team, you will be the engineering pillar for inventing and shipping our next-generation foundation models. You will partner directly with research scientists to design, build, and scale the systems for our most ambitious projects. You'll have the opportunity to tackle challenges such as scaling model pretraining, advancing reinforcement learning methods, or optimizing post-training pipelines. Your mission is to translate cutting-edge concepts into powerful and robust models that form the backbone of our drug discovery platform.
    Positions are available at various levels of seniority: Senior, Staff, and Principal.
    You will
    • 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.
    You are
    • 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.
    • Inspired by our culture of intellectual curiosity and the shared belief that breakthroughs happen when diverse perspectives and minds unite.
    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.
    What we offer
    • Competitive compensation package that includes salary and equity.
    • 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.
    About Genesis Molecular AI
    Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. The company's generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis has raised over $300 million from leading AI, tech and life science-focused investors, signed multiple AI-focused research collaborations with major pharma partners, and is deploying GEMS to advance an internal therapeutics pipeline for a variety of high-impact targets.

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