at Lila Sciences · 101-250 employees
Lila Sciences is a startup building an AI platform that trains models on scientific literature and integrates laboratory validation to enable autonomous scientific discovery.
<p><strong>Your Impact at LILA</strong></p> <p>Lila Sciences is seeking a Research Engineer, Scientific Computing and ML/Physics Infrastructure to help turn promising research tools into robust, scalable systems. This role bridges research and production: you will work with scientists and ML researchers who can prototype useful tools, then help make those tools efficient, distributed, fault tolerant, and usable across Lila's compute environments.</p> <p>The Molecular Intelligence team is building ML and physics-based infrastructure for drug discovery, including biophysics workflows, computational chemistry tools, cofolding models, low-data learning systems, simulation workflows, and agent-usable scientific pipelines. We need an engineer who can improve code quality, architecture, GPU efficiency, cluster portability, and operational reliability without slowing down research velocity.</p> <p><strong>What You'll Be Building</strong></p> <ul> <li>Take research tools, prototypes, and scientific workflows developed by scientists or academic-style researchers and make them scalable, efficient, and maintainable.</li> <li>Collaborate directly with computational biophysics, computational chemistry, and machine learning scientists to turn research workflows into scalable agent-usable systems.</li> <li>Build and support ML and physics infrastructure for model training, molecular simulation, data processing, and agent-executed scientific workflows.</li> <li>Ensure workflows run reliably across multiple clusters and compute environments.</li> <li>Improve GPU utilization, distributed execution, throughput, fault tolerance, and reproducibility for ML and scientific workloads.</li> <li>Architect larger-scale systems around research code, including job orchestration, retry behavior, monitoring, artifact handling, and workflow traceability.</li> <li>Optimize ML, physics, and pipeline code for performance and scalability.</li> <li>Maintain development and execution environments across local, cloud, and GPU-based systems.</li> <li>Package scientific tools into reusable services, workflows, or APIs that can be used by researchers, pipelines, and AI agents.</li> <li>Partner with research, platform, and infrastructure teams to bridge exploratory scientific work with reliable engineering systems.</li> <li>Document systems clearly and establish pragmatic engineering patterns for research teams.</li> </ul> <p><strong>What You'll Need to Succeed</strong></p> <ul> <li>Strong software engineering skills in Python and experience working with ML, scientific computing, or simulation codebases.</li> <li>Experience building, scaling, or operating distributed systems for research, ML, physics, simulation, or data-intensive workloads.</li> <li>Practical knowledge of GPU computing, performance profiling, distributed execution, and failure modes in large-scale workloads.</li> <li>Experience with PyTorch, JAX, CUDA-aware workflows, or related ML/scientific computing frameworks.</li> <li>Practical knowledge of Linux, Docker or containers, dependency management, and reproducible development environments.</li> <li>Experience with orchestration, scheduling, or distributed execution systems such as Kubernetes, Slurm, Ray, Flyte, Argo, or similar tools.</li> <li>Ability to take prototype-quality research code and improve its architecture, scalability, reliability, and maintainability.</li> <li>Strong debugging skills across code, environments, infrastructure, data pipelines, and compute clusters.</li> <li>Ability to work directly with researchers, understand ambiguous technical needs, and convert them into robust engineering solutions.</li> </ul> <p><strong>Bonus Points For</strong></p> <ul> <li>Familiarity with chemistry, computational biophysics, molecular simulation, computational chemistry, cheminformatics, or drug discovery workflows.</li> <li>Experience with cloud GPU infrastructure, multi-cluster execution, or hybrid compute environments.</li> <li>Experience building tools for LLM agents or automated research workflows.</li> <li>Experience with workflow observability, checkpointing, retries, and fault-tolerant scientific workloads.</li> <li>Experience with CI, testing, packaging, and release practices for research software.</li> <li>Comfort supporting fast-moving research teams without over-engineering exploratory work.</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><strong>Compensation</strong></p> <p>We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.</p> <p><strong>U.S. Benefits.</strong> Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.</p> <p><strong>International Benefits.</strong> Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.</p></div><div class="title">Expected Base Salary Range</div><div class="pay-range"><span>$224,000</span><span class="divider">—</span><span>$294,000 USD</span></div></div></div><div class="content-conclusion"><p><strong>About LILA</strong></p> <p>Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.</p> <p>LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes