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About the Role
As a Machine Learning Engineer at LILA, you will be the bridge between research and production, transforming models and agentic workflows into reliable, scalable systems. Your role is crucial in advancing LILA's mission to build Scientific Superintelligence and solve significant global challenges.
Responsibilities
- Own models and agentic workflows end to end, from research code to a deployed, monitored service.
- Build the integration layer that lets AI agents reliably call models, tools, simulations, and data services in production.
- Ensure models train correctly and that models and agents run efficiently, including profiling memory and compute, catching issues early, and keeping resource usage in check as workloads grow.
- Design and maintain production AI/ML pipelines (data ingestion, training, evaluation, deployment, monitoring), with testing, observability, CI/CD, and automated evaluations built in.
- Partner with domain scientists, ML researchers, and platform engineers to shape the architecture of our AI/ML platform, coding standards, and engineering best practices.
Requirements
- BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience.
- Strong Python software engineering fundamentals (testing, packaging, typing).
- Experience with machine learning frameworks (e.g., PyTorch, Hugging Face).
- Experience deploying AI/ML services in cloud-based infrastructure (e.g., FastAPI, containers, orchestration, databases).
- Hands-on experience bringing models to production (e.g., deep learning models, LLMs, multimodal models, or RAG).
- Strong debugging, profiling, and optimization skills.
- Clear communication and collaboration in cross-functional settings.
Skills
- Python
- PyTorch
- Hugging Face
- FastAPI
- Containers
- Orchestration
- Databases
- Deep learning models
- LLMs
- Multimodal models
- RAG
- Debugging
- Profiling
- Optimization
- Open-source ML
- Scientific software
- AI agents
- Tool calling
- Multi-step workflows
- Workflow orchestration
- Data provenance
- Large-scale compute environments
- Materials science
- Chemistry
- Physics
- Mixed precision
- Distributed training
- GPU profiling
- Efficient model serving
Location
- U.S.
Work Type
- Full-time
Experience Level
- Industry experience
- Experience deploying AI/ML services
- Hands-on experience bringing models to production
Education Level
- BS/MS/PhD in Computer Science, Engineering, or a related quantitative field
- Equivalent industry experience
Salary/Compensations
- $180,000—$298,000 USD
Benefits
- Competitive base compensation
- Bonus potential
- Generous early-stage equity
- Medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with generous company wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits
- Company subsidized lunch program
About the Company
- 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.
- LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy.
- Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance.
Equal Opportunity
- Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.