Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), software engineers build the systems that connect generative models, scientific data, and experimental workflows into reliable, production-grade pipelines powering Lila's protein design and engineering campaigns.

We're hiring a Staff ML Engineer, Life Sciences AI to lead software infrastructure development for our protein design and engineering pipelines. This is a senior IC role focused on the engineering systems that surround and support our ML stack — pipeline orchestration, data flow between computational and experimental systems, integration of new tools and methods, and the developer experience that lets LSAI move fast on commercial partnership deliverables.

What You'll Be Building

• Architect and build software infrastructure powering Lila's protein design and engineering pipelines: orchestration, data flow, APIs, and integration with experimental systems.
• Own the engineering side of LSAI's "Lab-in-the-Loop" lifecycle — connecting computational outputs to experimental inputs and feeding results back into design workflows.
• Onboard new tools and methods developed by AI scientists and ML engineers into production-ready systems used in commercial partnership campaigns.
• Partner cross-functionally with ML researchers, scientists, and platform engineers to translate research code into reliable, scalable systems.
• Set engineering standards for LSAI software — design reviews, CI/CD, testing, observability, reproducibility — and mentor senior engineers as the team grows.
• Diagnose and resolve reliability, performance, and scaling bottlenecks in production pipelines supporting partnership deliverables.

What You’ll Need to Succeed

• Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience).
• 8+ years of professional software engineering experience in Python (or comparable systems languages).
• Proven experience designing, building, and operating scalable production systems — APIs, data pipelines, orchestration, and cloud infrastructure.
• Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices.
• Experience building or operating scientific or ML-adjacent infrastructure — workflow orchestration, experiment tracking, and reproducible pipelines.
• Hands-on experience with containerization, orchestration platforms, and infrastructure-as-code on a major cloud provider.
• Track record of leading technical direction across multiple systems and partnering deeply with research scientists or ML engineers to translate scientific needs into production engineering.

Bonus Points For

• Experience building infrastructure for protein design and engineering, antibody engineering, or other molecular ML applications.
• Familiarity with biological data formats and bioinformatics tooling.
• Experience integrating ML training/inference systems with broader product or scientific platforms.
• Open-source contributions to scientific computing or data infrastructure projects.

Overview of the role:

As the Product Manager for Software / Applied AI at Lila, you will help build the first truly AI-native system of record for scientific discovery. We are looking for product leaders who can dive deep into a specific domain and own it completely.

Whether your strengths align best with our core software platform or our cutting-edge agentic science offerings, you will be embedded with a dedicated scrum team to solve complex, full-stack challenges. You will sit at the intersection of commercial, engineering, design, and scientific research, building the tools that accelerate the next generation of scientific discovery.

We are hiring for a wide range of function areas, and seniority levels — final title and level will be decided at the offer stage. Priority areas include 1) data, 2) ML / computational tooling, 3) scientific agents, and 4) harness & evals.

Location:

Ability to work from our San Francisco, CA; or Cambridge, MA office 3-5 days per week.

Responsibilities:

Deep Domain Ownership:

We will match your unique product strengths to a specific scrum team, where you will go deep, define the roadmap, and become the absolute subject matter expert for that product area. That said, we operate as one product team and collaborate closely.

Full-Stack Product Execution:

From designing user-facing scientific workflow tools to shaping the underlying data infrastructure that powers our AI models, you will define requirements and guide execution across the entire stack.

The intelligence platform for Science:

Build intuitive, powerful software that scientists actually want to use. You will be responsible for translating incredibly complex scientific workflows into scalable, elegant digital products.

Cross-Functional Orchestration:

Lead the dialogue between our research scientists (who push the boundaries of what's possible) and our engineering teams (who build robust, scalable systems), ensuring we ship high-impact, cohesive products.

Qualifications:

Core Product Experience:

Product Management experience building complex B2B SaaS, data-heavy platforms, or highly technical software products.

The Full-Stack Mindset:

You are comfortable navigating both the user experience of a complex workflow and the technical realities of the backend architecture required to support it.

Bias for Action in Complexity:

You thrive in ambiguous environments. You can take a vague, highly technical customer request and turn it into a clear, prioritized roadmap for your engineering team in days, not weeks.

Operational Rigor:

You care deeply about the "how" — how the product is built, how it performs, and how it directly impacts the end user's daily work.

Preferred Skills:

Scientific Background:

A degree or deep practical experience in the hard sciences (materials science, chemistry, biology, etc.).

Building for Scientists:

Direct experience building software, tools, or platforms specifically designed for scientific researchers or lab environments.

Startup DNA:

Experience as an early-stage product manager who knows how to move fast, iterate, and build from 0 to 1.

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI (LSAI), the AI for Protein Engineering team develops and uses the generative and predictive models that drive Lila's biomolecule design programs from in silico hypothesis to wet-lab validated lead.

We are seeking a Senior or Principal Scientist to join this team as a senior IC focused on antibody design and engineering. You will develop and execute the methods and workflows that ensure successful completion of antibody campaigns. Scope may expand to additional modalities such as enzymes and peptides as needs evolve.

This role sits at the bilingual edge of ML and biology. You will own biological understanding of campaign needs and partner closely with the Life Science Research team to design and validate computational predictions in the lab. You will shape the technical agenda for AI protein engineering at Lila and represent that work both internally and to the broader research community.

What You'll Be Building

• Develop and own protein design and engineering workflows for antibody campaigns, including de novo design, affinity maturation, and developability optimization
• Execute design workflows end-to-end for active campaigns and deliver wet-lab-validated leads against program milestones
• Translate campaign requirements — epitope selection, affinity targets, biophysical constraints, and developability criteria — into well-defined ML problems and design specifications
• Adapt and extend state-of-the-art AI methods (generative models, protein language models, structure-conditioned design) to the specific demands of antibody and broader biomolecule engineering
• Partner with the Life Science Research team on design validation, building active learning loops where wet-lab data refines and improves model performance
• Expand the protein engineering platform to additional modalities such as enzymes and peptides as needs evolve

What You’ll Need to Succeed

• PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, or a related quantitative field
• Proven track record of successful design of wet-lab-validated biomolecules through AI, with industry experience strongly preferred
• Deep ML expertise with the ability to modify and adapt state-of-the-art AI approaches for protein engineering, not just apply them off-the-shelf
• Strong fluency across both ML and protein biology, with hands-on understanding of antibody design
• Demonstrated ability to drive a research and engineering program independently, from problem definition through experimental validation and iteration
• Track record of close collaboration with experimental scientists and clear communication across the ML/biology boundary

Bonus Points For

• Direct experience designing antibodies, nanobodies, or other therapeutic proteins for clinical or therapeutic pipelines
• Experience with structure prediction, generative protein design (diffusion, flow-matching, or similar), and protein language models in a production research setting
• Experience in structural biology and conformational dynamics
• Experience extending design methods to additional modalities such as enzymes, peptides, or other engineered biomolecules
• High-impact publications or open-source contributions in AI for Science (NeurIPS, ICML, ICLR, Nature Methods, Nature Biotechnology, or equivalent)
• Experience designing or operating active learning loops between computational design and high-throughput experimental validation

Your Impact at LILA

Join us in shaping the future of science! We are seeking Sr Principal, and Principal Software Engineers with full stack experience to join our software org, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. Our teams span across App, Data, SSR, and LaS with a strong emphasis on Python development for scientific applications. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!

Our Software Teams

The Application Team (App)- They build the operating system for AI-driven science. Their platform unifies Machine Learning, Life Sciences, Physical Sciences, and Software into one AI-native experience — where a scientist can go from hypothesis to experiment to result in a single conversation. They ship the agents, the integrations, and the interfaces that make that possible.

The Scientific System of Record Team (SSR) - They build the memory layer for Lila's operations. It answers two questions:what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop.

The Lab as Software Team (LaS)- They act as the physical and virtual execution layer for Lila's AI-driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi-directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab.

The Data Platform Team (Data)- They build and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.

What You'll Be Building

• Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
• Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
• Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
• Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
• Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
• Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

What You'll Need To Succeed  

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 8-15 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend.
• Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)
• Hands on experience using AI coding assistants to drive productivity is required.
• Communication & Collaboration: Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
• Problem Solving: Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
• Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).
• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• Familiarity with Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
• Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
• Experience with laboratory devices, robotics, or hardware drivers.

Your Impact at LILA

Join us in shaping the future of science! We are seeking Staff, Senior II, and Senior I Software Engineers with either frontend or backend experience to join our software org, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. Our teams span across App, Data, SSR, and LaS with a strong emphasis on Python development for scientific applications. If you thrive in a collaborative, fast-paced environment and bring best practices in git, development workflows, and user-centered design, we want to hear from you!

Our Software Teams

The Application Team (App)- They build the operating system for AI-driven science. Their platform unifies Machine Learning, Life Sciences, Physical Sciences, and Software into one AI-native experience — where a scientist can go from hypothesis to experiment to result in a single conversation. They ship the agents, the integrations, and the interfaces that make that possible.

The Scientific System of Record Team (SSR) - They build the memory layer for Lila's operations. It answers two questions:what did we plan to build? and what actually happened? These systems connect scientific intent to physical reality. Together with the data and automation teams, their systems ensure reproducibility and close the Design-Build-Test-Learn (DBTL) loop.

The Lab as Software Team (LaS)- They act as the physical and virtual execution layer for Lila's AI-driven science. This connects the Lila App to AI Science Factories (AISFs), the mechanism through which experiments are dispatched to real instruments. This team owns the full stack of lab integration: orchestration of labflows, instrument integrations, bi-directional data transfer, and the UI/UX that AI scientists and operators use to interact with the lab.

The Data Platform Team (Data)- They build and support the data systems that underpins Lila's AI Science Factory™. Every experiment run in our labs, every measurement from an instrument, and every signal from our operational systems flows through the platform they build. Their work spans real-time ingestion, large-scale analytical storage, workflow orchestration, and the self-service tools scientists, engineers, and ML teams use to go from raw measurements to discoveries. They build the data backbone of Scientific Superintelligence™, so the science moves faster and each experiment makes the next one smarter.

What You'll Be Building

• Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications.
• Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability.
• Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability.
• Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads.
• Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale.
• Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.

What You’ll Need to Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 5-10 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend.
• Full Stack Development: Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic)
• Hands on experience using AI coding assistants to drive productivity is required.
• Communication & Collaboration: Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
• Problem Solving: Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability.

Bonus Points For

• Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
• Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django).
• Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster).
• Familiarity with Python for Science: Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch).
• Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields.
• Experience with laboratory devices, robotics, or hardware drivers.

Your Impact at LILA

We are seeking a Senior Software Engineer to join our Applied AI group and help build the next generation of our AI-driven scientific platform. In this role, you will design and optimize the backend systems, data pipelines, and AI integrations that power intelligent, data-driven applications. You’ll work at the intersection of backend engineering and machine learning, ensuring our platform seamlessly scales and supports cutting-edge applied AI techniques such as Retrieval-Augmented Generation (RAG), agentic AI, and large language model (LLM) integration.

This role is ideal for someone who thrives in bridging software engineering and applied AI—turning research into production-grade systems that drive real-world scientific discovery. If you are passionate about building performant, elegant systems that make AI useful and impactful, we would love to hear from you!

What You'll Be Building

• Applied AI Integration: Design and deploy backend services and data pipelines that directly support advanced AI applications, including LLMs, RAG, and agentic frameworks.
• API & Service Development: Build high-performance APIs and microservices that enable seamless integration between AI models, scientific tools, and user-facing applications.
• Data Pipeline Architecture: Architect and manage scalable pipelines capable of handling structured, unstructured, and vectorized data for AI/ML workloads.
• Database & Knowledge Systems: Implement and optimize SQL, NoSQL, and vector databases to support low-latency AI retrieval and inference workloads.
• Cloud & Infrastructure: Leverage AWS, Kubernetes, and infrastructure-as-code (Terraform/CloudFormation) to build robust, production-ready AI platforms.
• Performance & Reliability: Diagnose system bottlenecks, optimize for cost and speed, and ensure the reliability and fault-tolerance of AI-driven workflows.
• Collaboration: Partner with ML researchers, platform engineers, and scientists to translate models and algorithms into scalable, production-ready systems.

What You’ll Need to Succeed

• Educational Background: Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
• Backend & Data Expertise: 7+ years of professional experience building and scaling production systems, including APIs, data pipelines, and distributed services.
• Programming Skills: Strong Python skills (FastAPI, Flask, Django), with solid experience in backend service development.
• Databases: Proven experience with SQL, NoSQL, and vector databases; skilled in schema design, indexing, and query optimization.
• Applied AI Systems: Hands-on experience integrating ML models or AI-driven workflows into production services.
• Cloud & DevOps: Proficiency with AWS, Docker/Kubernetes, CI/CD pipelines, and infrastructure-as-code.
• Communication & Problem-Solving: Ability to work cross-functionally with diverse teams and explain complex technical concepts to non-experts.

Bonus Points For

• Scientific & Data-Intensive Domains: Experience working with life sciences, materials sciences, or other research-heavy fields.
• Startup Experience: Comfort with fast-paced, iterative environments where impact and adaptability matter.

Your Impact at LILA

As Commercial Counsel (Director/Associate Director) at Lila Sciences, you will be a foundational member of the legal team, responsible for shaping and scaling legal capabilities in a fast-moving, frontier technology environment. This is a dynamic, high-ownership role focused on structuring and negotiating a wide array of commercial agreements, while also advising on legal and regulatory considerations surrounding artificial intelligence, scientific research, and cutting-edge technology development. You'll partner cross-functionally across science, engineering, business development, and operations to enable Lila’s mission to accelerate discovery with AI.

We're looking for an experienced, entrepreneurial attorney with 6–10 years of legal experience—someone who is excited to work at the intersection of law, AI, and science. You should thrive in environments that demand both precision and creativity, be comfortable navigating ambiguity, and bring a strong sense of judgment and empathy to collaborative problem-solving. If you're passionate about building scalable legal infrastructure, engaging deeply with novel technologies, and evolving the practice of law using AI, this is the role for you.

What You'll Be Building

• Draft, negotiate, and manage a broad range of commercial agreements (e.g., SaaS, IP licensing, vendor, partnership, and R&D collaboration contracts)
• Provide pragmatic legal counsel to internal stakeholders across the business, product, and science functions, balancing legal risk with strategic objectives
• Serve as a thought partner on regulatory and ethical considerations related to the use of AI in scientific and commercial contexts
• Help build scalable systems and workflows to streamline legal processes and amplify team impact using AI tools and automation
• Support strategic transactions and partnerships, rapidly developing expertise in new domains to ensure sound legal outcomes

What You’ll Need to Succeed

• J.D. and membership in at least one U.S. state bar in good standing
• 6+ years of legal experience, with substantial time in-house at a technology company
• Deep experience drafting and negotiating complex commercial contracts
• Familiarity with AI, SaaS, and/or scientific R&D business models
• Ability to operate independently, manage multiple priorities, and communicate effectively across technical and non-technical teams

Bonus Points For

• Enthusiasm for transforming legal practice through AI and data-driven tools
• Prior work involving export-controlled technologies or CFIUS considerations
• Experience supporting R&D teams in life sciences, materials, or climate-tech domains
• Comfort with technical subject matter and scientific research environments
• Track record of building legal infrastructure or scalable processes from scratch

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team researches and develops large-scale generative models and reasoning frameworks that power automated scientific discovery across Lila's life science domains.

We are seeking a Principal or Senior Principal Scientist to join this team as a core contributor. You will define and drive research at the intersection of state-of-the-art machine learning and life science data, spanning biological sequences, molecular structures, and multimodal experimental data. As part of a dynamic team, you will design and implement foundational models end to end, from problem formulation and architecture through training at scale, evaluation, and integration into Lila's closed-loop discovery engine.

This is a high-impact IC role for someone who operates at the frontier of generative AI applied to biology. You will shape the technical agenda for foundation model research, collaborate closely with experimental scientists to close the computational-experimental loop, and represent Lila's work to the broader scientific community.

What You'll Be Building

• Drive research on foundation models for life science applications, including but not limited to biological sequence design, structure prediction, and multimodal scientific reasoning
• Design, train, and evaluate large-scale generative models on biological and chemical data, integrating domain-specific constraints and priors
• Contribute to the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: steer data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
• Translate complex biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists and computational biologists
• Advance research standards and methodology within the foundation models program, contributing insights that influence approaches across adjacent teams
• Represent Lila's foundation model research externally through publications at premier venues, conference presentations, and community engagement

What You’ll Need to Succeed

• PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field
• Multiple high-impact first-author or senior-author publications at premier venues (NeurIPS, ICML, ICLR, Nature Methods, Nature Biotechnology, or equivalent)
• Deep expertise in large-scale generative model architectures and training, with hands-on experience training models on distributed infrastructure
• Demonstrated ability to formulate and drive research programs independently, from problem definition through publication and deployment
• Fluency across ML and at least one life science domain (molecular biology, genomics, protein engineering, nucleic acid design, or related), with experience designing computational experiments grounded in biological reality
• Strong track record of cross-functional collaboration with experimental scientists, translating between ML and biology
• Expertise in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with large-scale distributed training infrastructure (AWS, GCP, or on-prem clusters)

Bonus Points For

• Experience in computational protein design, particularly antibody and nanobody engineering
• Experience designing biological sequences or molecular structures with demonstrated wet-lab validation
• Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
• Experience with agentic frameworks or active learning loops in scientific contexts
• High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAAI, Nature Methods, Nature Biotechnology, or equivalent)

Your Impact at Lila

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team researches and develops large-scale generative models and reasoning frameworks that power automated scientific discovery across Lila's life science domains.

We are seeking a Scientist I or II to join this team as a contributor to foundation model research at the intersection of machine learning and life science data. You will work on generative models spanning biological sequences, molecular structures, and multimodal experimental data, contributing to problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine.

This is an IC role for someone building deep expertise in generative AI applied to biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational-experimental loop, and contribute to Lila's presence in the broader scientific community.

What You'll Be Building

• Contribute to research on foundation models for life science applications, including biological sequence design, structure prediction, and multimodal scientific reasoning
• Design, train, and evaluate generative models on biological and chemical data, incorporating domain-specific constraints and priors
• Be part of the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: support data generation strategy, build pipeline models, and help design feedback loops where experimental results improve model performance
• Translate biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists and computational biologists
• Support research quality and methodology standards within the foundation models program

What You’ll Need to Succeed

• PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field (or Master's with equivalent research experience)
• Strong foundation in generative model architectures and training, with hands-on experience in model development and evaluation
• Ability to formulate and execute research independently, from problem definition through experimentation
• Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, nucleic acid design, or related)
• Experience collaborating with experimental scientists or working with biological/chemical data
• Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows

Bonus Points For

• Experience in computational protein design or molecular structure prediction
• Experience with active learning loops or closed-loop experimental workflows
• Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
• Familiarity with distributed training infrastructure
• High-impact publications or open‑source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAAI, Nature Methods, Nature Biotechnology, or equivalent)

Your Impact at LILA

Lila is seeking a highly motivated and experienced engineering leader to lead a team responsible for our Lila's product data platform. You will own the data platform and infrastructure end-to-end — architecture, delivery, reliability, and developer/data scientist experience.

Our mission is to deliver Scientific Super Intelligence through a reliable, scalable, and self-service infrastructure for data ingestion, storage, processing, and interaction — enabling AI/ML, product teams, and scientists to build data-intensive applications with confidence and speed. Our platform supports analytical and machine learning workloads across Lila, serving autonomous DBTL cycles, instrument data pipelines, and AI inference workflows.

You will be responsible for building and leading a team of talented engineers, driving technical strategy, and ensuring the scalability and performance of our data management and data serving capabilities of our Data Platform. You will work closely with data scientists, data engineers, lab scientists, and product teams to understand their needs and deliver innovative solutions that leverage the power of cutting edge data processing technologies.

What You'll Be Building

• Team Leadership: Build, mentor, and manage a high-performing team of 8-12 data engineering experts. Evaluate and adopt modern data infrastructure - including real-time streaming (Kafka, Flink), columnar engines (DuckDB, ClickHouse), lake house, and cloud-native object storage architectures; Foster a culture of collaboration, innovation, and continuous improvement; Provide technical guidance and mentorship to team members, promoting their professional growth; Conduct performance reviews, provide feedback, and identify opportunities for training and development; Manage team workload, prioritize projects, and ensure timely delivery of high-quality solutions.
• Technical Strategy and Execution: Define and execute the technical roadmap for our data platform, aligning with Lila’s overall data strategy; Drive innovation in data Lakehouse and data serving ecosystem exploring new technologies and approaches to improve usability, performance, scalability, and efficiency; Ensure the reliability, availability, and security of our data processing infrastructure.; Collaborate with other engineering teams to integrate our data processing technologies with other Lila systems and services.
• Stakeholder Management: Partner with data scientists, data engineers, lab scientists, product managers, and other stakeholders to understand their data processing needs and requirements; Communicate technical concepts and solutions effectively to both technical and non-technical audiences; Advocate for best practices in data processing and engineering; Manage expectations and ensure alignment across different teams.
• Engineering Thought Leadership: Represent Lila’s data platform work at external conferences; Deliver presentations, and write blog posts highlighting Lila’s leadership in big data processing.
• Scientist and Engineering Productivity: Drive innovative, agentic, and low-code solutions to deliver data interfaces - exploration, query, analytics, and ML/inference solutions at scale.

What You’ll Need to Succeed

• 12+ years of software development experience, with a focus on data processing at scale. 5+ years of experience leading senior engineers.
• Experience with building on AWS/GCP primitives like S3 + Athena/BigQuery, and query engines.
• Operated data platforms at petabyte scale with sub-second query latency requirements.
• Experience managing data infrastructure supporting 100+ concurrent ML training and inference workloads.
• Familiarity with LLM/AI-native data patterns — vector stores, embedding pipelines, pre/mid/post training.
• Track record of building data platforms in high-growth or early-stage environments where speed-to-value mattered as much as long-term architecture.
• Hands-on coding in Python and modern backend frameworks. Experience with infrastructure-as-code and containerized deployments (Kubernetes).
• BS, MS, or Ph.D. in Computer Science or a related field of study.

Bonus Points For

• Thought leadership in the community via presentations in conferences or blog posts.
• Experience building and growing teams focusing on open source technologies.
• Scientific data management and quality experience
• Built self-service data products/platforms where developer experience was a first-class product concern.

Your Impact at LILA

You will define and deliver secure cloud architectures that meet the highest federal standards while staying practical and engineer friendly. Your designs will become the foundation for multiple systems and scale across teams and environments.

• FedRAMP IL5 / IL6 security architectures co-owned with Application Architects and applied across multiple systems
• Deployable security designs grounded in NIST 800-53 Rev. 5, DoD Cloud SRG (IL5/IL6), and FedRAMP
• Core security building blocks, including:
  • Identity, credential, and access management (ICAM)
  • Network segmentation, trust boundaries, and secure data flows
  • Encryption, key management, and secrets handling
• Zero Trust architecture (NIST 800-207) implemented in production—not just documented
• Reusable security platforms and patterns, including:
  • IAM and access control
  • Secrets management and key lifecycle
  • Logging, monitoring, and detection
  • Secure CI/CD and Infrastructure as Code
• Enterprise reference architectures supporting both FedRAMP and non-FedRAMP systems
• Compliance-aware designs that minimize authorization scope through control inheritance, reuse, and standardization
• Automated compliance evidence pipelines that reduce manual effort and improve audit readiness
• Security-by-design integrations embedded into product engineering and cloud infrastructure
• Architectural support for resilience and response, including threat modeling, risk assessment, incident response, and forensic readiness

What Success Looks Like

• Security controls are designed once and reused across applications and platforms
• Compliance evidence is automated, repeatable, and scalable
• Engineering teams adopt security patterns naturally, without friction
• Security architecture accelerates delivery instead of slowing it down

This role is for someone who wants to build durable, reusable security architecture that scales beyond one system—and enables high-assurance cloud delivery at speed. This is not a policy-only or governance-only position.

What You’ll Need to Succeed

You bring deep, hands-on experience designing and delivering secure cloud systems in highly regulated environments. You’re comfortable translating complex requirements into scalable architecture and guiding teams through delivery.

• 8+ years in cybersecurity, security architecture, or security engineering
• Proven, hands-on delivery of FedRAMP IL5 and/or IL6 systems, from design through authorization
• Strong working knowledge of:
  • NIST 800-53 Rev. 5
  • DoD Cloud Computing SRG (IL5/IL6)
  • FedRAMP authorization and ATO processes
• Cloud security architecture experience in AWS GovCloud
• Experience securing cloud-native platforms, including:
  • Container orchestration (EKS, AKS, OpenShift)
  • CI/CD pipelines and Infrastructure as Code
• Ability to explain control intent and risk tradeoffs, not just map requirements to controls

Your Impact at LILA

Lila is looking for a Senior HR Manager to join the HRBP team and serve as a trusted people partner to assigned managers and client groups at our San Francisco office. You will lead execution of core people programs, advise managers on employee relations and team effectiveness, and help build a strong, consistent culture as the organization scales. You bring enough experience to work with meaningful independence and to start shaping how HR programs operate on the ground — with room to grow into a broader strategic advisory capacity over time.

What You'll Be Building

• Partner with assigned managers to provide hands-on HR guidance on talent management, employee relations, performance management, and team effectiveness
• Coach managers on people leadership, navigating difficult conversations, and developing their teams
• Lead execution of core HR programs including performance review cycles, compensation planning, and engagement initiatives
• Serve as the on-site HR presence for the SF office, supporting employee-facing touchpoints including onboarding, offboarding, and in-person HR needs
• Manage operational HR processes specific to the California office
• Identify trends in people data and employee feedback to surface insights and recommend action
• Collaborate cross-functionally with HR, Legal, and Finance to ensure consistency and compliance across people processes
• Contribute to the continuous improvement of people programs and processes as the HR function matures

What You’ll Need to Succeed

• 7–9 years of progressive HR experience, with at least 3 years in an HRBP, HR generalist, or client-facing HR role
• Experience handling HR operational tasks including onboarding administration, I-9 compliance, and new hire processes
• Demonstrated ability to build trusted relationships with managers and leaders across an organization
• Strong knowledge of core HR disciplines including performance management and employment law fundamentals, with familiarity with California employment law a plus
• Experience coaching managers through complex or sensitive people situations with minimal oversight
• Comfort operating in a fast-paced, evolving environment where priorities shift quickly
• Strong judgment and discretion when handling confidential matters

Bonus Points For

• Experience supporting technical or scientific organizations, particularly in biotech, AI, or deeptech
• Familiarity with HRIS and people tools such as HiBob, Udemy, Greenhouse, or similar platforms
• Background in organizational development, change management, or culture-building initiatives
• Experience in a startup or high-growth company where HR processes and infrastructure were still being built
• Comfort using AI tools to improve and scale people processes and programs

Your Impact at LILA

As a Senior Technical Recruiter at Lila Sciences, you will be the San Francisco-market-facing driver of our engineering and AI/ML research hiring. This role is built for someone who recruits like a technologist and a community builder: you’ll experiment with agentic recruiting tools, go deep in high-signal technical communities (X, GitHub, open-source, research circles), and build real-world relationships through coffee chats and meetups — then convert those relationships into exceptional hires.

What You'll Be Building

• Build and sustain a high-trust network of software engineers and AI/ML researchers in San Francisco through consistent relationship-building: coffee chats, warm intros, repeat touchpoints, and community presence.
• Develop non-traditional sourcing and discovery loops: identify high-signal talent via X, GitHub, open-source ecosystems, technical writing, niche communities, and research networks — not just LinkedIn searches.
• Build and iterate on agentic recruiting workflows/tools (lightweight automations, discovery agents, enrichment/triage helpers, outreach experimentation) to continuously improve how we find and engage the right people.
• Attend and host community touchpoints: meetups, dinners, office hours, and small events that strengthen Lila’s presence and create a durable “people know people” pipeline.
• Partner closely with our Senior Technical Sourcer to convert top-of-funnel into engaged candidates and maintain a high-quality, high-touch experience from first conversation through close.
• Run full-cycle recruiting for technical roles: intake with hiring managers, recruiter screens, interview loop management, candidate coaching, offer strategy, and closing.
• Develop compelling narratives tailored to technical audiences — why Lila, why now, why this role — and evolve messaging based on what you’re hearing in the market.
• Maintain operational rigor: clean pipeline tracking, stage hygiene, weekly updates, and clear next steps for candidates and hiring teams.

What You’ll Need to Succeed

• 3-5+ years of experience in full-cycle recruiting, with a demonstrated track record in highly technical or specialized environments (in-house, agency, or a mix).
• Track record of finding talent through high-signal technical channels (e.g., X, GitHub, open-source communities, meetups, research networks) — not only conventional databases.
• Comfort running a high-touch cadence (multiple in-person conversations per week), with excellent follow-up and relationship management.
• Demonstrated success recruiting for Software Engineers, AI/ML researchers and research-adjacent profiles (research engineers, applied scientists, etc.).
• Hands-on experience building and iterating agentic recruiting tools/workflows (scripts, automations, prototypes, or internal utilities) that improve candidate discovery, prioritization, and engagement; comfort operating in public technical ecosystems (e.g., GitHub/X) when appropriate.
• High ownership and operational discipline in a fast-moving environment where priorities may shift.
• Demonstrated commitment to inclusive hiring practices.
• Curiosity, adaptability, and enthusiasm for Lila’s mission of accelerating scientific discovery through AI.

Bonus Points For

• Experience hosting meetups/salons or building durable technical communities in SF.
• Experience recruiting for research-heavy or frontier AI organizations (or similarly high-bar technical environments).
• Familiarity with research signals (papers, citations, open-source impact) and how they translate to hiring decisions.
• Comfort experimenting quickly with new tooling/workflows and sharing learnings with a team.

Your Impact at LILA

As a Sr./Principal Research Engineer focused on Synthetic Data, you’ll contribute to the vision, roadmap, and delivery of our synthetic data program—from asset generation and simulation to ML training integration and measurable model gains. You will be working with the Research Engineering team in designing, generating, and implementing artificial datasets to train, test, and improve Lila’s platform and help us reach our goals.

What You'll Be Building

• Help to define the synthetic data strategy and multi-quarter roadmap
• Develop evaluation frameworks that tie synthetic interventions to real model performance
• Establish standards for asset quality, diversity, documentation, and reproducibility; contribute to a healthy review culture.

What You’ll Need to Succeed

• 6+ years in applied ML/ML systems with 3+ years leading industry initiatives; track record of advanced algorithms and frameworks designed specifically for generating large-scale synthetic data.
• 8+ years working with modern ML workflows: Python, PyTorch, dataset tooling, training loops, and evaluation frameworks; comfortable profiling and optimizing GPU-heavy pipelines.

Bonus Points For

• Track record of building synthetic data sets from source data to measurably augment model performance in targeted domains.
• Experience instruction fine tuning and hillclimbing therein.
• Building product requirements and feedback into sythentic data generation pipeline at scale
• Knowledge of quantization/distillation, routing/mixture-of-experts, and cost optimization at scale
• Experience working in compliance-heavy environments (HIPAA, PCI, FedRAMP) and on-prem/VPC deployments

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), ML engineers build and operate the systems that turn generative models and reasoning frameworks into production capabilities powering automated scientific discovery across Lila's life science domains.

We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind models spanning biological sequence design, molecular structure prediction, antibody engineering, and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine.

This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational-experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities.

What You'll Be Building

• Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data, including distributed training across GPU clusters
• Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows
• Architect ML infrastructure that supports rapid iteration across sequence design, structure prediction, and multimodal scientific reasoning workloads
• Drive the engineering side of Lila's "Lab-in-the-Loop" lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows
• Define and advance ML engineering standards, tooling, and best practices across the AI organization
• Collaborate with AI scientists to translate research prototypes into robust, scalable production systems, bridging the research-to-deployment gap

What You’ll Need to Succeed

• Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)
• 10+ years of hands-on experience building and operating production ML systems at scale
• Deep expertise in distributed training infrastructure, including experience with large-scale GPU clusters (AWS, GCP, or on-prem)
• Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices
• Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance
• Demonstrated ability to drive technical direction for ML infrastructure independently, from architecture through implementation
• Track record of cross-functional collaboration with research scientists, translating between ML methodology and engineering execution

Bonus Points For

• Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data
• Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows
• Contributions to open-source ML tools, frameworks, or infrastructure projects
• Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, or nucleic acid design)
• Experience with model evaluation frameworks for scientific applications where ground truth is sparse or delayed

Your Impact at LILA

Cloud Security & Compliance Lead is responsible for the end-to-end security, governance, risk management, and regulatory compliance of Lila Sciences’ cloud environments and research workflows. You’ll own cloud security architecture, policy frameworks, data protection, and compliance programs across multi-cloud and on-premises contexts as appropriate. You’ll partner with Engineering, Data Science, IT, Legal, and Compliance to codify secure patterns, enable rapid yet safe experimentation, and maintain a robust governance program with auditable evidence for regulators and customers.

What You'll Be Building

Cloud Security Architecture & Governance

• Define and maintain cloud security strategy, reference architectures, and security baselines for public cloud (AWS, Azure, GCP) and hybrid deployments. 
• Implement secure-by-default patterns for CI/CD is intentionally out of scope; focus on secure design patterns for cloud resources, data flows, and analytics. 
• Establish IAM least privilege, network segmentation, private endpoints, key/secret management, and centralized logging across AWS, Kubernetes (where applicable), and cloud-native services. 

Governance, Compliance & Risk Management

• Develop, implement, and continuously improve policies, standards, and procedures aligned to applicable frameworks (e.g., NIST CSF, NIST 800-53, FedRamp, ISO 27001, SOC 2, GDPR/CCPA). 
• Lead data protection program: data classification, data minimization, data retention, and data lifecycle management; oversee DLP strategies where relevant. 
• Manage third-party risk assessments, vendor security questionnaires, and contract security annexes; maintain evidence for audits. 

Security Controls & Monitoring

• Define and oversee security controls across cloud resources, including identity, access management, encryption, key management, log collection, and telemetry. 
• Collaborate with Security Operations to establish monitoring, alerting, incident response coordination, and evidence collection for audits. 

Compliance & Audit Readiness

• Prepare for internal and external audits; map controls to frameworks and translate them into engineering artifacts and evidence. 
• Maintain alignment with SOC 2, ISO 27001, and other regulatory requirements, coordinate with Legal and Privacy on data protection controls. 

Data, ML/AI Security & Privacy

• Ensure secure data movement, storage, and access patterns; implement data lineage and isolation for training vs. inference in ML workflows. 
• Address privacy-by-design considerations in data science processes; oversee secure handling of sensitive datasets. • Collaboration & Enablement 
• Partner with Engineering, IT, Legal, and Commercial teams to ensure cohesive risk management. 
• Provide security training and awareness for engineering, data science, and product teams; translate security requirements into actionable tasks. 

Evidence & Documentation

• Create and maintain security documentation, runbooks, policies, and evidence packs suitable for audits and regulator requests.

What You’ll Need to Succeed

• Education: Bachelor’s degree in computer science, Information Security, Cybersecurity, Engineering, or related field. Masters preferred.
• Experience: 5–8+ years in cloud security, information security, or a related role; hands-on experience with cloud environments (AWS, Azure, GCP) and Kubernetes is a plus; experience in governance, risk, and compliance activities.
• Certifications: CISSP, CISM, CCSK, ISO 27001 Lead Auditor, SOC 2 Practitioner, or cloud security certifications are desirable.
• Technical Skills:  Strong understanding of cloud architectures, IAM, encryption, KMS, secret management, data protection, and network security.
• Familiarity with Kubernetes concepts and security considerations (RBAC, network policies, pod security standards) as they apply to governance and compliance contexts. 
• Experience with policy frameworks and policy-as-code concepts (OPA, Kyverno, Checkov) for governance and automated compliance checks.
• Knowledge of SBOMs, software supply chain concepts, artifact signing (Cosign/Sigstore), and SBOM generation. 
• Familiarity with audit-ready control mapping, risk assessment, and remediation tracking.
• Soft Skills: Excellent communication, stakeholder management, and the ability to translate complex security requirements into actionable business and engineering tasks.

Bonus Points For

• Experience with data-intensive research environments, HPC, or bioinformatics workloads.
• Familiarity with privacy by design, data governance, and model governance in ML/AI contexts.
• Prior startup or high-growth experience enabling developer velocity with strong guardrails; knowledge of Sigstore/Cosign and SLSA concepts for software supply chain integrity.
• Experience with at least one modern programming language (Python, Go, Rust, JavaScript) for automation or tooling.

Your Impact at LILA

Lila is building a talent-dense, high-agency AI safety team at Lila that will engage all core teams within the organization (science, model training, lab integration, etc) to prepare for risks from scientific superintelligence. We are looking for a Senior or Principal Technical Program Manager to join our AI Research team and serve as a critical operational force behind our AI safety work.

In this role, you will be the connective tissue between our researchers, model development teams, engineering, policy stakeholders, and leadership — translating complex, fast-moving research programs into structured, accountable execution. While not an ML researcher yourself, but you must be genuinely curious about the technical substance of AI safety work, and able to clearly articulate what our efforts are trying to accomplish. The successful candidate will be someone who thrives in ambiguity, communicates exceptionally well across audiences, and knows how to build clarity and momentum in a fast paced and rapidly growing team.

What You'll Be Building

• Serve as the key communication interface between the AI safety team and the research, science and technical teams, effectively communicating complex outcomes and securing necessary resources. Set up the organisational information flows that allow for this communication to happen with speed at scale.
• Drive accountability across distributed, cross-functional teams without relying on direct authority; build consensus through clear communication and sound judgment.
• Implement best practices for rapid experimentation and iteration, facilitating efficient and agile program progression.
• Develop clear documentation and reporting to communicate vision, track progress, and align initiatives with organizational priorities.
• Represent program status and risks accurately and confidently, even when the picture is uncertain or evolving.

What You’ll Need to Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, Life Sciences, or a related field.
• 6+ years of program or project management experience in the technology or life sciences field.
• Proven experience in program management, leading cross-functional teams and driving programs to successful completion.
• Strong analytical and problem-solving skills, with the ability to turn technical requirements into actionable program roadmaps.
• Exceptional written and verbal communication skills; a track record of producing executive-quality documents, roadmaps, and updates that drive decisions.

Bonus Points For

• Direct experience in AI/ML research or product organizations, ideally in a program management or research operations capacity.
• Working familiarity with AI safety concepts — alignment, red-teaming, evaluations, responsible scaling — sufficient to engage credibly with researchers and ask good questions.
• Experience managing programs that span safety, compliance, or governance workstreams, particularly in the context of model development and deployment.
• Enthusiastic about emerging technologies and experienced in driving rapid experimentation and program iteration.