Your Impact at LILA

Lila is seeking a motivated SCADA Engineer to contribute to the design, development, and implementation of control systems for automated scientific equipment. This role involves applying core engineering principles to support PLC controls, robotics integration, troubleshooting systems, and collaborating within cross-functional teams to enhance laboratory automation.

What You'll Be Building

• Assist in designing, programming, and deploying control systems using PLC and SCADA technologies to optimize laboratory workflows.
• Support the integration and testing of advanced robotics and conveyance systems within existing automation frameworks.
• Assist in integrating 3rd party equipment and vendor software into SCADA and make custom HMIs to aid in user controls.
• Contribute to the design and implementation of safety systems to safely operate a mix of lab equipment and robotics.
• Collaborate with software teams to support the integration of control systems with broader IT/OT factory infrastructure.
• Create, update, and maintain engineering documentation, including P&ID diagrams, wiring schematics, control narratives, and instrument IO lists.
• Support multi-team automation projects by independently executing technical tasks, meeting milestones, and adhering to engineering best practices.
• Perform calibration, testing, troubleshooting, and validation of control systems to ensure they meet performance, accuracy, and safety standards.
• Work closely with subject matter experts (SMEs) and project teams to understand technical requirements and solve moderately complex hardware/software challenges.
• Identify recurring technical issues and implement improvements to enhance system reliability and operational efficiency.

What You’ll Need to Succeed

• 2–4 years of experience in designing, implementing, or maintaining control systems, preferably in automated manufacturing or laboratory environments.
• Solid understanding of PLCs, HMI, SCADA, and related automation software and hardware.
• Familiarity with industrial networking basics in OT deployments (e.g., IP addressing, basic routing).
• Experience reading and creating detailed engineering documents and schematics using tools like AutoCAD, SolidWorks Electrical, or similar.
• Hands-on experience or academic background in robotics integration and programming.
• Effective communication and collaborative skills for working within project teams and with internal stakeholders.
• Strong analytical and problem-solving skills, with the ability to independently execute solutions for well-defined technical issues.

Bonus Points For

• Ignition SCADA Core training and HMI design experience.
• Structured text programming experience in TwinCAT, Python, C++, etc.
• Familiarity with communication protocols like OPC-UA, MQTT, and REST APIs.
• Exposure to safety system design concepts and classifications like SIL or PL.
• Basic understanding of managed network switches, port addressing/IP subnetting, and DHCP services.
• Ability to troubleshoot basic traffic flows between OT networks and cloud instances.

Your Impact at LILA

We're building autonomous robotic systems that serve as the intelligent physical infrastructure of our Scientific Superintelligence Platform at Lila. To support our robotics research roadmap, we need to scale robot data collection to produce high-quality robot data, collected by humans, at scale, day after day while improving per-operator throughput, lowering churn, and keeping data quality consistent across every supervisor and shift

We're hiring an Operations Manager to own that scale-up end-to-end.

As Data Operations Manager, you'll own the day-to-day function that generates the training data powering Lila's robotic systems. That means managing the supervisors who run our data collection shifts, setting the conditions for consistent operator performance, and making sure every hour of work on the floor translates into high-quality, usable data for our research team.

This is a builder role. The team is early — a growing group of operators across two shifts — and the processes, metrics, and programs that will scale it are still being shaped. You'll design and own them. You'll report directly to the Robotics team and serve as the primary operational link between the floor and Robotics Research.

What You'll Be Building

• Run the operation: Own the day-to-day operation across all shifts. Set throughput targets, monitor adherence, debug when shifts under-deliver, and make weekly calls on staffing, scheduling, and task allocation
• Manage the team: You'll manage the supervisors who directly run our operators. Set clear goals, run a consistent performance loop, and grow the supervisory bench as shifts, and sites, expand
• Drive retention and engagement: Data collection is physical, repetitive, attention-heavy work. Design and continuously refine onboarding, training, performance incentives, recognition, and scheduling so operators stay engaged and the bar keeps rising. Approach this with a product mindset: hypothesize, ship, measure, iterate
• Own throughput and quality metrics. Set targets, build the dashboards, and run the review cadence that make performance visible across the team. Reduce reject rates, compress reset time, and increase usable hours per shift
• Plan capacity: Translate research priorities into concrete operational plans — operator allocation, supervisor coverage, equipment requirements, and risk. Forecast throughput, flag potential misses early, and re-plan as priorities evolve
• Scale the footprint: Partner with facilities, recruiting, and leadership to expand the operation across additional shifts and, in time, additional sites. Maintain an operational playbook that makes opening each new shift or site repeatable rather than bespoke
• Be the bridge to research: Meet with the research team to understand upcoming priorities: which tasks, which embodiments, what success looks like. Translate those needs into shift plans, and surface friction (poor reset ergonomics, ambiguous instructions, unreliable hardware) before it costs the team hours

What You’ll Need to Succeed

• 4+ years of operations or program management experience, including direct people management
• A track record of running shift-based, throughput-driven operations — ****warehousing, manufacturing, contact centers, data labeling, clinical ops, field operations or comparable
• Demonstrated success reducing churn or improving retention in a frontline workforce: We want to hear the specifics: the baseline, the intervention, and the outcome
• Comfort with metrics: You should be able to look at our shift summary, immediately spot what's interesting, and know what to ask for next
• Builder mindset: This is a function being built, not inherited. Some processes are still taking shape, and we are looking for the right person who sees that as an opportunity to grow and deliver
• Strong written communication — ****you will be the connection between the floor and research and engineering teams, and much of that work happens in documentation
• Willingness to be on-site and on-floor: This role depends on presence and proximity to the team

Bonus Points For

• Experience managing managers or building a supervisor layer from scratch
• Background in robotics, autonomous systems, or physical AI data collection
• Experience scaling a frontline team from under 20 to 100+
• Familiarity with workforce management software, shift-scheduling tooling, or QA workflow tools
• Background in industrial engineering, operations research, or a related quantitative field

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

Lila Sciences is seeking a Mechatronics Engineer II to join our Advanced Engineering Team to design, develop, and optimize electromechanical systems. This role bridges hardware, software, electrical, and controls to deliver intuitive, high-performance automated solutions.

What You'll Be Building

• Write control software to coordinate sensors, actuators, and motion systems into cohesive automated solutions
• Develop HMI/GUI interfaces for operator interaction and system monitoring
• Prototype and build test jigs and fixtures to de-risk designs and optimize system performance
• Program and tune servo drives, actuators, and sensor feedback loops
• Create control algorithms and logic for automated equipment
• Collaborate with cross-functional engineering teams

What You’ll Need to Succeed

• Bachelor's degree in Mechatronics, Electrical, or Mechanical Engineering
• 3-5 years of relevant experience
• Experience with HMI development platforms
• Proficiency with version control systems, especially Git & Github.
• Proficiency with C/C++ and Python, or similar languages
• Experience with microcontrollers (STM-32, Arduino, Raspberry Pi)
• Experience with rapid prototyping techniques
• Experience specifying and integrating sensors, motors, drives
• Understanding of analog signal types (4-20mA, 0-10V) and discrete I/O
• Thrives in a hands-on prototype heavy environment
• Strong troubleshooting and debugging skills

Bonus Points For

• Understanding of machine states, control theory and PID tuning
• Experience or familiarity with PLC based systems
• Familiarity with industrial communication protocols (EtherNet/IP, RS-232, OPC/UA, MQTT)
• Experience or familiarity with CAD software (Solidworks, Creo, Siemens NX)
• Experience working directly with end-users to develop requirements

Your Impact at LILA

We are seeking a Staff Software Engineer - AI Lab Execution System to join our software group and help build the next generation AI-driven scientific platform. In this role, you will design, build, and optimize intelligent, data-driven applications front-end. You will focus on developing UI, services, high-performance APIs, databases, and ensuring the reliability of services that integrate advanced AI frameworks with complex scientific analytics and laboratory workflows.

You’ll work closely with ML researchers, platform engineers, and scientists to develop systems that can handle diverse workloads and scale seamlessly, including structured SQL database, data lake houses, and vector databases. This is an opportunity to apply your deep front-end and backend expertise to a cutting-edge AI platform with real scientific impact. If you are passionate about building performant, and elegant systems, we would love to hear from you!

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.
• 4-6 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend or data modeling and design.
• Typescript, React and Python Expertise: Strong experience with React and Typescript is required.
• Expertise in Databases: Strong experience with SQL, NoSQL, and emerging database technologies (e.g., Vector DBs); proven track record in schema design, indexing, and query optimization.
• API Development: Proven ability to design and scale RESTful or GraphQL APIs with a focus on reliability and performance.
• Hands on experience using AI coding assistants to drive productivity is required.
• Scientific or Data-Intensive Domains: Experience working with life sciences, material sciences, or other research-heavy fields.
• 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, GCP, or Azure; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions).
• Orchestration Systems: Experience with orchestration tools (Flyte, Temporal, Airflow, Prefect, etc.).

Your Impact at LILA

Lila is building AI Science Factories (AISFs) — highly automated, AI-directed labs that generate the proprietary experimental data powering our AI platform. As we scale from one AISF to many, the operational infrastructure that keeps those factories running is a first-class strategic capability.

The Director of Lab Operations & Maintenance Management (LOMM) owns the physical and logistical foundation of Lila's science operations: consumables and reagent supply chains, equipment asset management, CMMS management, waste coordination, space readiness, preventative maintenance (PM), first response equipment/instrument demand maintenance, and multi-shift logistics continuity.

This role will build out and manage Lila's Maintenance Management (MM) function from the ground up — hiring the initial team, developing best practices, and establishing the operational handoff as we mature and scale.

But this role is as much about building these systems as running them — you'll define the LOMM operating model, lead its expansion to new sites, drive AI-enabled innovation in how we manage lab resources, and author the standards that travel with every AISF we open.

You'll operate as a recognized authority on lab operations within Lila, partnering directly with VPs and senior leaders across Autonomous Science Platform (ASP), Finance, EHS, and Facilities. You'll be a principal contributor to the Blueprint program — the global deployment framework that defines how Lila builds and runs AI Science Factories at scale.

What You'll Be Building

Lab Operations Strategy & Operating Model

• Define and own the LOMM operating model across Lila sites — from single-shift pilot to multi-site, continuous operations
• Establish the systems, org structure, staffing model, and governance framework that enable LOMM to scale reliably
• Set and track operational KPIs (inventory accuracy, stockout rate, PM scheduling compliance, asset data integrity, ticket resolution time); use data to drive structured improvement cycles and report trends to ASP leadership

Consumables, Reagents & Supply Chain

• Own end-to-end supply chain continuity for all lab consumables, reagents, and PPE across all shifts and sites
• Design and govern the par-level inventory system, reorder triggers, cold storage management, and expiry tracking infrastructure
• Lead dual-source qualification strategy for high-risk consumables; build the satellite site supply chain playbook for future AISFs in support of the Blueprint project

CMMS & Equipment Asset Management

• Own CMMS strategy, selection, implementation, and long-term governance — accountable for data accuracy across all commissioned instruments and mobile assets
• Define asset tagging standards and location tracking protocols in alignment with Blueprint's asset taxonomy
• Own PM coordination across sites; ensure calibration access and instrument readiness for production operations

Maintenance Management— Build & Oversight

• Build out the Maintenance Management function from scratch in collaboration with Hardware & Controls Engineering (HCE) team: define the team structure, hire initial MM staff, and establish the operating model for instrument PM, calibration, and first-response troubleshooting
• Stand up a dedicated L1 (Level 1) maintenance support team as the first line of response for lab-floor issues across shifts: triage incoming requests, execute standard work (resets, swaps, basic mechanical/electrical checks, consumables replenishment for instruments as defined), and escalate efficiently to HCE- L2/L3 Sustaining Engineering, vendors, or Integration Automation Engineering when deeper troubleshooting or design changes are required
• Establish the escalation and handoff protocols between MM, LabOps, Research Operations (production team), and HCE/Automation; define the boundary between maintenance of commissioned instruments that are best serviced by MM and engineering modifications
• Explore and define the HCE onboarding/training model for MM technicians, including potential rotational program for cross-training and augmentation

AI Integration & Continuous Improvement

• Lead Lila's thinking on AI-enabled lab operations — evaluate, pilot, and scale intelligent solutions across inventory management, predictive reordering, asset tracking, and operational reporting
• Build a continuous improvement culture within LOMM: baseline metrics, run structured improvement cycles, and bring external best practices from the biotech/life sciences industry into Lila's operating model
• Define the innovation roadmap for LOMM in partnership with engineering and operations teams

Vendor & Logistics Management

• Own vendor relationships for all lab supplies and logistics within approved frameworks; partner with Finance on contract strategy for key suppliers
• Oversee lab-side receiving, intake, space readiness for new instrument deployments, waste and hazmat scheduling, gas management, and cold storage operations
• Manage LOMM support ticket system as the single intake for all lab supply, equipment, and space requests

Team Leadership

• Lead, grow, and develop the LOMM team across shifts and sites, scaling with each new site and shift expansion
• Build and lead the MM team as a direct report function — from first hire through a fully operational multi-site MM capability
• Define staffing models, hiring roadmaps, and capability development plans for both functions as they mature
• Build a team culture defined by operational rigor, proactive problem-solving, and continuous improvement

Blueprint Contribution & Multi-Site Scaling

• Author and own the LOMM chapter of Blueprint — documenting processes, system requirements, staffing models, and vendor frameworks so LOMM scales reliably to every new AISF
• Pilot the Site Launch Readiness Scorecard for each new facility; feed findings back into Blueprint standards
• Define the LOMM readiness criteria and pre-commissioning checklist for all future site launches

What You’ll Need to Succeed

• 12–15+ years of lab operations experience in a biotech or life sciences environment, with demonstrated strategic impact at the program or portfolio level
• Proven track record of defining and scaling lab operations systems — not just inheriting and running them; you've built operating models, established governance, and led functions through significant growth
• Deep expertise in CMMS selection, implementation, and governance — you've owned platform decisions and data accuracy programs, not just used the tool
• Experience building and leading teams across multiple functions, shifts, or sites — including hiring from scratch and developing operational capability in parallel with day-to-day execution
• Experience owning and successfully defending ISO 9001:2015 audits related to preventive maintenance (PM) and calibration programs (e.g., evidence packages, CAPAs, and auditor-facing readiness)
• Recognized as an authority in lab operations within your organization; comfortable operating as a peer to VPs and senior leaders and representing your function externally
• Strong strategic and systems-level thinking — you anticipate org-wide operational challenges and define the structures to solve them, not just the immediate fix

Bonus Points For

• Experience building or overseeing a Maintenance Management or instrument PM function — familiarity with IQ/OQ/PQ, calibration registries, MTBF tracking, and service vendor management
• Experience leading or partnering closely with field service / technical service teams (dispatch, SLAs, escalation models, parts logistics, and service performance management) in high-uptime environments
• Experience applying AI or intelligent automation to lab operations workflows (e.g., predictive inventory, automated asset tracking, AI-assisted reporting)
• Background in materials science, life science, and chemistry lab environments — familiarity with the specific consumables, reagents, handling requirements, and vendor landscape in those domains
• Experience supporting multi-shift or 24/7 lab operations
• Familiarity with QMS-adjacent environments (SOP document control, change control, training records)
• Experience contributing to a global site deployment or standardization program (playbook authorship, site launch readiness frameworks)

Your Impact at LILA

As a member of our team in the Physical Sciences division, you will shape the ML- and agent-driven infrastructure that enables Lila’s scientific superintelligence to autonomously construct, execute, and interpret complex physics simulations. Your work will focus on building the core systems that allow AI agents to reason over and control computational experiments, spanning from electronic structure calculations to surrogate-driven atomistic modeling, and beyond. Your work will directly influence how autonomous computational processes will explore chemical and materials landscapes with unprecedented autonomy and robustness.

What You'll Be Building

• Architect and implement agentic frameworks that support dynamic, multi-stage simulation workflows for scientific tasks
• Develop pipelines enabling agents to autonomously plan, schedule, execute, and interpret computational tasks at scale
• Build integration layers and APIs that connect ML models, large-scale simulation engines, databases, and heterogeneous compute platforms.
• Work with AI researchers to productionize agent behaviors, including tool-use strategies, simulation-aware decision loops, and adaptive task planning.
• Improve the robustness, modularity, performance, and reproducibility of agent-driven computational workflows; build internal tooling for debugging, observability, and validation.

What You’ll Need to Succeed

• MS/PhD or equivalent experience in Computer Science, ML/AI, Scientific Computing, or a related technical field.
• Strong experience building ML-driven pipelines, workflow systems, or tool-use frameworks, ideally for complex or scientific applications.
• Proficiency in Python and ML ecosystems; experience with one or more compiled languages (e.g., C++, Rust, Julia) is beneficial.
• Familiarity with large-scale scientific or engineering software, including integrating external tools into automated computational workflows.
• Experience with distributed systems, HPC environments, cloud platforms, or accelerator-based computing.
• Deep understanding of modern ML architectures and their deployment in production systems (e.g., GNNs, transformers, diffusion models, multimodal or tool-using models).
• Strong engineering fundamentals: reproducibility, testing, modular design, CI/CD, and scalable ML operations.

Bonus Points For

• Experience developing or integrating agentic frameworks, autonomous ML pipelines, or multi-step tool-using agents, particularly for scientific applications
• Background in large-scale simulation frameworks, scientific workflow orchestration, or automated computational experiment platforms.
• Contributions to open-source ML infrastructure, workflow engines, agent frameworks, or scientific software ecosystems.
• Familiarity with data-centric engineering practices: streaming systems, provenance tracking, distributed metadata services, or large-scale data orchestration.
• Experience with workflow orchestration systems (e.g., Flyte, Prefect, Airflow) and container orchestration (Kubernetes).
• Familiarity with materials science simulation codes (VASP, LAMMPS) or workflow frameworks (atomate, aiida) is a plus but not required.
• Expertise in advanced ML techniques relevant to agentic reasoning (planning models, self-improving systems, multi-tool agents, RLHF/RLAIF workflows).

Your Impact at LILA

Lila Sciences is hiring a Software Engineer to develop the next generation of Lab Instrument Integration Software, which is a foundational component of our AI-enabled laboratory. We are looking for self-motivated problem solvers who enjoy sitting at the often tricky interfaces between software, science, and hardware to deliver powerful solutions that will accelerate science.

What You'll Be Building

• Lab Instrument Software Development: Develop robust software to enable communication between robotics and automation hardware (e.g., robotic arms, liquid handlers, sensors) and the control software, ensuring reliable and efficient hardware integration.
• System Integration: Seamlessly integrate lab instruments into our Next Generation, AI-enabled lab automation infrastructure, managing complex interfaces between cloud and edge infrastructure, and device execution.
• Testing and Debugging: Perform rigorous testing, debugging, and troubleshooting of drivers to identify, isolate, and rectify performance issues, bugs, and compatibility challenges.
• Technical Documentation: Create, update, and maintain clear, comprehensive documentation for driver interfaces, APIs, and integration methods, facilitating ease of use by internal teams and end-users.
• Collaboration and Support: Work closely with cross-functional teams, including hardware engineers, software developers, product managers, and end-users, providing technical support and resolving integration challenges promptly.
• AI driven development: work with state of the art coding models and development infrastructure to improve the speed and quality of software delivery.

What You’ll Need to Succeed

• Proficiency in Python, C#/.NET, or other programming languages or frameworks is essential
• Demonstrated ability in debugging and problem-solving, preferably hardware-software integration issues.
• Strong analytical thinking and a structured approach to troubleshooting and resolving complex technical problems.
• Strong verbal and written communication skills to collaborate effectively with multidisciplinary teams.

Bonus Points For

• A strong interest in or background in science
• Experience in developing software for laboratory automation systems or robotics used in life sciences, pharmaceutical, or similar industries.
• Experience working directly with end-users (e.g., scientists, laboratory technicians), using feedback to drive product improvements.
• Experience with hardware integration and device driver development, especially in robotics or automation contexts.
• Proficiency with version control systems, especially Git.

Your Impact at LILA

As a member of our team in the Physical Sciences division, you will design and build intelligent agent-driven systems that enable AI-accelerated and AI-orchestrated process engineering across a broad range of industrial applications. The core mission of this role is to develop methods by which AI agents can reason about, design, simulate, optimize, and operate complex physical and chemical processes using existing or ML-driven process engineering tools. You will focus on creating agentic infrastructures that allow AI systems to plan and execute multi-step process engineering workflows, ranging from process synthesis and flowsheet generation to steady-state and dynamic simulation, control strategy design, and techno-economic evaluation. Your work will directly shape how Lila’s scientific superintelligence performs closed-loop autonomous process engineering to solve real-world problems.

What You'll Be Building

• Architect and implement agentic frameworks that support end-to-end process engineering workflows, including process setup, simulation, optimization, and analysis.
• Develop AI agents capable of autonomously planning, executing, and iterating on process engineering tasks using existing tools (e.g., steady-state and dynamic simulators, optimizers, and data systems).
• Explore agentic approaches for advanced tasks such as process intensification, control co-design, real-time optimization, and closed-loop learning from operational data.
• Improve robustness, interpretability, and reproducibility of agent-driven process engineering workflows; build internal tooling for debugging, observability, validation, and auditability.
• Work with interdisciplinary teams to apply agentic process engineering to a broad range of industrial applications

What You’ll Need to Succeed

• PhD or equivalent experience in Chemical Engineering, Industrial Engineering, Systems Engineering, or a closely related field.
• Research experience in method development for process engineering, a strong publication record in this area or established industry experience
• Hands-on experience with process simulation and optimization tools (commercial or open-source), including steady-state and dynamic modeling.
• Proficiency in Python and scientific/engineering computing ecosystems
• Experience integrating external engineering tools or simulators into automated workflows via APIs, scripting interfaces, or custom wrappers.
• Familiarity with distributed systems, HPC environments, cloud platforms, or scalable compute infrastructure.

Bonus Points For

• Experience developing or integrating agentic frameworks, autonomous planners, or multi-step tool-using AI systems for engineering or scientific domains.
• Experience building computational pipelines, automation systems, or tool-use frameworks for complex engineering or scientific workflows.
• Experience with digital twins, real-time optimization, or model-predictive control frameworks.
• Background in techno-economic analysis (TEA), life-cycle assessment (LCA), or sustainability-driven process design.
• Contributions to open-source engineering software, ML infrastructure, workflow engines, or agent frameworks.

Your Impact at LILA

The Director, Discovery Chemistry will lead the design, synthesis, and optimization of small molecules across Lila’s discovery platform, with a primary focus on organic synthesis, molecular design, and scalable chemistry execution. This leader will build and scale a world-class molecular chemistry program that translates AI-generated molecular hypotheses into high-quality, synthetically tractable, and functionally validated chemical matter.

Working closely with Lila’s AI, automation, computational, biology, and platform teams, this role will establish the chemistry infrastructure required to rapidly progress novel molecules from concept to validated candidates across a broad range of discovery efforts. The Director will drive synthetic strategy, route design, analog generation, molecular property optimization, and chemistry-enabled innovation, while enabling seamless integration with robotic synthesis and platform-based experimentation.

This leader will play a central role in shaping Lila’s molecular discovery engine by combining deep expertise in synthetic organic chemistry, molecular design, and data-enabled experimentation to accelerate the discovery of high-value molecules for therapeutic and broader platform applications.

What You'll Be Building

• Drive discovery chemistry strategy across discovery programs, with strong emphasis on small molecule design, synthetic innovation, analog generation, and optimization of chemical matter.
• Lead the design and execution of synthetic routes for novel small molecules, emphasizing speed, robustness, scalability, and compatibility with automated synthesis platforms.
• Develop and implement efficient workflows for molecular design, compound prioritization, analog generation, and iterative design–make–test cycles in collaboration with AI and computational teams.
• Partner closely with automation and platform teams to establish robotic and high-throughput chemistry capabilities that expand accessible chemical space and accelerate molecule generation.
• Guide the selection of reagents, reaction modalities, and synthetic methodologies to enable rapid exploration of structurally diverse and functionally relevant chemical matter.
• Drive discovery chemistry strategy across discovery programs, with strong emphasis on small molecule design, synthetic innovation, analog generation, and optimization of chemical matter.
• Lead the design and execution of synthetic routes for novel small molecules, emphasizing speed, robustness, scalability, and compatibility with automated synthesis platforms.
• Develop and implement efficient workflows for molecular design, compound prioritization, analog generation, and iterative design–make–test cycles in collaboration with AI and computational teams.
• Partner closely with automation and platform teams to establish robotic and high-throughput chemistry capabilities that expand accessible chemical space and accelerate molecule generation.
• Guide the selection of reagents, reaction modalities, and synthetic methodologies to enable rapid exploration of structurally diverse and functionally relevant chemical matter.
• Oversee molecular optimization by integrating chemistry principles including structure–activity relationships, physicochemical properties, selectivity, developability, and synthetic tractability.
• Collaborate with biology, screening, and profiling teams to interpret assay and characterization data and translate those insights into informed molecular design decisions.
• Drive discovery efforts from early chemical matter generation through optimization of advanced molecules, including scaffold evolution, property tuning, and progression toward candidate-quality compounds.
• Establish best practices for experimental design, chemistry data capture, compound registration, quality control, and reproducibility across molecular chemistry operations.
• Contribute strategically to program direction by aligning chemistry efforts with therapeutic hypotheses, platform capabilities, and broader company priorities.

What You’ll Need to Succeed

• PhD in Organic Chemistry, Medicinal Chemistry, Chemical Biology, or a closely related discipline with 12–15 years of experience in small molecule discovery and molecular design.
• Demonstrated experience building and leading high-performing chemistry teams, including hiring, mentoring, and developing scientists across multiple levels.
• Proven track record advancing small molecule programs through molecular design, high-throughput synthesis, optimization, and candidate progression.
• Deep expertise in synthetic organic chemistry, retrosynthesis, route design, and practical execution of complex multistep syntheses.
• Strong experience optimizing molecular properties, including potency, selectivity, physicochemical behavior, and overall developability of small molecule series.
• Demonstrated success designing and delivering compound libraries or analog series to efficiently explore chemical space and address multiparameter optimization challenges.
• Experience working effectively in highly interdisciplinary environments spanning chemistry, biology, automation, and computational sciences.
• Experience collaborating with biology, screening, pharmacology, or related functions to incorporate experimental data into chemistry decision-making.
• Strong understanding of compound quality standards, purification and characterization workflows, and chemistry data integrity.
• Track record of scientific leadership, program delivery, and advancing discovery-stage molecules in an industrial or high-performance research environment.

Bonus Points For

• Experience working in AI-enabled discovery environments or integrating computational design and machine learning insights into chemistry workflows.
• Experience building or scaling automated, robotics-enabled, or high-throughput chemistry platforms for molecular synthesis and optimization.
• Familiarity with modern synthesis-enabling technologies.
• Experience translating chemistry needs into platform requirements, including instrumentation selection, workflow design, and integration with automated systems.
• Broad understanding of molecular design principles across therapeutic discovery and other small-molecule innovation areas.
• Experience operating in fast-paced environments where scientific creativity, speed, and cross-functional execution are critical.
• Ability to evaluate synthetic feasibility alongside innovation potential to prioritize molecules and chemical series effectively.
• Strong communication and leadership skills, with the ability to align multidisciplinary teams around ambitious scientific goals.

Your Impact at LILA

The Scientist I/II, Organic Chemistry will develop, execute, and optimize modern chemical transformations that support Discovery Chemistry efforts across Lila’s platform, with a focus on the rapid synthesis of small molecules and structurally diverse chemical matter. This role is intended for an experimental organic chemist who combines strong foundations in synthetic chemistry with hands-on experience in high-throughput experimentation (HTE), reaction workup, and analytical characterization.

Working closely with Discovery Chemistry, analytical chemistry, automation, screening, and AI/computational teams, this scientist will help build and apply efficient chemistry workflows that accelerate molecular design–make–test–analyze cycles. The Scientist II will contribute to reaction development, analog generation, and route execution using modern organic chemistry approaches that are compatible with scalable, data-rich, and increasingly automated discovery environments.

This role is ideal for a chemist excited by solving synthetic problems at the interface of reaction innovation, high-throughput experimentation, process efficiency, and analytical insight, and who is motivated to help shape next-generation discovery workflows beyond traditional bench chemistry.

What You'll Be Building

• Design, execute, and optimize chemical transformations relevant to Discovery Chemistry programs, with emphasis on the rapid synthesis of small molecules and analog series.
• Apply modern synthetic organic chemistry to enable efficient exploration of structurally diverse and medicinally relevant chemical space.
• Plan and perform multi-step synthesis, reaction optimization, workup, purification, and compound characterization in support of molecular discovery campaigns.
• Design and execute HTE workflows for reaction screening, condition optimization, reagent evaluation, and rapid assessment of transformation scope.
• Develop robust approaches for reaction setup, reaction workup, sample preparation, and analytical readout to support fast and information-rich experimentation.
• Use analytical tools such as chromatography, NMR, and related methods to assess reaction performance, characterize products, and guide synthetic decisions.
• Contribute to the development of practical and scalable chemistry workflows that integrate reaction discovery, reaction optimization, and downstream compound generation.
• Partner closely with Discovery Chemistry scientists to translate molecular design concepts into executable synthetic strategies and experimentally tractable reaction plans.
• Collaborate with analytical chemistry and automation teams to build workflows that connect reaction execution with streamlined workup, purification, and data generation.
• Support the adaptation of synthetic workflows to parallel, automated, or robotics-enabled platforms where appropriate.
• Interpret reaction and analytical data rigorously, identify bottlenecks, and propose solutions to improve efficiency, robustness, and reproducibility.

What You’ll Need to Succeed

• PhD in Organic Chemistry, Medicinal Chemistry, or a closely related discipline with relevant postdoctoral and/or industry experience.
• Strong expertise in synthetic organic chemistry, including design and execution of multi-step synthesis and development of chemical transformations relevant to small molecule discovery.
• Hands-on experience with HTE for reaction screening, optimization, or scope evaluation.
• Experience with reaction workup, sample preparation, purification, and analytical characterization in fast-paced synthetic workflows.
• Strong working knowledge of analytical methods commonly used to support organic synthesis.
• Demonstrated ability to troubleshoot challenging reactions and independently solve synthetic and experimental problems.
• Strong understanding of how reaction design, execution, workup, and analysis fit together in efficient discovery workflows.
• Ability to work effectively in interdisciplinary environments spanning chemistry, analytics, automation, and computational sciences.
• Strong organizational, documentation, and communication skills, with commitment to scientific rigor and reproducibility.

Bonus Points For

• Experience supporting Discovery Chemistry or medicinal chemistry programs in biotech, pharma, or advanced research environments.
• Familiarity with modern reaction classes relevant to small molecule discovery, including coupling chemistry, heterocycle synthesis, late-stage functionalization, and rapid analog generation.
• Experience with parallel synthesis or robotics-enabled chemistry workflows.
• Exposure to data-rich, AI-enabled, or platform-based R&D environments.
• Collaborative mindset and enthusiasm for building modern chemistry capabilities that go beyond traditional bench-scale synthesis.

Your Impact at LILA

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before.

As a Head of Software Product you will serve as the primary architect of our software product strategy. You will lead the development of our “Agentic Superscience” software—the foundational platform designed to reinvent the scientific method through AI. Reporting directly to the Chief Revenue & Product Officer, you will be responsible for transforming complex scientific workflows into a scalable, world-class software ecosystem. You will set the roadmap, build the processes, and the culture necessary to deliver Lila’s most critical competitive advantage. As a member of the leadership team, you will play a critical role in shaping the culture, technical strategy, and therapeutic impact of our software.

What You'll Be Building

• The Agentic Platform: Lead the roadmap and build of our foundational software platform that enables AI agents to conduct, analyze, and iterate on complex materials, chemistry, and biology experiments.
• A Cross-Functional Engine: Create the integrated framework that allows Software, ML, and Wet-lab teams to work in a continuous, high-performance loop.
• Customer-Centric Interfaces: Deliver reliable, intuitive interfaces and high-performance endpoints that make scientific superintelligence accessible to diverse users.
• Operational Excellence: Own the product lifecycle from discovery to delivery. Implement robust frameworks for prioritization that balance rapid innovation with enterprise-grade reliability.
• The Product Org & Executive Voice: You will build the frameworks for high-level communication, serving as the authoritative voice for the software product and providing the strategic insights that will influence Lila’s overall trajectory.

What You’ll Need to Succeed

• 15+ years of experience in Product Management, with a track record of shipping complex, multidisciplinary software systems (0-to-1 and scaling phases) using a modern tech stack.
• Experience building software with AI at its core and must be AI-native.
• Deep understanding of the scientific method, either via schooling in a scientific domain or from experience building software for scientists
• Demonstrated experience building and leading large, high-performing teams in fast-paced, high-stakes environments.
• A drive for impact against mission goals, with a high degree of comfort in the ambiguity of a ‘founding’ environment.
• Capable of communicating technical and scientific concepts to any audience, from engineers to investors.

Bonus Points For

• Experience with automated or self-driving labs.
• Experience building complex agentic AI strategies.

Your Impact at LILA

Lila Sciences is seeking a relentless executor and strategic operator to spearhead our Instrument Development initiatives and lead our Virtual Lab Team. As the Director of Instrument Development Acceleration, you will be responsible for getting instruments online, fast, reliably, and at scale. The role is for someone who thrives in high-throughput environments and measures success in instruments shipped.

What You'll Be Building

• Set clear expectations, remove obstacles quickly, and keep the team focused on delivery over deliberation.
• Own the rapid design and development of laboratory instruments across the full lifecycle, from concept to operational in the lab, at high volume.
• Architect and implement integration frameworks connecting virtual laboratory environments with physical instrumentation and robotic systems.
• Maintain rigorous hardware-software integration standards, system performance benchmarks, and quality assurance processes without letting them become bottlenecks.
• Make pragmatic technical decisions across software, hardware, and systems integration that keep momentum, escalating only what truly needs escalation.
• Work tightly with AI researchers, software engineers, and robotics teams to drive fast, efficient system integration and deployment.
• Communicate clearly and concisely on status, blockers, and decisions to stakeholders at all levels.
• Build and refine lean development practices that prioritize speed, system reliability, and repeatable quality assurance.

What You’ll Need to Succeed

• Bachelor's or Master's degree in Computer Science, Engineering, Biomedical Engineering, or a related field. Ph.D. is a plus but not required.
• At least 10 years in engineering or technical leadership, with a proven record of delivering complex systems at scale and on time.
• Strong hands-on coding ability in C++, Python, or Java, with deep experience in hardware-software integration.
• Proven ability to lead complex systems integration across software, hardware, and robotic platforms.
• A problem-solver who makes decisions with imperfect information and keeps moving.
• Clear, direct communicator who can align engineers and stakeholders without unnecessary overhead.

Bonus Points For

• Experience with agile or lean development methodologies in high-throughput environments.
• Hands-on knowledge of AI, robotics, and simulation technologies as applied to laboratory instrumentation and automation.
• Experience with system monitoring, performance optimization, and QA in hardware-software environments.
• Track record of scaling teams and processes during periods of rapid growth.

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.

Your Impact at LILA

The Staff/Principal DevOps Engineer will drive the design, implementation, and optimization of our infrastructure and delivery platforms. This role bridges platform engineering, site reliability, and DevOps practices, building scalable, automated systems that enable fast, reliable software delivery across cloud and Kubernetes environments. You will collaborate with software engineers, lab scientists, and ML engineers to build infrastructure that powers automated scientific analysis, experiment orchestration, and more.

What You'll Be Building

• Build Kubernetes-based infrastructure supporting scientific services, ML pipelines, and platform workloads; including production hardening, RBAC, network policies, and Pod Security Standards
• CI/CD pipelines with GitHub Actions/GitLab CI implementing best practices: build attestations, SBOM generation, dependency scanning, and container image hardening
• Infrastructure-as-code with Terraform and Helm; policy-as-code guardrails (OPA/Kyverno/Checkov) with drift detection
• AWS cloud infrastructure: EKS clusters, IAM least privilege, VPC/PrivateLink networking, KMS/Secrets Manager, ECR, S3, and centralized logging/monitoring
• Platform tooling to streamline deployment, observability, and developer workflows, enabling self-service with secure defaults
• Reliability engineering: SLOs/SLIs, incident response, capacity planning, and performance optimization throughout the stack
• Software supply chain practices: artifact signing, registry governance and vulnerability management
• QA and testing infrastructure: static analysis and code quality gate enforcement in CI pipelines, automated end-to-end and browser-based regression test suites, ephemeral test environments for PR-based validation, and pre-merge quality checks
• Automation and tooling in Python or Go to improve infrastructure operations and integrate telemetry with observability platforms

What You’ll Need to Succeed

• Expertise in DevOps, SRE, or Platform Engineering in large scale cloud environments
• Expertise in deploying to cloud environments (AWS, GCP, etc) using infrastructure-as-code (Terraform, Helm) and containerization
• Deep experience with CI/CD systems (GitHub Actions, GitLab CI, or Jenkins) and GitOps practices
• Strong proficiency in Python/scripting languages for automation and tooling
• Strong understanding of Kubernetes operations: deployments, networking, storage, observability, and troubleshooting

Bonus Points For

• Experience with GitOps tools (ArgoCD, Flux)
• SRE practices: observability platforms, chaos engineering, incident management
• Securing ML/AI pipelines (model registries, training clusters, inference gateways)
• Experience in regulated/audit-heavy environments (SOC 2, ISO 27001)
• Supply chain security maturity: SBOMs, image signing, SLSA concepts
• Administering static analysis platforms (custom quality profiles, security hotspot triage) and scaling browser-based test suites across parallel CI environments
• Prior startup/high-growth experience balancing velocity with reliability

Your Impact at LILA

Lila Sciences is seeking a Principal Software Engineer, Instrument Simulations to architect and lead the development of our next-generation laboratory automation platform. In this role, you'll be responsible for designing the simulation infrastructure and integration layer that connects AI-driven experimental design with physical robotic systems, enabling closed-loop scientific discovery at unprecedented scale.

What You'll Be Building

• Architect and implement high-fidelity simulation environments that model complex laboratory instrumentation, fluidics, and robotic workflows
• Design robust integration patterns between heterogeneous hardware systems, ensuring reliable and safe execution of fully automated experiments
• Lead the development of APIs and interfaces that enable scientists and AI agents to seamlessly interact with sophisticated automation infrastructure
• Maintain and help establish best practices for hardware-software integration, system monitoring, and CI/CD in laboratory automation contexts for all newly developed systems
• Mentor engineers, likely leading a small agile team

What You’ll Need to Succeed

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field: Background that supports technical and leadership capabilities.
• 10+ years of software development experience, including 5+ years in a leadership role: Demonstrates ability to lead and manage complex projects and engineering teams.
• Proven experience in architecting and directing large-scale software projects: Skilled in designing and leading the development of scalable, reliable, and maintainable systems.
• Strong proficiency in programming languages such as C++, Python, or Java: Capable of setting technical standards and guiding development practices.
• Strong record of implementing and advocating for engineering best practices such as code reviews, testing, and continuous integration.
• Experience in leading diverse engineering teams with a focus on collaboration, innovation, and efficiency.
• Excellent strategic thinking skills, with the ability to understand and align team efforts with organizational goals.
• Proven ability to mentor and develop engineering talent, fostering an environment of growth and continuous learning.
• Outstanding communication and interpersonal skills: Effectively engage with stakeholders at all levels, from developers to senior executives.

Bonus Points For

• Ph.D. in Computer Science, Engineering, or a related field
• Experience in leading the development of laboratory automation systems or scientific instrumentation
• Experience with organizational change management and fostering innovation in large technical programs.
• Track record of successful project delivery in agile environments, optimizing team workflows and balancing priorities.
• Published work or presentations that highlight contributions to the field of software engineering and leadership in simulation or automation systems.

Your Impact at LILA

We are seeking a motivated and detail-oriented Associate Engineer to join our high-throughput Research Operations team. In this role, you will focus on materials characterization of materials using spectroscopy and microscopy techniques

You will gain hands-on experience with state-of-the-art automation technologies while contributing directly to projects that drive our company’s growth.

This is an excellent opportunity for candidates excited to learn engineering principles, work with advanced laboratory automation, and develop a strong foundation in scientific operations in a dynamic environment.

What You'll Be Building

• Perform materials characterization techniques including SEMXRF, XRD, Optical Microscopy, Nanoindentation, and Profilometer
• Prepare reagents, solutions/electrolytes , samples, and consumables to ensure seamless continuation of experiments across shifts
• Follow detailed standard operating procedures (SOPs) to ensure accuracy, reproducibility, and throughput
• Monitor workflow performance, troubleshoot common issues, and escalate when necessary to ensure continuity between shifts
• Record, organize, and maintain experimental data with high attention to detail
• Identify opportunities to improve efficiency and streamline processes across shift operations
• Flag unusual features or results for scientist review
• Collaborate with scientists and engineers to support diverse projects across life sciences and physical sciences

What You’ll Need to Succeed

• Bachelor’s degree in Chemistry, Physics, Chemical/Materials Engineering, or a related field (or equivalent laboratory experience)
• Attention to detail and strong organizational skills
• Basic understanding of electron microscopy principles
• Hands-on SEM experience (academic or industry). EDS experience is nice to have.
• Experience with sample preparation techniques: sputter coating

Bonus Points For

• Familiarity with surface characterization techniques methods: XRF, optical microscope, XRF, XRD, nanoindentor, profilometer.
• Experience working in high-throughput production environments with a focus on scalability, efficiency, and accuracy
• Basic engineering skills for troubleshooting characterization tools for microscopes, x rays, and lasers
• Familiarity with automated workflows and techniques to operate and generate custom scripts

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

The Scientist I/II, Process Chemistry will develop and apply modern process chemistry approaches to support Discovery Chemistry efforts across Lila’s platform, with a focus on reaction/reactor engineering, process design, high-throughput experimentation, and digitalized chemistry workflows for small molecules and related molecular matter. This role is intended for a hands-on scientist who combines strong foundations in synthetic and process chemistry with an engineering mindset and experience building practical, scalable, and data-rich experimental workflows.

Working closely with Discovery Chemistry, analytical chemistry, automation, platform engineering, and AI/computational teams, this scientist will design, execute, and optimize reaction and process workflows that improve the speed, robustness, and scalability of chemical transformations. The Scientist I/II will help bridge early discovery chemistry and modern process development by establishing experimentally efficient, digitally enabled, and automation-compatible approaches to reaction screening, process optimization, and chemistry execution.

This role is ideal for a scientist excited by the intersection of chemistry, engineering, high-throughput platforms, process intensification, and digital experimentation, and who wants to help shape next-generation process chemistry capabilities beyond traditional bench development.

What You'll Be Building

• Design, execute, and optimize process-relevant chemical transformations that support Discovery Chemistry programs and the generation of small molecules.
• Develop reaction and process workflows with emphasis on engineering rigor, scalability, reproducibility, throughput, and data quality.
• Apply high-throughput experimentation (HTE) to reaction screening, process optimization, condition scouting, and evaluation of process-relevant variables.
• Build and execute workflows that connect reaction setup, process screening, workup, analytical readout, and data capture into efficient experimental cycles.
• Contribute to the design of digitally enabled process chemistry workflows, including structured experimental data generation, electronic documentation, and integration of chemistry data into computational and AI-ready systems.
• Collaborate with automation and platform teams to develop and adapt reaction and process workflows for robotics-enabled experimentation.
• Work closely with analytical chemistry teams to ensure rapid and informative analytical support for reaction monitoring, impurity assessment, and process decision-making.
• Evaluate reaction performance through both chemistry and engineering lenses, including yield, selectivity, impurity formation, mass balance, robustness, throughput, and operational simplicity.
• Establish best practices for AI-enhanced process design in early discovery settings, balancing speed and molecular access with process understanding and future scalability.
• Stay current with advances in modern process chemistry, reaction engineering, HTE, digitalization, automation, and enabling technologies relevant to next-generation discovery chemistry.

What You’ll Need to Succeed

• PhD in Process Chemistry, Organic Chemistry, Chemical Engineering, Medicinal Chemistry, or a related discipline with relevant postdoctoral and/or industry experience.
• Strong hands-on experience in process chemistry, reaction development, or scale-relevant synthetic chemistry.
• Demonstrated experience applying engineering principles to chemical process design, optimization, and troubleshooting.
• Experience with HTE for reaction or process screening, optimization, and data-rich experimentation.
• Strong understanding of key process variables in discovery chemistry.
• Experience with reaction workup, sample handling, purification, and analytical interpretation in fast-paced chemistry environments.
• Hands-on familiarity with analytical tools commonly used to support process chemistry.
• Experience generating well-structured experimental data to support reproducibility and digital workflows.
• Ability to troubleshoot complex experimental problems and independently drive practical, technically sound solutions.
• Strong collaborative skills and ability to work effectively across chemistry, analytics, automation, engineering, and computational teams.

Bonus Points For

• Experience supporting Discovery Chemistry or small molecule R&D in biotech, pharma, or advanced platform environments.
• Familiarity with modern process chemistry in early discovery settings, including rapid route improvement, robustness assessment, impurity understanding, and scalable reaction design.
• Experience with miniaturized process screening, parallel reactors, or robotics-enabled chemistry platforms.
• Experience contributing to the digitalization of process chemistry, including structured data capture, ELNs, workflow automation, or AI/ML-enabled experimental environments.
• Ability to connect chemistry execution with broader platform goals such as throughput, automation compatibility, and data generation.
• Strong scientific curiosity and enthusiasm for building next-generation process chemistry capabilities that go beyond traditional development paradigms.

Your Impact at LILA

The Senior Scientist, Analytical Chemistry will develop and deploy advanced analytical and characterization strategies to support Discovery Chemistry efforts across Lila’s platform, with a focus on enabling the rapid design, synthesis, screening, and optimization of small molecules and related molecular matter. This scientist will play a key role in generating high-quality, information-rich experimental data that informs chemistry decisions and accelerates platform learning.

Working closely with Discovery Chemistry, automation, AI/computational, and screening teams, this role will establish and scale multimodal characterization workflows that go beyond traditional analytical chemistry approaches. The Senior Scientist will help build an integrated analytical capability spanning compound characterization, reaction monitoring, molecular profiling, and high-throughput data generation to support fast and iterative design–make–test–analyze cycles.

This role will be critical to shaping Lila’s next-generation discovery engine by combining deep expertise in analytical chemistry with high-throughput experimentation, multimodal data generation, AI-enabled workflows.

What You'll Be Building

• Develop, optimize, and implement analytical and multimodal characterization workflows to support Discovery Chemistry programs and molecular discovery campaigns.
• Support the design, synthesis, screening, and optimization of small molecules by delivering rapid, reliable, and information-rich analytical data.
• Lead analytical method development for compound identification, purity assessment, impurity profiling, reaction monitoring, and molecular characterization using techniques such as liquid chromatography, mass spectroscopy, NMR, and complementary orthogonal methods.
• Build characterization strategies that extend beyond traditional analytical methods, incorporating multimodal measurements that provide deeper insight into molecular identity, reaction outcomes, physicochemical properties, and structure–function relationships.
• Partner closely with Discovery Chemistry and screening teams to support high-throughput screening and high-throughput experimentation workflows, including rapid sample analysis, reaction triage, and compound profiling.
• Collaborate with automation and platform teams to integrate analytical instrumentation and data pipelines into robotics-enabled and scalable discovery workflows.
• Work closely with AI and computational teams to ensure analytical datasets are structured, interoperable, and useful for model development, closed-loop learning, and iterative molecule design.
• Establish robust workflows for reaction monitoring, in-process analytics, final compound quality assessment, and characterization of complex mixtures or intermediates.
• Troubleshoot analytical and characterization challenges related to sample preparation, instrumentation, method robustness, data interpretation, and workflow integration.
• Contribute to the development of platform-level capabilities for data-rich molecular discovery, including scalable characterization pipelines, method standardization, and best practices for analytical rigor.

What You’ll Need to Succeed

• PhD in Analytical Chemistry, Organic Chemistry, Pharmaceutical Sciences, Chemical Biology, or a related scientific discipline with 5–8+ years of relevant industry or high-performance research experience.
• Deep expertise in analytical chemistry and molecular characterization in support of chemistry-driven discovery efforts.
• Strong hands-on experience with key analytical techniques for small molecule analysis.
• Demonstrated experience developing analytical methods for compound characterization, reaction monitoring, impurity analysis, and purity assessment.
• Experience supporting Discovery Chemistry, medicinal chemistry, synthetic chemistry, or other molecule-focused R&D environments.
• Strong understanding of how analytical data informs compound progression, screening decisions, reaction optimization, and molecular design.
• Experience working with or supporting high-throughput workflows, including rapid analytical turnaround in fast-paced research settings.
• Ability to solve complex analytical problems independently and work effectively across interdisciplinary teams spanning chemistry, screening, automation, and computational sciences.
• Strong documentation, communication, and data interpretation skills, with a commitment to rigor, reproducibility, and scientific quality.

Bonus Points For

• Experience with multimodal characterization approaches that combine orthogonal measurements to generate deeper insight into molecular systems and discovery workflows.
• Experience supporting high-throughput screening, reaction screening, or automated discovery platforms.
• Familiarity with analytical workflows integrated with robotics, liquid handlers, autosamplers, or other automation-enabled laboratory systems.
• Experience working in AI-enabled or highly data-driven R&D environments where experimental data is used to drive predictive modeling or closed-loop optimization.
• Knowledge of characterization approaches that extend beyond traditional compound QC, including molecular profiling, reaction analytics, and property-focused measurements.
• Familiarity with data systems such as ELNs, LIMS, and software tools used to manage and analyze large analytical datasets.
• Ability to operate effectively in a fast-moving, innovative environment with evolving platform needs and cross-functional priorities.
• Strong scientific curiosity and interest in building next-generation analytical capabilities for molecular discovery.

Your Impact at LILA

Lila Sciences is looking for a strategic, hands-on Director of Facilities to lead the day-to-day and long-term management of multiple facilities. You’ll own a scalable facilities strategy, drive safety and compliance across sites, and oversee budgets, capital planning, vendor governance, and KPI reporting. This role is a cross-functional, cross-site leadership position with real responsibility for building and growing a world-class facilities team that scales with our growth and supports our scientific mission.

What You'll Be Building

• Strategy & Leadership
  • Define and execute a comprehensive facilities strategy across multiple sites that aligns with company growth, science programs, and operational risk posture.
  • Build, mentor, and lead a high-performing facilities organization. Establish clear career paths, succession plans, and a culture rooted in safety, reliability, and continuous improvement.
• People & Team Management
  • Directly manage and develop facility managers, supervisors, and support staff; create a positive, collaborative culture with a strong focus on employee experience and professional growth.
  • Foster cross-functional collaboration with BD, tech, finance, legal, and HR to drive decisions and improvements.
• Safety, Compliance, and Quality
  • Set and uphold enterprise-wide safety standards and facilities compliance across all sites; guide audits and corrective actions.
  • Lead capital planning, lifecycle management, and EOL decisions for critical equipment using ROI-driven analyses.
• Financial Stewardship
  • Oversee multi-site budgets, long-range capital planning, and KPI achievement; optimize total cost of ownership.
  • Own the vendor management lifecycle, including RFPs, contract negotiations, SLAs, and performance reviews.
• Operations & Process Excellence
  • Standardize and optimize maintenance processes with documentation, digital tools, and scalable playbooks; champion improvements with a strong emphasis on the employee and occupant experience.
  • Implement governance, risk management, and compliance controls; ensure audit readiness and transparent reporting.
• Stakeholder & Client Liaison
  • Be the primary touchpoint with clients, senior leadership, Facilities Org, and key business teams; translate facilities strategy into tangible business value.
  • Partner with HR on workforce planning, safety training, and employee engagement initiatives.
• Project Delivery & Portfolio Management
  • Lead major projects (renovations, expansions, high-impact maintenance) from scoping to closeout; manage schedule, budget, and risk.
  • Align cross-site efforts to ensure consistent service levels and performance metrics.
• Performance Measurement & Improvement
  • Define and track key metrics (safety, reliability, lifecycle spend, uptime, cost-to-serve, work-order throughput, client satisfaction) and use data to drive improvements.
  • Nurture a culture of continuous improvement; lead post-implementation reviews and share learnings across sites.

What You’ll Need to Succeed

• Bachelor’s degree in Mechanical, Electrical, Business, or a related field. An advanced degree or professional certifications (e.g., FMP, CFM, CPM) are a plus.
• 8–15+ years of facilities management experience, with a track record of leading multi-site operations in life sciences, physical sciences, or related industries.
• Proven ability to design and implement scalable facilities strategies and to lead through ambiguity and rapid growth.
• Experience managing large budgets, capital plans, and vendor ecosystems; strong ROI and financial acumen.
• Demonstrated track record of cultivating a safety-first culture, robust reporting, and effective cross-functional collaboration at the executive level.
• Strong leadership, communication, and stakeholder management skills; able to influence and mobilize diverse teams.

Bonus Points For

• Direct experience in environments blending physical science and life sciences.
• Familiarity with regulatory standards (OSHA/EHS, environmental compliance) and enterprise facilities software.

Your Impact at LILA

As Lila scales its AI Science Factories globally, the VP of Engineering will create and sustain an engineering model for a unified, scalable platform for autonomous experimentation. This role will be responsible for defining and implementing that platform across hardware, automation, and systems engineering. This executive will own strategy, execution, and talent across Lila’s systems, hardware, and automation engineering. The role sits on the senior leadership team, reports directly to the Chief Autonomous Science Officer, and interfaces directly with Robotics, Software, Operations, and Science teams.

The VP Engineering will bring deep technical experience in automation of lab science, a track record of building and leading multi-disciplinary engineering organizations, mentorship practices that foster career development of engineers at all levels, and the judgment to balance near-term delivery with long-term platform development. This is a hands-on leadership role in a fast-moving environment, requiring brilliance and dynamism to realize autonomous experimentation across materials science, chemistry, and life science. The role requires engineering innovations to achieve an unprecedented depth of AI integration in lab science, which combined with aggressive scaling of Lila’s AI Science Factories (AISFs) will produce the highest degree of scientific intelligence on earth.

What You'll Be Building

• Set the engineering roadmap for ASP Engineering, including objectives, risks, and platform priorities via collaboration with the Robotics, Software, Science, and Product teams.
• Define and drive a unified platform architecture across hardware, automation, and systems engineering, reducing one-off designs and enabling repeatable system deployment across sites
• Establish system-level ownership of architecture, interfaces, and requirements to ensure consistent integration across all engineering domains
• Implement efficient processes for lifecycle, design controls, safety and compliance, change management, and deployment of autonomous laboratories to support rapid scaling without compromising system consistency
• Develop a repeatable deployment model for standing up global AI Science Factory sites with predictable performance and reliability
• Create a culture of mentorship that supports execution of the roadmap while fostering career development of engineers
• Partner with Robotics and Software to build, industrialize, and scale all-of-science autonomous labs with a focus on reliability, throughput, and quality
• Integrate AI into the engineering lifecycle to bolster engineering speed and quality, decision making, and continuous improvement
• Collaborate with Talent Acquisition to establish hiring practices that accelerate identification and onboarding of brilliant, culturally-aligned engineers
• Partner with AI Research, Software, Product, and Science to align platform capabilities with scientific and commercial priorities
• Develop and communicate clear prioritization frameworks across engineering initiatives; translate organizational feedback into actionable systems and process improvements
• Represent Engineering with executive stakeholders and external partners; communicate tradeoffs, progress, and impact clearly
• Partnering with external integrators, OEMs, and contractors to expand engineering capacity

What You’ll Need to Succeed

• 15+ years building lab automation systems
• 10+ years leading managers and multi-disciplinary teams at scale
• Proven track record scaling automated labs or manufacturing-like lab operations, including instrument and robot integration, scheduling and orchestration, reliability engineering, and capacity planning
• Demonstrated experience integrating AI/ML into engineering, operations, and/or research environments
• Experience building and standardizing complex engineering systems from early-stage, project-based environments into scalable, repeatable platforms
• Demonstrated ability to balance rapid experimentation with the introduction of structure required for scale
• Technical expertise across lab automation, controls/SCADA, and science instrumentation, including integration and co-development of robotics and software/data/compute platforms
• Strong people and org leadership: recruiting and developing exceptional managers and ICs; building inclusive, high-ownership cultures that scale
• Excellence in retaining cultural and technical excellence across multiple sites of lab automation innovation and build-outs

Bonus Points For

• Multi-domain engineering excellence (mechanical, controls, electrical, systems) and cross-discipline engineering fluency (robotics, software, computer)
• Experience defining interface standards across hardware, robotics, and software systems
• Experience standing up lifecycle management (e.g., PLM-lite, change control, commissioning frameworks) in fast-moving environments
• Multi-domain science fluency (materials science, chemistry, life science)
• 10+ years experience integrating LIMS, orchestration, schedulers, and reliability and observability pipelines in lab automation
• Experience managing stage-gate transitions from R&D to scaled operations across multiple sites

Your Impact at LILA

We're 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. The initial focus of this team will be to build and implement a bespoke safety strategy for Lila, tailored to its specific goals and deployment strategies. This will involve technical safety strategy development, broader ecosystem engagement, safety-focused evaluations, safety systems to mitigate risks, and a safety research agenda that explores longer-term needs such as oversight of superintelligent scientific systems.

We’re seeking a Technical Mitigations Lead, to lead the build out of safety systems at Lila for the safe deployment of our scientific capabilities to the world. Given the novelty of Lila’s workflows, integrating frontier-class language models with narrow scientific tools and lab-based automation, this role will require the design and deployment of technical safeguards beyond the current state-of-the-art.

We expect the person in this role to start off the initial mitigations build-out, and then slowly build a team to support this function.

What You'll Be Building

• Set the build and research strategy for Lila’s safety systems, across scientific data analysis and generation pipelines, safety post-training, refusal classifiers, automated safety-testing / red-teaming systems, and monitoring systems.
• Conduct initial safeguards experimentation and buildout for Lila’s specific scientific needs, and subsequently lead a small team to execute on the build and research agenda
• Lead safety systems research to iterate Lila’s systems beyond the state of the art, given the needs of technical safeguards for both in silico and lab-based scientific workflows.
• Partner closely with
  • Other members of the safety team, such as domain-specific experts (bio, chem, materials) and eval buildout teams, and
  • Non-safety teams, such as core AI, lab automation, and product teams,
• Contributing to broader, high-quality research efforts - as and when needed - for scientific capability evaluation and restriction.
• Contribute to external communications on Lila’s safety efforts.

What You’ll Need to Succeed

• Track record of building safety systems, classifiers, or conducting post-training for frontier-class problems - science, reasoning, programming, etc.
• 4-6+ years working in technically engineering with ML systems.
• Experience building scalable, production systems, not just prototypes.
• Demonstrated ability to set research directions for open problems in post-training, classifier buildouts, and other relevant systems.
• Ability to communicate complex technical concepts and concerns to non-expert audiences effectively.

Bonus Points For

• Experience in developing or applying ML to biological or physical sciences
• Experience in building safeguards for scientific risks for frontier models / narrow scientific tools.
• Demonstrated ability to lead teams towards engineering goals

Your Impact at LILA

Lila Sciences is using its autonomous science platform to accelerate chemical discovery — from smarter synthesis pathways and next-generation catalysts to molecular design for clean energy and industrial applications. The Chemistry experiment team is running programs at a pace and scale that demands serious operational rigor, and we're looking for a Technical Program Manager to provide it. You'll work closely with the Chemistry domain lead who sets scientific strategy and direction, while you own the execution layer: the project plans, the cross-functional dependencies, and the blockers that need to be surfaced and resolved before they become problems. You won't be directing scientific work — you'll be creating the conditions for it to happen at speed and at scale. This is a role for someone who genuinely loves the craft of program management in complex, technical environments.

What You'll Be Building

• Own day-to-day program execution within the Chemistry experiment team — maintaining project plans, tracking milestones, and ensuring agentic science programs are delivered on time and with rigor
• Serve as the primary operational interface between the Chemistry team and platform partners (Automation, ML, Engineering) — communicating needs, aligning on priorities, and keeping dependencies visible and resolved
• Translate scientific program goals into structured workstreams that enable autonomous experimentation to run efficiently and at scale
• Run program rituals including sprint planning, stand-ups, and retrospectives with a bias toward purposeful meetings, documentation, and follow-through
• Partner with the Chemistry domain leads to surface blockers, risks, and interdependencies early and drive them to resolution
• Support the delivery of commercial contracts by working with the Product & Revenue team to ensure program commitments are well-scoped, tracked, and clearly communicated to all stakeholders
• Contribute to capacity planning and resource coordination in partnership with scientific leadership

What You’ll Need to Succeed

• 10+ years of program or project management experience in a chemistry, pharmaceutical, or deep tech environment
• A Bachelor's degree in Chemistry, Chemical Engineering, or a related field
• A track record of managing multiple concurrent programs in fast-paced, ambiguous environments and energized by building programs from the ground up; early-stage start-up experience is a strong plus
• Demonstrated ability to coordinate across scientific and operational stakeholders and manage complex cross-functional dependencies
• Sufficient scientific fluency to understand Chemistry program goals and communicate them clearly to partner teams
• Experience supporting commercial program delivery and working with external partners or customers
• Exceptional written and verbal communication skills — able to break complex goals into clear, achievable units of work and keep stakeholders consistently well-informed

Bonus Points For

• Familiarity with project management tools such as Smartsheet, Asana, Jira, or similar
• PMP, PgMP, or agile certification (e.g., CSM, SAFe) is a plus

Your Impact at LILA

Lila Sciences is applying its autonomous science platform to some of the hardest materials discovery problems in the world — from next-generation structural materials and extreme-environment coatings to clean energy catalysts and carbon capture. The Materials Science experiment team is running programs where the pace of autonomous experimentation demands real operational rigor, and we're looking for a Technical Program Manager to provide it. You'll work closely with the Materials Science domain leads who set scientific strategy and direction, while you own the execution layer: the project plans, the cross-functional dependencies, and the blockers that need to be surfaced and resolved before they become problems. You won't be directing scientific work — you'll be creating the conditions for it to happen at speed and at scale. This is a role for someone who genuinely loves the craft of program management in complex, technical environments.

What You'll Be Building

• Own day-to-day program execution within the Materials Science experiment team — maintaining project plans, tracking milestones, and ensuring agentic science programs are delivered on time and with rigor
• Serve as the primary operational interface between the Materials Science team and platform partners (Automation, ML, Engineering) — communicating needs, aligning on priorities, and keeping dependencies visible and resolved
• Translate scientific program goals into structured workstreams that enable autonomous experimentation to run efficiently and at scale
• Run program rituals including sprint planning, stand-ups, and retrospectives with a bias toward purposeful meetings, documentation, and follow-through
• Partner with the Materials Science domain leads to surface blockers, risks, and interdependencies early and drive them to resolution
• Support the delivery of commercial contracts by working with the Product & Revenue team to ensure program commitments are well-scoped, tracked, and clearly communicated to all stakeholders
• Contribute to capacity planning and resource coordination in partnership with scientific leadership

What You’ll Need to Succeed

• 10+ years of program or project management experience in a materials science, advanced manufacturing, or deep tech environment
• A Bachelor's degree in Materials Science, Chemistry, Chemical Engineering, or a related field
• A track record of managing multiple concurrent programs in fast-paced, ambiguous environments and energized by building programs from the ground up; early-stage start-up experience is a strong plus
• Demonstrated ability to coordinate across scientific and operational stakeholders and manage complex cross-functional dependencies
• Sufficient scientific fluency to understand Materials Science program goals and communicate them clearly to partner teams
• Experience supporting commercial program delivery and working with external partners or customers
• Exceptional written and verbal communication skills — able to break complex goals into clear, achievable units of work and keep stakeholders consistently well-informed

Bonus Points For

• Familiarity with project management tools such as Smartsheet, Asana, Jira, or similar
• PMP, PgMP, or agile certification (e.g., CSM, SAFe) is a plus