Your Impact at LILA

Lila Sciences is seeking Platform Scientists, Soft Materials Focus to ideate, prototype, and transfer new experimental concepts for AI Science Factories targeting complex and soft material systems. In this role, you will work at the forefront of scientific ideation—partnering with a broad range of chemistry, materials, and AI teams to define new approaches to formulation, structure–property relationships, and performance evaluation. You will translate these concepts into scalable experimental workflows, custom apparatus, and software-enabled systems for autonomous research.

What You'll Be Building

• Collaborate with diverse scientific teams to ideate new experimental strategies for soft and complex materials, including formulations, colloids, polymers, gels, and biological or hybrid systems
• Lead conceptual development of characterization and performance workflows leveraging techniques such as SAXS, SANS, rheology, and formulation performance assays
• Design, prototype, and validate custom apparatus, experimental methods, and software tools to realize these concepts
• Develop and refine workflows that connect structure, dynamics, and performance across multiple length and time scales
• Transfer successful prototypes into production-ready systems within Lila’s AI Science Factory platform, enabling autonomous experimentation

What You’ll Need to Succeed

• MS or PhD in Materials Science, Chemistry, Chemical Engineering, Physics, or a related field, with a focus on soft or complex materials
• Demonstrated experience collaborating across scientific disciplines to generate and refine experimental concepts
• Hands-on experience with one or more of the following: SAXS, SANS, rheology, formulation performance or functional assays
• Experience designing and implementing custom experimental workflows and/or scientific software
• Strong programming skills (e.g., Python, C++, or similar) applied to data analysis, automation, or experiment control
• Excellent communication skills and comfort operating in a fast-paced, exploratory R&D environment

Bonus Points For

• Experience with lab automation, high-throughput experimentation, or instrument control software
• Background in formulation science (e.g., consumer products, coatings, energy materials, bio-soft matter)
• Experience linking scattering, rheological, and performance data to predictive or ML-driven models
• Familiarity with prototyping tools such as 3D printing, machining, microfluidics, or custom sample environments
• Industry or national lab experience working on soft materials or complex fluids

Your Impact at LILA

As a Machine Learning Scientist focused on Scientific Reasoning, you will help pioneer the next generation of AI systems capable of reasoning like a scientist. You’ll design novel frameworks that push the boundaries of LLM-based reasoning methods — while also implementing scalable frameworks that integrate with Lila’s platforms. This role bridges deep theoretical thinking with practical ML engineering, enabling breakthroughs in how scientific hypotheses are generated, tested, deployed and optimized.

What You'll Be Building

• Design and formalize frameworks for scientific reasoning with LLMs, including structured prompting, reasoning chains, and test-time compute.
• Explore and implement methods for in-context learning, self-reflection, and adaptive reasoning in scientific discovery workflows.
• Build scalable model prototypes that can be deployed to solve frontier scientific problems.
• Collaborate with scientists and engineers to encode domain knowledge into reasoning systems that integrate symbolic and statistical approaches.

What You’ll Need to Succeed

• PhD (preferred) or equivalent research/industry experience in Computer Science, Machine Learning, AI, Engineering, Materials Science or related fields.
• Strong programming skills in Python with deep expertise in LLM frameworks (PyTorch, HuggingFace Transformers, LangChain, LlamaIndex, and related toolkits).
• Expertise in LLM reasoning methods: in-context learning, test-time compute, chain-of-thought, or tool-augmented reasoning.
• Ability to balance theoretical research with practical ML engineering to deliver scalable solutions.

Bonus Points For

• Research experience in causal reasoning, symbolic AI, or probabilistic programming.
• Contributions to open-source LLM reasoning frameworks.
• Familiarity with scientific discovery pipelines in chemistry, biology, or materials science.
• Experience with multimodal reasoning (e.g., combining text, image, and experimental data).
• Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, ACL).

Your Impact at LILA

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

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

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

Lila Sciences is seeking a detail-oriented and process-driven HR Generalist (Contract) to join our HR Operations team. This role will serve as the execution backbone of our HR Operations function. The HR Generalist will handle the day-to-day administration of the employee lifecycle, systems management, benefits support, and cross-functional HR tasks. This is a high-execution role best suited for someone who takes pride in doing administrative work accurately and efficiently, thrives on process ownership, and is energized by bringing order and consistency to a growing organization.

What You'll Be Building

Employee Lifecycle, Onboarding & Offboarding

• Execute end-to-end pre-boarding and onboarding processes, including coordination of IT intake, systems access setup, and employee introduction materials
• Identify and implement improvements to pre-boarding workflows to increase consistency and reduce manual effort
• Support offboarding administration, including exit paperwork and required system updates
• Process administrative HR actions across the employee lifecycle: new hires, transfers, internal role changes, manager changes, and terminations
• Generate employment letters (transfer, termination, etc.) and manage signature request workflows
• Maintain and organize employee documentation in accordance with compliance requirements

Immigration & Benefits Administration

• Manage immigration paperwork and coordinate with external counsel or vendors on active cases under HR Operations Manager guidance
• Administer benefits tasks that cannot be automated, including enrollment support and data entry
• Provide leave administration support as needed, in coordination with HRBPs

HR Inbox & Employee Support

• Manage the HR team inbox: triage, respond to, and route inquiries related to onboarding/offboarding, immigration, benefits, and company policies
• Serve as a reliable first point of contact for employee and manager HR questions, ensuring timely and accurate responses, and escalating complex cases appropriately

Process & Compliance

• Own the I-9 compliance process end-to-end: complete Section 2 verification for all new hires, manage re-verification for expiring work authorization documents, maintain organized and audit-ready I-9 records, and coordinate E-Verify submissions where applicable
• Collaborate with HR Operations to document, standardize, and improve HR workflows and procedures

What You’ll Need to Succeed

• 1–3+ years of HR experience with a focus on HR operations, administration, or HR coordination
• Hands-on experience with HRIS platforms; HiBob and/or ADP experience is a plus
• Strong attention to detail and a track record of accurate, high-volume administrative work
• Comfortable managing multiple competing priorities with minimal supervision
• Clear and professional communicator (written and verbal)
• Ability to handle sensitive, confidential HR information with discretion
• Demonstrated commitment to process improvement and operational consistency
• Local to the Cambridge, MA area; able to work hybrid (averaging ~3 days/week onsite based on need)

Bonus Points For

• Experience with ATS platforms (e.g., Greenhouse)
• Direct experience administering the I-9 process
• Experience with process documentation and process flow documents
• Familiarity with immigration processes and vendor coordination
• Experience supporting benefits administration or leave management
• Comfort with ambiguity in a fast-paced, evolving environment

Overview of the role:

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

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

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

Location:

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

Responsibilities:

Deep Domain Ownership:

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

Full-Stack Product Execution:

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

The intelligence platform for Science:

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

Cross-Functional Orchestration:

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

Qualifications:

Core Product Experience:

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

The Full-Stack Mindset:

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

Bias for Action in Complexity:

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

Operational Rigor:

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

Preferred Skills:

Scientific Background:

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

Building for Scientists:

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

Startup DNA:

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

Your impact at LILA

We’re building a talent-dense, high-agency research team to develop the next generation of learning systems and reasoning algorithms for agentic LLMs. Our work sits at the intersection of large language models, post-training, and scientific reasoning, with the goal of enabling systems that learn from experience, reason effectively, and improve through interaction.

Scientific domains present a distinct set of challenges that make this problem uniquely hard. Feedback is sparse and delayed — experiments take days or weeks, not milliseconds. Ground truth is expensive or contested. Distribution shift is structural, as instruments, techniques, and knowledge bases evolve continuously. The hypothesis space is vast and reward signal is thin. Existing benchmark do not capture these nuances. The goal is to build systems that can operate effectively in this scientific regime.

This role spans a few complementary directions. Candidates are expected to bring deep expertise in one (ore more) of the following areas. In the event of cross-track expertise, please select the one you align to the most. Our interview process will be catered to verifying the chosen expertise area.

Expertise Area 1 — Agentic system building

Focus: Build systems that autonomously propose, execute, and verify scientific hypotheses over long time horizons.

• Create and analyze long-running auto-research systems that propose and verify hypotheses
• Design planning frameworks for agentic systems operating over long, sparse feedback loops
• Design memory architectures that allow agents to build and retrieve structured knowledge over time
• Explore algorithms in recursive self-improvement, multi-agent coordination, and continual learning

Expertise Area 2: Distillation

Focus: Translate strong inference-time behaviors and reasoning traces into efficient, trainable models.

• Develop distillation strategies from large or ensemble models into deployable systems
• Research methods for self-improvement, including iterative self-distillation and critique loops
• Investigate how to preserve generalization and reduce catastrophic forgetting through the distillation process

Expertise Area 3 — Scalable experience generation

Focus: Develop inference-time algorithms and synthetic data pipelines that generate high-quality training signal for scientific reasoning.

• Design and benchmark inference-time search, sampling, and verification strategies
• Propose new techniques in synthetic environment creation and curriculum learning
• Develop synthetic data generation strategies that capture high-quality scientific reasoning for agentic model training
• Measure the end-to-end impact of inference-time improvements on real scientific tasks

What you’ll need to succeed:

• An advanced degree in computer science, machine learning, or a related field, or or comparable experience
• Strong foundation in LLMs and empirical research
• Experience designing and executing rigorous ML experiments, including benchmarking and ablations
• Experience working with large-scale training or evaluation pipelines
• Ability to define and pursue research directions in open-ended, rapidly evolving spaces
• Strong collaboration and communication skills across research and engineering teams

Bonus points for:

• Experience with synthetic data generation, distillation, or self-improvement loops
• Familiarity with reinforcement learning (e.g., RLHF, on-policy methods)
• Experience with planning, search, or decision-making systems at scale
• Experience in building agentic systems with tool use, or multi-agent workflows
• Background in program synthesis, coding benchmarks, or long-horizon tasks
• Experience building evaluation frameworks or large-scale benchmarks

Scientific rigor & persistence:

• You take a principled approach to experimentation, with careful baselines, ablations, and evaluation design
• You are motivated by understanding why systems work, not just improving metrics
• You prioritize clarity, reproducibility, and intellectual honesty in research
• You are comfortable working through long, nonlinear iteration cycles
• You operate effectively in ambiguous, fast-evolving research environments

Your Impact at LILA

The AI Research team is tackling one of the most exciting, open problems in AI: training LLMs to run long-horizon scientific discovery tasks. Our approach spans the full post-training stack - from SFT to asynchronous RL on agentic harnesses - teaching models to plan, use tools, and learn from experience in domains where the ground truth isn't a preference label, but a scientific result.

We're rapidly growing our Research Engineering org and seeking talented engineers and ML practitioners across levels to design, build, and optimize systems to push this frontier: scaling post-training, sharpening reasoning, and unlocking compute-intensive agentic-harness training. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems.

We operate with high agency, and a bias toward execution. Below are several focus areas within the team. We ask that candidates select the stream that best matches their experience and excitement.

Work Streams

Stream A: GPU Optimization & Training Performance

Maximize hardware utilization across 100B+ parameter asynchronous RL training runs. Responsibilities include profiling, performance optimization, custom kernel development, communication-computation overlap, and long-context throughput improvements. You set and maintain the performance baseline.

Stream B: Stack & Infrastructure

Own the post-training infrastructure end-to-end — supervised fine-tuning, asynchronous RL with tool integration, and data pipelines. Build modular, reproducible workflows with single-command execution. Manage upstream framework upgrades and deliver composable pipelines spanning Data, SFT, and RL stages. You work tightly with Research Scientists to develop and productionize novel algorithms to run at scale.

Stream C: Model Experimentation

Bring deep, hands-on experience training large language models. Lead experimentation on reasoning model development, including mixture-of-experts stabilization, curriculum design, and synthetic reasoning trace generation. You have a bias toward experimental design and tracking, and know how to prioritize runs that yield promising outcomes.

Stream D: Evaluations & Benchmarks

Design and build best-in-class scientific agentic benchmarks and harnesses, along with the dashboards and leaderboards that inform every training decision. You have experience working with well known public benchmarks and have spent time building bespoke agentic benchmarks and harnesses.

Stream E: Agentic Capabilities & Frontier Research

Train models capable of planning, exploration, and tool use over extended horizons. Advance the state of the art in RL at scale with tool-calling, subgoal decomposition, and shared memory/skills across trials to expand the frontier of scientific agent capabilities.

What You'll Need to Succeed

• Strong software engineering skills in Python; C++/CUDA a plus
• Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray)
• Understanding of large-scale model training techniques for 100B+ models
• Experience with cloud or HPC environment
• Ability to communicate technical results to internal and external stakeholders

Bonus Points For

• Prior work with large scale scientific datasets or domain-specific modeling
• Contributions to open-source ML frameworks
• Experience with RL post-training (RLHF, GRPO, tool-augmented RL)
• Experience training MoE architectures

Location

San Francisco, CA or Cambridge, MA (Remote, Hybrid, and On-Site available depending on team needs).

Your Impact at LILA

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

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

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

Your Impact at LILA

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

Lila is seeking a dedicated and skilled Research Associate, Protein Sciences to join our high through-put Cell-Free Expression platform. As a Research Associate, you will play a vital role in our interdisciplinary research efforts focused on building and automating artificial intelligence-driven processes across a variety of valuable applications.

What You'll Be Building

• Development and refinement of cell-free expression systems for efficient protein synthesis.
• Optimize conditions for high-yield and high-quality protein production
• Prepare media and solutions for cell culture and processing
• Perform molecular cloning, mutagenesis, and other genetic manipulation for strain development.
• Demonstrate proficiency in operating and utilizing liquid handlers and robotic systems for automated laboratory processes.
• Collaborate with biologists, machine learning scientists, software developers, and other engineers to develop and optimize AI-driven processes.
• Track and maintain excellent data records in formats amenable to computational workflows.
• Analyze and interpret experimental data, prepare technical reports, and present findings to internal teams and stakeholders.

What You’ll Need to Succeed

• A bachelor's or master's degree in biochemistry, molecular biology, biotechnology, biological engineering, chemical engineering, or a related field.
• 1-3 years of laboratory experience
• Experience with cell culture techniques, including growth, maintenance, and transformation, across a variety of cell lines
• Familiarity with plate-based assays
• Excellent attention to detail, organization, and communication

Bonus Points For

• Skilled in cloning, expression and optimization of protein
• Data analysis using relevant software (Python, R, or similar)
• Scripting for automation platforms

Your Impact at LILA

As a Robotics Scientist at Lila, you will lead the research and development of autonomous robotic systems that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You’ll develop novel algorithms and deploy intelligent robotic solutions that interact seamlessly with human scientists and complex lab environments. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering.

What You'll Be Building

• Design and develop autonomous robotic systems for transport and workcell operations, integrating advanced path planning, navigation, and motion planning algorithms
• Build production-grade mobile manipulation platforms leveraging ROS/ROS2 and modular robotic architectures
• Advance robotic perception by integrating sensing modalities such as 3D vision, LIDAR, and tactile sensors to enable robust, adaptable task execution
• Use simulation environments to model, test, and optimize task planning, scheduling, and robot behaviors in diverse lab scenarios
• Collaborate with AI, mechanical, and software engineering teams to translate theoretical robotics research into real-world autonomous systems

What You’ll Need to Succeed

• Ph.D. in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field, or equivalent research experience
• Expertise in motion planning, path planning, and navigation for manipulation and mobile robotics
• Proficiency with ROS/ROS2, C++, and Python, with hands-on experience building robotic systems in real-world environments
• Deep understanding of perception systems and sensor integration (e.g., camera, LIDAR, tactile sensors)
• Proven ability to take robotic systems from concept through to deployment

Bonus Points For

• Experience with dual-arm mobile manipulation systems or other high-DOF robotic platforms
• Familiarity with simulation tools such as Gazebo, Isaac Sim, or PyBullet
• Background in machine learning for perception, control, or adaptive planning
• Experience with real-time decision-making in human-robot collaboration scenarios
• Strong publication or patent record in robotics, autonomy, or perception

Your Impact at LILA

As a Robotics Scientist at Lila, you will lead the research and development of autonomous robotic systems that serve as the intelligent physical infrastructure of our scientific superintelligence platform. You’ll develop novel algorithms and deploy intelligent robotic solutions that interact seamlessly with human scientists and complex lab environments. Your work will accelerate our mission by enabling fully autonomous workflows for scientific discovery, combining cutting-edge robotics, machine learning, and systems engineering.

What You'll Be Building

• Pioneering approaches for precise and dexterous robotic manipulation that leverage foundation models, reinforcement learning, diffusion-based methods, and human guidance to enable adaptive and intelligent robotic systems capable of complex tasks across diverse scientific environments
• Developing novel human-robot interaction frameworks that incorporate imitation learning, and learning from human guidance, feedback, demonstrations and corrections, creating intelligent robotic agents that can seamlessly integrate with human scientific workflows and rapidly adapt to new experimental contexts
• Advancing dexterous manipulation research through cutting-edge machine learning approaches, including diffusion models and adaptive learning algorithms, that synthesize multi-modal sensing (tactile, visual, and language) to develop generative skill representation sand sophisticated motor learning policies for intelligent robotic systems
• Designing autonomous robotic systems with trust calibration mechanisms, enabling intelligent agents that can dynamically adjust their behaviors based on contextual information in complex scientific tasks

What You’ll Need to Succeed

• Ph.D. in Robotics, Machine Learning, Computer Science, or a related field with demonstrated expertise in foundation models for robotic learning
• Advanced proficiency in reinforcement learning, diffusion-based methods, imitation learning, and adaptive learning algorithms for robotic manipulation
• Expert-level experience with machine learning frameworks (PyTorch, TensorFlow) and deep learning architectures for developing foundation models, with specific expertise in diffusion-based generative models for robotics
• Proven track record of developing multi-modal perception systems integrating tactile, visual, language and other contextual sensing for intelligent robotic agents
• Strong publication record in robot learning, demonstrating innovative approaches to trust calibration, contextual learning, and generative robotic skill learning

Bonus Points For

• Research contributions to foundation models and diffusion methods in robotics
• Experience with large-scale machine learning model development, particularly generative and diffusion-based approaches
• Expertise in human-in-the-loop learning, correction-based training paradigms, and diffusion-guided skill transfer
• Demonstrated ability to translate theoretical machine learning research, especially diffusion and generative models, into practical robotic implementations

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 looking for a Director of Product, Life Sciences strategic and visionary product leader who will shape the future of our therapeutic research and development capabilities. Reporting to the Vice President, Life Science Product, you will lead the strategy, roadmap, and execution for products and platforms at the intersection of drug discovery, chemical synthesis, and scientific intelligence.

What You'll Be Building

• Drive the strategic direction for turning our AI capabilities toward breakthrough solutions in drug discovery, chemical synthesis, and molecular characterization
• Translate strategic objectives into target product profiles (TPPs) and development campaigns that guide science and engineering teams and enable breakthrough innovations in life sciences
• Lead customer and internal-user discovery, serving as the technical link to external customers and partners in the life sciences
• Identify and shape product opportunities grounded in real-world discovery challenges, partnering closely with collaborators, customers, and strategic partners to ensure relevance and downstream value
• Translate insights into clear product requirements, acceptance criteria, and experiment plans that guide engineering and science teams
• Drive product execution from concept to launch while leading cross-functional teams across science, machine learning, automation, and software engineering
• Manage resource allocations (budget, compute, lab access, people) and decisions against a complex set of priorities (e.g., focus of experiments between AI model training and near-term partnership requests)
• Define clear success metrics and milestones, and clearly communicate progress against them to the company, including executive leadership

What You’ll Need to Succeed

• MS, PhD, or equivalent experience in medicinal chemistry, chemical biology, chemical engineering, biochemistry, or a related field, with a strong grounding in experimental science
• 7+ years in Product Management building new and/or experimental products in drug discovery, molecular profiling, machine learning, or scientific software development
• Deep understanding of drug development processes, molecular and cellular research tools, and wet and dry lab workflows
• Experience with small molecule discovery workflows (e.g., lead optimization, ADMET profiling, synthesis planning) is strongly preferred
• Experience defining and executing target product profiles (TPPs) for new drug products, life sciences research tools, or scientific data platforms
• Experience with managing extremely complex, multidisciplinary projects using project management best practices
• Relentlessly output-focused; driven by impact against our mission and program goals, not by doing tasks, owning decisions, or being in meetings
• Proven ability to influence without authority and lead cross-functional teams effectively
• Highly collaborative and empathetic
• High degree of comfort with ambiguity and able to adapt to changing teams and needs
• Able to create small, achievable units of work from large, complex, and ambiguous goals
• Excellent communication and presentation skills, capable of conveying technical information to any audience

Bonus Points For

• Experience with automated/self-driving labs and experience working with (not necessarily having run) ML approaches relevant to drug discovery and molecular science
• Following the rapid evolution of LLMs and other AI solutions and have thoughts and opinions on how this will impact the future of scientific discovery and product development
• Background in partnering with biopharma or research organizations to define collaborative milestones and workflows

Your Impact at LILA

The Senior Director, Small Molecule Therapeutics will lead the biological evaluation and screening of small molecule therapeutics across Lila’s discovery platform. This leader will build and scale a world-class screening organization that translates AI-designed and robotically synthesized molecules into validated biological insights and therapeutic opportunities.

Working closely with Lila’s chemistry, automation, and AI/computational teams, this role will establish the biological screening infrastructure required to rapidly evaluate novel chemical matter. The Senior Director will design and deploy high-throughput and high-content screening strategies, build automated assay platforms, and develop innovative approaches to interrogate large chemical spaces.

This leader will play a critical role in shaping Lila’s small molecule discovery engine by integrating biology, automation, and data science into scalable discovery workflows that accelerate the identification of high-quality therapeutic candidates.

What You'll Be Building

• Build and lead Lila’s small molecule biology and screening organization, including hiring, mentoring, and developing a high-performing scientific team.
• Design and implement high-throughput and automated screening platforms to evaluate small molecules generated through Lila’s AI-driven chemistry and robotic synthesis systems.
• Develop and optimize biochemical, biophysical, and cell-based assays to identify and validate novel small molecule hits.
• Establish scalable automation-enabled workflows for assay execution, screening campaigns, and data generation.
• Lead the development and implementation of DNA-encoded library (DEL) screening strategies and downstream validation pipelines.
• Partner closely with chemistry, computational, and automation teams to integrate screening results with molecular design and synthesis efforts.
• Define and execute screening strategies for new therapeutic targets, including target validation, assay design, and hit identification.
• Oversee large-scale screening campaigns, including hit triage, orthogonal validation, and early mechanism-of-action studies.
• Implement data-driven approaches to analyze screening outputs and guide iterative molecule design.
• Establish best practices for screening data quality, reproducibility, and assay robustness.
• Collaborate across disciplines to translate screening hits into validated chemical starting points for therapeutic programs.

What You’ll Need to Succeed

• PhD in Molecular Biology, Chemical Biology, Pharmacology, Biochemistry, or a related scientific discipline with 12–15 years of experience in drug discovery, or MS/BS in a relevant scientific field with 15+ years of industry experience in small molecule drug discovery and screening.
• Demonstrated experience building and leading small molecule screening teams or platforms, including hiring, mentoring, and developing multidisciplinary scientists.
• Proven track record designing and executing high-throughput screening (HTS) campaigns for small molecule discovery.
• Deep expertise in biological assay development, including biochemical, biophysical, and cell-based assays used in early-stage drug discovery.
• Experience establishing automated or robotics-enabled laboratory platforms, including integration of liquid handling, assay automation, and scalable experimental workflows.
• Experience with DNA-encoded library (DEL) screening or other large-scale chemical library interrogation approaches.
• Strong understanding of hit identification, triage, and validation workflows in small molecule discovery programs.
• Experience working closely with medicinal chemistry teams to translate screening hits into validated chemical matter.
• Demonstrated ability to operate effectively in highly interdisciplinary environments spanning chemistry, biology, automation, and computational teams.
• Track record of scientific leadership and program delivery, including advancing discovery programs and mentoring scientific teams.

Bonus Points For

• Familiarity working in AI-enabled drug discovery environments or integrating machine learning insights into experimental workflows.
• Experience scaling automated discovery platforms that integrate robotics, high-throughput screening, and computational workflows.
• Deep familiarity with large-scale screening technologies, including DNA-encoded libraries (DEL), fragment screening, or other modern small molecule discovery approaches.
• Experience implementing high-content screening, phenotypic assays, or complex cellular screening systems.
• Strong understanding of screening data analysis and interpretation, including management of large experimental datasets.
• Experience building screening infrastructure from the ground up, including instrumentation selection, workflow design, and laboratory automation.
• Demonstrated ability to lead in fast-moving, innovative biotech environments.
• Strong communication skills and the ability to align multidisciplinary teams around ambitious scientific goals.

Your Impact at LILA

As a ML Research Scientist - Multimodal Data Extraction, you will advance Lila’s vision of scientific superintelligence by developing foundation models that autonomously read, interpret, and structure scientific knowledge across text, images, and experimental data in the physical sciences. Your research will help unify the world’s scientific information into machine-understandable form, powering reasoning, prediction, and autonomous discovery across materials science and chemistry.

What You'll Be Building

• Research and develop AI systems that extract and structure knowledge from diverse scientific sources.
• Design and fine-tune large language, multi-modal and specialized models for factual, interpretable data extraction.
• Build scalable pipelines for unstructured and heterogeneous scientific data, integrating text, tables, and visuals.
• Collaborate with domain experts to align extracted data with real-world discovery workflows.
• Publish research that advances the state of the art in multimodal understanding and AI-driven knowledge extraction.

What You’ll Need to Succeed

• PhD (or equivalent research experience) in Computer Science, Chemistry, Materials Science, or related field.
• Expertise in machine learning, NLP, and vision–language modeling using PyTorch and Hugging Face Transformers.
• Proven ability to train, fine-tune, and evaluate LLMs and multimodal models for scientific data extraction.
• Strong understanding of data structures and representations used in the physical sciences.
• Demonstrated research impact through publications, preprints, or open-source work (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, Scientific Journals).

Bonus Points For

• Experience with multimodal fusion architectures and document-level understanding.
• Knowledge of scientific document parsing (OCR, table extraction, figure-caption linking).
• Familiarity with knowledge graph construction or reasoning systems for science.
• Experience with noisy or heterogeneous real-world scientific data.
• Collaborative mindset and passion for advancing AI in the physical sciences.

Your Impact at Lila

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

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

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

Your Impact at LILA

As a Principal Security Engineer focused on AI Security, you will define and drive the technical strategy for securing how AI is used across Lila's enterprise. You will operate as a senior individual contributor, partnering with IT and business teams to ensure safe and compliant adoption of AI tools and platforms.

While Lila builds AI-powered systems, this role is primarily focused on securing the use of third-party and internally deployed AI tools across the enterprise — ensuring sensitive data, intellectual property, and scientific workflows are protected as AI becomes deeply embedded in how work gets done.

What You'll Be Building

• Enterprise AI Security Strategy — Define and implement security controls and guardrails for the use of AI tools (e.g., LLM APIs, SaaS AI platforms, and internal AI services) across the organization.
• AI Gateway & Agentic Gateway Security — Design and implement AI gateway controls to manage and monitor access to external and internal AI systems. Secure agentic workflows by enforcing identity, authorization, tool-use constraints, and policy controls for autonomous or semi-autonomous agents.
• AI Red Teaming & Adversarial Testing — Conduct red teaming and adversarial testing focused on enterprise AI usage, including prompt injection, data exfiltration, jailbreaks, and abuse of connected tools and plugins.
• Data Protection for AI Usage — Develop and enforce controls to prevent sensitive data leakage through AI systems, including input/output filtering, data classification, tokenization, and secure handling of prompts, embeddings, and outputs.
• Multi-Layer AI Security (Network, Endpoint, Data) — Integrate AI security into existing enterprise security layers: network visibility and control over AI service access, API traffic inspection, and zero trust enforcement; endpoint security for developer machines, research environments, browsers, and plugins; data layer controls ensuring proper handling of sensitive data when interacting with AI systems.
• AI Threat Modeling (Enterprise Context) — Develop threat models focused on enterprise AI usage, including risks such as data leakage, prompt injection, model misuse, supply chain risks from AI vendors, and unauthorized agent actions.
• Vendor & Platform Security — Assess and guide secure adoption of third-party AI vendors and platforms, including evaluating data handling practices, model behavior, and integration risks.
• Incident Response for AI Usage — Define and support response approaches for AI-related incidents, such as sensitive data exposure, policy violations, or misuse of AI tools.
• Cross-Functional Technical Leadership — Partner with Legal, Compliance, IT, and Engineering to align AI usage with regulatory requirements, data governance policies, and responsible AI practices.
• Security Enablement — Contribute to internal guidance and education on safe AI usage, including secure prompting, data handling, and appropriate use of AI tools.
• Security Tooling & Implementation — Evaluate and implement tooling for AI security, including AI gateways, DLP integrations, monitoring solutions, and policy enforcement mechanisms.

What You’ll Need to Succeed

• 8+ years of experience in information security, with strong expertise in enterprise, cloud, or application security.
• Hands-on experience designing and implementing security controls in enterprise environments.
• Familiarity with AI/ML systems and how modern AI tools (LLMs, copilots, APIs) are used in practice.
• Experience with cloud platforms (AWS/GCP), SaaS security, and zero trust architectures.
• Experience with data protection technologies (e.g., DLP, data classification, access controls).
• Practical experience with threat modeling, red teaming, or adversarial testing.
• Strong communication and influence skills across technical and non-technical stakeholders.

Bonus Points For

• Experience securing enterprise use of LLMs, copilots, or generative AI platforms.
• Familiarity with AI gateways, prompt filtering, or model interaction controls.
• Experience evaluating or securing third-party AI vendors and APIs.
• Background in regulated environments (biotech, healthcare, defense, or government).
• Experience with browser security, endpoint controls, or SaaS security platforms.
• Knowledge of privacy-enhancing technologies or confidential computing.
• Contributions to AI/ML security research or community.

Your Impact at LILA

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

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

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

Your Impact at LILA

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

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

What Success Looks Like

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

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

What You’ll Need to Succeed

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

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

Your Impact at LILA

Lila 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

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

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, as well as developing technical collateral including risk- and capability-focused evaluations and safeguards.

What You'll Be Building

• Design and build capability evaluations to test for scientific risks (both known but especially novel) from cutting edge scientific models integrated with automated physical labs, across biological / physical sciences.
• Coordinate and lead threat modelling exercises with internal and external scientific experts, including monitoring for emerging technologies and use-cases.
• Develop and curate high-quality training and test data for evals and safety systems.
• Evaluate risks from Lila’s capabilities, including through interactions with the wider ecosystem of capabilities (e.g. general-purpose frontier models as well as narrow scientific tools)
• 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

• A PhD in either a biological sciences domain (e.g., molecular biology, virology, computational biology or related fields) or a physical sciences domain (materials sciences, physics, chemistry, chemical or nuclear engineering, or related fields), or other related experience.
• Experience in scientific computing, across either biological or physical sciences.
• Familiarity with dual-use research and dissemination concerns, across the relevant safety / regulatory / governance frameworks (e.g. export control frameworks, biological and chemical-related conventions and controls).
• Ability to communicate complex technical concepts and concerns to non-expert audiences effectively.
• Demonstrated ability to lead teams of internal and external collaborators in buildout of Lila’s point-of-view on biological / physical risks
• Demonstrated ability to deal with cross-functional stakeholders (science, AI, product, policy) in a complex environment.

Bonus Points For

• Experience in developing or applying ML to biological or physical sciences
• Experience in building evaluations, or conducting red-teaming exercises, across scientific risks for frontier models / narrow scientific tools.

• This position may be based in any of Lila's offices, including Cambridge (MA), San Francisco (CA), or London (UK).

• For US-based candidates (Cambridge or San Francisco), we expect the base salary for this role to fall between $176,000–$304,000 USD per year, along with bonus potential and generous early equity. The final offer will reflect your unique background, expertise, and impact.
• For UK-based candidates, compensation will be determined separately and will be aligned with local market benchmarks and internal leveling.

Your Impact at LILA

Your role in our Physical Sciences division will center on developing the next generation of in silico materials discovery methods, from creating autonomous workflows and data-driven pipelines to building the interface between simulation and AI. You’ll pioneer strategies that enable agents to reason over simulation data, extract latent insights, and guide hypothesis generation and materials design. Your work will expand how we leverage simulation outputs for discovery, accelerating the integration of physics-based modeling and AI reasoning systems. You’ll collaborate with experts in areas spanning simulation, AI agents, and experimental automation to push the boundaries of digital discovery.

What You'll Be Building

• Develop methods and workflows for in silico materials discovery that connect physics-based simulations, generative models, and agentic AI systems.
• Build intelligent pipelines where AI agents can design, launch, interpret, and refine simulations autonomously.
• Design frameworks that utilize simulation data more effectively for prediction, inference, and discovery, including automatic feature extraction, model training, and data-driven exploration.
• Prototype and evaluate new paradigms for simulation-aware agents that can learn from and act on scientific simulations.
• Design data representations, metadata standards, and APIs that enable seamless flow of information between simulations, machine learning models, and experimental databases.
• Create scalable, modular workflows that bridge electronic structure, atomistic, and mesoscale simulations with AI-driven reasoning and hypothesis generation.
• Collaborate with computational scientists, machine learning experts, and platform engineers to integrate in silico discovery pipelines into Lila’s broader scientific superintelligence ecosystem.

What You’ll Need to Succeed

• PhD or equivalent experience in Computer Science, Materials Science, Chemistry, Physics, Applied Mathematics, or related disciplines.
• Strong foundation in in silico materials discovery, computational materials modeling, and/or simulation workflow design.
• Familiarity with large language models and their application in scientific domains, and
• Experience building AI-driven or agentic workflows for scientific automation and discovery.
• Solid programming skills in Python and scientific computing frameworks
• Familiarity with atomistic simulation software and libraries (e.g., VASP, LAMMPS, ASE, Pymatgen, etc.).

Bonus Points For

• Strong publication record in in silico materials discovery, simulation-AI integration, AI-driven inverse design.
• Familiarity with scientific agent architectures, large-scale reasoning systems, or multi-agent frameworks for hypothesis generation and experimental planning.
• Familiarity with ontologies, metadata standards, and data infrastructure for scientific simulations.
• Experience with automated experiment–simulation loops, integrating computational predictions with robotic or cloud-based laboratory platforms.

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

Your Impact at LILA

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

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

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

Your Impact at LILA

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

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, as well as developing technical collateral including risk- and capability-focused evaluations and safeguards.

What You'll Be Building

• Set the build and research strategy for Lila’s safety approach to biological / physical risks.
• Design and build capability evaluations to test for scientific risks (both known but especially novel) from cutting edge scientific models integrated with automated physical labs, across biological / physical sciences.
• Coordinate and lead threat modelling exercises with internal and external scientific experts, including monitoring for emerging technologies and use-cases.
• Develop and curate high-quality training and test data for evals and safety systems.
• Evaluate risks from Lila’s capabilities, including through interactions with the wider ecosystem of capabilities (e.g. general-purpose frontier models as well as narrow scientific tools)
• 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

• A PhD in either a biological sciences domain (e.g., molecular biology, virology, computational biology or related fields) or a physical sciences domain (materials sciences, physics, chemistry, chemical or nuclear engineering, or related fields), or other related experience.
• Demonstrated ability to set research directions for open problems in dual-use risks in the biological / physical sciences.
• Experience in scientific computing, across either biological or physical sciences.
• Familiarity with dual-use research and dissemination concerns, across the relevant safety / regulatory / governance frameworks (e.g. export control frameworks, biological and chemical-related conventions and controls).
• Ability to communicate complex technical concepts and concerns to non-expert audiences effectively.
• Demonstrated ability to lead teams of internal and external collaborators in buildout of Lila’s point-of-view on biological / physical risks
• Demonstrated ability to deal with cross-functional stakeholders (science, AI, product, policy) in a complex environment.

Bonus Points For

• Experience in developing or applying ML to biological or physical sciences
• Experience in building evaluations, or conducting red-teaming exercises, across scientific risks for frontier models / narrow scientific tools.

• This position may be based in any of Lila's offices, including Cambridge (MA), San Francisco (CA), or London (UK).

Your Impact at LILA

We are seeking a motivated and detail-oriented Senior Research Associate / Associate Scientist to join our high-throughput autonomous chemistry team. This is a hands-on role at the intersection of organic and inorganic synthesis, advanced characterization, and laboratory automation. You will design and execute high-throughput experiments, synthesize small molecules, and perform rigorous downstream analysis while working side-by-side with senior scientists to translate ambitious product goals into scalable experimental workflows that directly drive our company’s growth.

This position offers first-hand exposure to automated chemistry platforms, with real ownership over experiments that inform company strategy and scientific direction. Ideal candidates are execution-driven, thrive in fast-paced laboratory environments, and are eager to build deep technical expertise while contributing meaningfully to next-generation chemical discovery at Lila Sciences.

What You'll Be Building

• Independently execute experiments to synthesize, purify, and characterize organic and inorganic small molecules
• Optimize reactions, perform method development, and interpret analytical data to guide project decisions
• Translate early-stage ideas into reproducible, functional experiments
• Apply retrosynthetic planning to support target molecule synthesis
• Operate Schlenk lines, gloveboxes, and other inert-atmosphere systems for sensitive compounds
• Use PAT tools (UPLC, LCMS, NMR, IR, Fluroescence, DLS) to monitor reactions, ensure quality control, and generate high-quality data for ML workflows
• Leverage automation and AI-driven platforms to accelerate workflows and reduce experimental cycle times
• Collaborate with engineers, ML scientists, and stakeholders to validate model-suggested conditions, analyze trends, troubleshoot issues, and communicate results clearly through reports, discussions, and presentations
• Document results, maintain accurate digital records and adhere to EHS and laboratory best practices

What You’ll Need to Succeed

• M.S. in Chemistry or related discipline (organic, organometallics, catalysis) with 2+ years of hands-on high-fidelity laboratory experience in an academic or industry setting, or B.S. + 5 years
• Hands-on experience in organic/inorganic synthesis, purification, chromatographic techniques, product analysis
• Strong understanding of retrosynthesis, reaction mechanisms, and method optimization workflows
• Proficiency with analytical techniques such as TLC, HPLC, LCMS, NMR, and IR
• Ability to follow SOPs and work safely with diverse chemical reagents and automated platforms
• Ability to thrive in a fast-paced, multidisciplinary research environment
• Required proficiency in using SciFinder, Reaxys, Chemdraw to search public database and literature
• Basic proficiency in Python or other scripting languages for data handling
• Good document practices, strong attention to detail and a proactive, problem-solving mindset
• Effective oral and written communication skills; contributed to internal reports, technical documentation, or peer-reviewed publications

Bonus Points For

• Experience with high-throughput or AI-driven automated platforms
• Authored publications in peer-reviewed journals in chemical catalysis, organic methodology
• Ability to handle air/moisture/light sensitive compounds

Your Impact at LILA

Lila Sciences is seeking an experienced Senior Research Associate to join our Antibody Platform team. In this role, you will play a pivotal role in building Lila's Scientific Superintelligence platform by leveraging your deep expertise in high-throughput antibody discovery, production, and characterization.

What You'll Be Building

• Design, optimize and execute high-throughput workflows for antibody expression and purification. Drive platform evolution by identifying opportunities to improve efficiency, robustness, and scalability of antibody production.
• Collaborate closely with scientists, engineers, and automation experts to discover, evaluate and implement novel automation tools, technologies, and reagents to expand the Antibody Platform’s capabilities.
• Innovate and adapt protocols to accommodate new antibody formats, targets, and expression strategies.
• Analyze antibody yield and binding characteristics using biolayer interferometry (BLI) and other relevant techniques; interpret and communicate findings effectively.
• Ensure data quality, documentation, and reproducibility in all experiments; actively contribute to knowledge sharing and continuous improvement initiatives. Collaboration, flexibility, and creative problem-solving are key to thrive in this dynamic role.

What You’ll Need to Succeed

• Bachelor’s or Master’s degree in Biochemistry, Molecular Biology, Bioengineering, or related discipline with 2–5+ years of hands-on industry or academic experience.
• Proficiency with antibody expression in CHO-S, including DNA transfection, small-scale cell culture, and antibody characterization; ExpiCHO expertise strongly preferred.
• Experience using sequence analysis software for molecular biology workflows and annotating antibody sequences, specifically Geneious, Benchling, PipeBio, etc.
• Experience with liquid handling automation and high-throughput formats.
• Experience with biolayer interferometry (BLI) or surface plasmon resonance (SPR) quantitative binding assays, CE-SDS, and antibody developability assays.
• Outstanding troubleshooting skills, adaptability, and ability to drive innovation in experimental design.
• Familiarity with laboratory data analysis, digital recordkeeping, and collaborative scientific workflows.
• Strong communication skills and an eagerness to collaborate with a fast-paced, cross-functional team.

Bonus Points For

• Skilled in cloning, expression and optimization of multispecific antibody formats
• Data analysis using relevant software (Python, R, Spotfire, or similar)
• Scripting for automation platforms

Your Impact at LILA

We are seeking a (Senior) Research Associate to join our Life Sciences team. This role will focus on formulating lipid nanoparticles (LNPs), functionalizing them with targeting ligands, and characterizing the resulting targeted LNPs (tLNPs). The ideal candidate will have hands-on expertise in LNP formulation and characterization, bioconjugation, protein characterization, and automation technologies. Your work will unlock new understandings in the delivery space by enabling a high-throughput closed-loop screening cascade for tLNPs. This position will work closely with others across the protein, cell biology, automation, chemistry machine learning, and data science teams to help establish high-throughput next-generation tools for targeted nucleic acid delivery.

What You'll Be Building

• Design, execute, and characterize (DLS, ribogreen, TNS, etc.) LNP formulations across multiple scales and mixing modalities
• Develop and refine high-throughput ligand conjugation and purification methods, employing automation and robotics to increase throughput
• Collaborate with cell assay and antibody specialists to screen hits across a variety of tissue targets in in vivo models
• Collaborate with chemists, biologists, engineers, and data scientists to document systems, share insights, and refine best practices in autonomous flow chemistry
• Write, implement, and maintain Standard Operating Procedures (SOPs) for both existing and new lab processes

What You’ll Need to Succeed

• MSc in Chemical Engineering, Biomedical Engineering, Pharmaceutical Sciences, or a related field with 3+ years, or BSc with 5+ years, of hands-on experience in targeted LNP production, or related experience. Industry experience strongly preferred
• Strong working knowledge of bioanalytical techniques (e.g., DLS, fluorescence spectroscopy, electrophoresis, in vitro assays)
• Experience with protein and antibody design and characterization
• Familiarity with automation and high-throughput workflows
• Excellent troubleshooting, communication and collaboration skills, with a proven ability to work effectively across interdisciplinary teams

Your Impact at LILA

Lila Sciences is seeking an experienced Senior Research Associate specializing in Protein Purification, Analytics and Developability to support the build-out and day-to-day operation of a high-throughput protein characterization platform. In this role, you will contribute directly to the development of Lila's Scientific Superintelligence platform by executing and optimizing biophysical characterization workflows, collaborating effectively with cross-functional teams, and ensuring high-quality, reproducible data generation.

What You'll Be Building

• Execute and optimize high-throughput workflows for protein characterization across a wide range of modalities, including antibodies, bi-specifics, and enzymes.
• Operate and maintain automated chromatography systems, including Size Exclusion Chromatography (SEC), Hydrophobic Interaction Chromatography (HIC), and affinity chromatography using HPLC platforms.
• Perform biophysical characterization using techniques such as Dynamic Light Scattering (DLS), melting temperature analysis (DSF), binding assays (BLI/SPR), capillary electrophoresis (CE-SDS, cIEF) and more, to assess protein critical quality attributes
• Support workflows to characterize protein post-translational modifications (PTMs), ensuring comprehensive sample preparation and accurate data capture.
• Collaborate closely with scientists, engineers, and automation experts to identify and implement improvements in efficiency, robustness, and scalability of developability workflows.
• Innovate and adapt protocols to accommodate new protein formats, targets, and analytical strategies.
• Ensure data quality, documentation, and reproducibility in all experiments; actively contribute to knowledge sharing and continuous improvement initiatives.
• Collaborate with computational scientists to support data analysis pipelines, data processing, and the development of predictive models for protein characteristics.

What You’ll Need to Succeed

• Bachelor's or Master's degree in Biochemistry, Biophysics, Molecular Biology, Bioengineering, or a related discipline with 2-5+ years of hands-on industry or academic experience in protein production and characterization.
• Proven hands-on experience with HPLC techniques such as SEC, HIC, and affinity purification methods.
• Familiarity with biophysical characterization techniques including DLS, DSF, BLI/SPR, CE-SDS, cIEF or similar analytical methods.
• Experience with liquid handling automation and high-throughput formats.
• Familiarity with laboratory data analysis, digital recordkeeping (e.g., Benchling, electronic lab notebooks), and collaborative scientific workflows.
• Outstanding troubleshooting skills, adaptability, and ability to drive innovation in experimental design.
• Strong communication skills and an eagerness to collaborate within a fast-paced, cross-functional team.

Bonus Points For

• Familiarity with laboratory automation and high-throughput screening technologies.
• Data analysis using relevant software (Python, R, or similar).
• Experience with protein post-translational modification analysis workflows.

Your Impact at LILA

Lila is hiring a Boomi Developer to design, build, and support integrations that power our core business systems. You’ll own end-to-end integration delivery in Boomi AtomSphere—from requirements and mapping through deployment, monitoring, and ongoing support—partnering closely with Finance, People Ops, IT, and GTM stakeholders. Experience with procure-to-pay (P2P) and onboarding/offboarding workflows is a strong plus.

What You'll Be Building

• Design, develop, and maintain Boomi AtomSphere integrations (processes/atoms, APIs, schedules, connectors) across ERP, HCM, IAM, CRM, ITSM, and data platforms.
• Translate business requirements into scalable integration designs and data mappings (JSON, XML, CSV, EDI).
• Build resilient error handling, retries, and alerting; ensure observability and clear runbooks for L1/L2/L3 support.
• Manage Boomi MDH to support Data Quality and Source Data integrity
• Implement and manage REST/SOAP APIs, webhooks, and event-driven patterns where appropriate.
• Optimize existing integrations for performance, reliability, and cost; deprecate legacy jobs safely.
• Manage CI/CD, environment promotion, and release governance for integration assets.
• Ensure data quality, security, and compliance (PII handling, least privilege, audit readiness).
• Partner with stakeholders to prioritize a roadmap; provide estimates, status, and documentation.
• Provide production support within defined SLAs, including incident response and root-cause analysis.
• Contribute to standards, templates, and best practices for integration development at Lila.

What You’ll Need to Succeed

• 3–5 years in integrations/iPaaS, including 2+ years hands-on with Boomi AtomSphere.
• Proficiency with API design and integration (REST/SOAP), authentication (OAuth2, JWT, API keys), and data formats (JSON, XML, CSV).
• Strong data transformation skills (map functions, scripting, XSLT preferred) and error-handling patterns.
• Solid SQL fundamentals; familiarity with relational schemas and data modeling for integrations.
• Solid MDH experience
• Experience with Git-based workflows and CI/CD for integration deployments.
• Excellent collaboration and communication with non-technical stakeholders.

Bonus Points For

• Direct experience building/supporting Procure-to-Pay integrations (e.g., ERP, procurement, AP, payments).
• Direct experience with Onboarding/Offboarding integrations across HRIS, IAM/SSO (e.g., Okta/Azure AD), ITSM, and device management.
• Boomi certifications (Professional Developer, API Management, MDM) or equivalent.
• Familiarity with ERP (e.g., NetSuite/Oracle/SAP), HRIS (e.g., Workday/BambooHR), ITSM (e.g., Jira/ServiceNow), and procurement (e.g., Coupa).
• Experience with event-driven architectures, queues/streams (e.g., Kafka/SQS), and EDI B2B flows.
• Scripting for integrations (Groovy/JavaScript), and lightweight Python for tooling.
• Exposure to BOOMI AI functionality

Your Impact at LILA

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

Your Impact at LILA

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

Join us in shaping the future of science! We are seeking a software engineer with a background in life sciences to join our data science team, where you’ll collaborate with software engineers, lab scientists, and machine learning engineers to build cutting-edge tools for automated scientific analysis and more. This role spans web services and data engineering, 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!

What You'll Be Building

• Participate in the entire software development life cycle, focusing on designing, implementing, and maintaining software services.
• Develop reusable code and libraries to improve efficiency and scalability.
• Align development with strategic goals, ensuring software supports broader organizational needs.
• Manage git repositories, team Jira board and Notion Hub, promote best practices, assist lab scientists with use of new tools, and foster a collaborative development culture.
• Work directly with scientists to identify gaps and unmet needs, develop tailored software solutions for data management, LIMS functionality, and data automation
• Support infrastructure as code and design efficient deployment strategies.
• Write clear, concise documentation for both engineers and end users.

What You’ll Need to Succeed

• Minimum of 2 years of experience writing software in a commercial setting.
• High-level proficiency in Python.
• Strong understanding of git best practices.
• Acute listening skills and patience to deeply understand user challenges.
• Experience implementing scalable software solutions.
• Excellent problem-solving skills and team-first mentality.
• Strong communication skills to effectively collaborate with team members and stakeholders.
• Energetic self-starter and independent thinker, with strong attention to detail.
• Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting.

Bonus Points For

• Background in biological sciences
• Familiarity with data science and ML libraries (pandas, numpy, scipy).
• Knowledge of modern developer tools (pydantic, pyright, uv).
• Understanding of Kubernetes, ArgoCD, and GitHub Actions.
• Familiarity with AWS fundamentals (e.g., RDS, EC2, S3, EKS).

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

What You'll Be Building

• Developing, wrapping, and adapting domain-specific tools for molecular modeling, data analysis, process optimization, molecular modeling, and more
• Deploying custom, agentic scientific workflows that integrate with industrial instruments (PLCs, control software) and data systems (ERP, MES, SCADA)
• Building real-time data pipelines that transform messy scientific data into actionable insights
• Creating intuitive interfaces that make complex AI/ML capabilities accessible to scientists
• Architecting solutions that scale from proof-of-concept to enterprise-wide deployment

What You'll Need to Succeed

• Background in biology, molecular science, or related scientific field
• Outstanding communication skills—you'll interface directly with both executives and the manufacturing line
• Experience with data engineering, ETL pipelines, and working with complex/messy datasets
• Strong full-stack development skills with the ability to work across backend, frontend, and data layers
• Exceptional problem-solving abilities and comfort with ambiguous requirements
• Knack for product discovery and ability to pass requirements back to the platform
• Willingness to travel to customer sites for frequent and extended deployments

Bonus Points For

• Previous customer-facing or consulting experience
• Depth in controls engineering and machine learning
• Experience with scientific computing libraries (NumPy, Pandas, SciPy, RDKit)
• Familiarity with ML/AI implementation and deployment
• Familiarity with modern web technologies (TypeScript, React)

What Makes a Great FDE

• Customer Obsessed: You're energized by understanding customer problems deeply and seeing your solutions drive real impact
• Fast Learner: You quickly grasp new scientific domains and translate complex requirements into elegant solutions
• Autonomous: You thrive with minimal supervision and take extreme ownership of customer success
• Collaborative: You work seamlessly with both technical and non-technical stakeholders
• Technically Versatile: You're comfortable jumping between technologies and picking up new tools as needed

Ready to Transform Scientific Discovery?

If you're excited about embedding with world-class scientists, shipping impactful solutions at startup speed, and building the future of scientific software, we want to hear from you. Join us in our mission to accelerate breakthrough discoveries that will define the next century of human progress.

Your Impact at LILA

Your Impact at LILA

Lila Sciences is seeking an Industrial Engineer to own and architect the physical layout of our AI Science Factories. In this role, you will be responsible for integrating lab instrumentation and robotics into a cohesive, efficient factory design. You will collaborate closely with hardware engineering, science teams, robotics, facilities, and operations to create layouts that are safe, scalable, and engineered for high-throughput autonomous research. This role is central to translating complex cross-functional requirements into a factory environment that enables Lila’s next-generation scientific capabilities. 

What You'll Be Building

• Own the end-to-end factory layout, including spatial planning, adjacency mapping, and utility requirements
• Integrate lab instrumentation layouts from diverse scientific and engineering domains into a unified, high-performance factory design.
• Collaborate with hardware, robotics, science, automation, safety, and facilities teams to incorporate requirements for utilities, safety, accessibility, maintenance, and environmental controls.
• Build and maintain accurate 2D and 3D CAD models of the full factory layout; drive version control and change management for layout updates.
• Work with robotic teams to run simulations and scenario analyses to assess throughput, operational efficiency, and bottlenecks.
• Partner with construction, facilities, and external vendors to ensure physical buildouts reflect the intended design and can scale with future demand.
• Develop standards, documentation, and layout guidelines for new lab instrumentation and factory expansions.

What You’ll Need to Succeed

• BS/MS degree in Industrial Engineering, Mechanical Engineering, Manufacturing Engineering, Systems Engineering, or related field.
• Experience designing factory, lab, or production layouts in high-complexity environments.
• Proficiency with CAD tools (e.g., AutoCAD, REVIT, SolidWorks).
• Strong understanding of workflow optimization, material flow, ergonomics, and spatial systems.
• Ability to collaborate across multidisciplinary engineering and scientific teams.
• Clear communication and documentation skills.

Bonus Points For

• Experience with robotic or automated systems and their physical integration into facilities.
• Familiarity with lab/industrial utility requirements (HVAC, gases, power, chemicals, drainage).
• Experience in R&D facilities, pilot plants, or advanced automation environments.

Your Impact at LILA

Lila Sciences is seeking an experienced Staff/Principal Mechanical Engineer to join our Hardware & Controls Engineering team. The specific title (Staff or Principal) will be determined based on experience level and expertise. As a key technical leader, you'll leverage your deep expertise to design and engineer components for workcell robotics, mechanical systems, and automation across life science and physical science domains. You'll collaborate with experimental scientists, AI/ML scientists, software/controls engineers, and process/automation engineers to lead complex projects, guide multidisciplinary teams, and deliver state-of-the-art automation solutions.

What You'll Be Building

• Lead the design and implementation of bench-scale robotics and mechanical systems for sophisticated automation in life science and physical science applications.
• Design innovative pneumatic, electro-mechanical, and optical solutions for advanced robotic workcells used in biological research and materials discovery.
• Lead and mentor teams in advanced CAD design and analysis using SolidWorks, optimizing systems for high-throughput workflows.
• Direct the creation and validation of designs through 3D printing, laser cutting, and other rapid prototyping methods.
• Lead cross-functional collaborations to integrate mechanical designs with software and controls across scientific domains.
• Develop strategies to transform manual processes into automated workflows while maintaining rigorous safety and quality standards.
• Coordinate with external vendors for custom component development and system integration.

What You’ll Need to Succeed

• Bachelor's or Master's degree in Mechanical Engineering, Robotics, or a related field.
• 8+ years of mechanical engineering experience with proven expertise in life science and physical science automation (title will be determined based on experience & expertise).
• Extensive proficiency in SolidWorks CAD design, GD&T, and DFMA
• Extensive proficiency working with & implementing requirement management systems, product lifecycle management, & product data management.
• Demonstrated leadership in managing complex projects and mentoring engineering teams.

Bonus Points For

• Deep experience in automation across scientific domains, with expertise in SolidWorks CAD design, PDM Vault management, rapid prototyping, and mechanical testing.
• Expert knowledge of motion control and mechatronic systems for diverse scientific applications.
• Strong background in automated systems and robotic integration for biological research, and materials science in the biotech sector.
• Demonstrated strong attention to detail in documentation, adherence to standards, and equipment management.
• Experience in automotive manufacturing, battery manufacturing, semiconductor manufacturing, and packaging with discrete material movement.

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

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

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

What You'll Be Building

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

What You’ll Need to Succeed

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

Bonus Points For

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

Your Impact at LILA

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

Are you a strategic social thinker who thrives at the intersection of science, technology, and community? We’re seeking a Community & Ecosystem Growth Strategist who blends social strategy, influencer relations, and hands-on community building to accelerate Lila’s executives and scientific leaders’ credibility and reach. You’ll grow high-quality networks across academia, industry, government, and frontier tech ecosystems, helping our leaders participate credibly in technical discourse and earn durable attention.

What You’ll Be Building

Executive presence and follower growth

• Design and execute growth plans for key executives’ social profiles (X, LinkedIn, Substack, and niche platforms).
• Grow audiences with high-quality followers: researchers, lab leaders, R&D decision-makers, open-source contributors, and public-sector stakeholders.
• Establish an “always-on” cadence: posts, replies, threads, commentary, and light community engagement that feels authentic and valuable.

Engagement in frontier technical communities

• Actively participate in discussions around AI-accelerated science, robotics, materials science, life science, chemistry, frontier tech, generative AI, and agentic science.
• Bring Lila into conversations in helpful, credible ways, even in skeptical or high-signal threads.

Influencer and network activation

• Cultivate relationships with credible creators and technical influencers across academia, industry, and government-adjacent networks.
• Orchestrate co-created moments: AMAs, roundtables, briefings, interviews, guest threads, and research spotlights that elevate Lila leaders.

Thought leadership enablement

• Ghostwrite or co-author with executives and technical leaders while preserving their voice and cadence.
• Coach leaders on engaging in replies and threads to build trust and depth, not hype.

Measurement, learning, and iteration

• Define and track meaningful metrics: follower quality, engagement depth, network effects, and relationship development.
• Test hypotheses, and continuously refine the playbook.

What You’ll Need to Succeed

• Proven track record of organically growing executive and brand followings in technical/scientific spaces.
• Strong writing and editorial judgment for technical audiences; ability to translate complex ideas into clear, credible content.
• Comfort engaging in public technical discourse, including disagreement and skepticism.
• Marketing fluency to align with brand and communications strategy, plus the independence to run social/community initiatives day to day.

Bonus Points For

• Existing networks across AI-for-science, robotics, open-source, academia, national labs, or government-adjacent science/tech communities.
• Experience supporting executives or founders as thought leaders (voice, cadence, engagement strategy).

Your Impact at LILA

We are seeking a highly organized and proactive Administrative Coordinator to provide operational and administrative support to our Government Affairs team in Washington, D.C. This role is the operational backbone of the team and ensuring the Government Affairs office can focus on its core efforts. The ideal candidate is a self-starter who thrives in a fast-paced, politically dynamic environment and exercises sound judgment and discretion at all times. This role requires 5 days a week in office in Washington, DC.

What You'll Be Building

GA Team Support 

• Manage scheduling for government affairs team, including scheduling meetings with Members of Congress, agency officials, and coalition partners
• Coordinate domestic and international travel logistics, including flights, hotels, ground transportation, and itineraries
• Prepare and process expense reports, invoices, and reimbursements in a timely manner
• Draft, proofread, and edit correspondence, briefing materials, talking points, and internal memos
• Anticipate executive needs and proactively handle administrative matters with minimal direction

Office Management

• Oversee day-to-day office operations, serving as the primary point of contact for building management, vendors, and service providers
• Manage office supply inventory, equipment maintenance, and facilities needs
• Onboard new team members, including coordinating workspace setup, badge access, and IT provisioning
• Maintain organized filing systems for legislative documents, regulatory filings, lobbying disclosures, and compliance records
• Coordinate with IT support to troubleshoot technology issues and manage software subscriptions

Legislative & Government Affairs Support 

• Assist with the preparation and timely filing of lobbying disclosure reports (LDA filings) in coordination with compliance counsel
• Maintain and update contact databases for congressional offices, agency contacts, and external stakeholders
• Coordinate logistics for Capitol Hill meetings, fly-ins, briefings, receptions, and fundraising events with external consultants
• Support the preparation of leave-behinds, one-pagers, and materials for Congressional meetings

Events & Stakeholder Coordination

• Plan and execute team events, including staff retreats, coalition meetings, roundtables, and receptions
• Manage coordination including invitations, RSVPs, catering, AV setup, and venue coordination
• Liaise with external partners, trade associations, and consulting firms on joint events and initiatives

What You’ll Need to Succeed

• 3–5 years of experience in an executive assistant, office manager, or administrative role, preferably in public service, government affairs, lobbying, law, or public policy environment
• Strong familiarity with the Washington, D.C. political landscape, including how Congress and federal agencies operate
• Exceptional organizational skills with the ability to manage multiple priorities under tight deadlines
• Outstanding written and verbal communication skills
• High degree of professionalism, discretion, and judgment when handling sensitive or confidential information
• Proficiency in Microsoft Office Suite (Outlook, Word, Excel, PowerPoint) and collaboration tools (e.g., Teams, Zoom, Slack)
• Ability to travel up to 10% of the calendar year, including international travel
• Ability to be in office 5 days a week in Washington, DC

Preferred

• Experience supporting C-suite executives or senior government officials
• Familiarity with Lobbying Disclosure Act (LDA) compliance and reporting requirements
  • Experience with stakeholder or contact management platforms (e.g., Quorum, Fireside, Salesforce)
  • Familiarity with political giving processes, including PAC administration, contribution tracking, and FEC reporting requirements
• Bachelor's degree in political science, public policy, communications, business, or a related field

Bonus Points For

• Discretion & Professionalism — Handles sensitive political and organizational information with the utmost confidentiality
• Proactive Problem-Solving — Anticipates issues and resolves them before they escalate
• Attention to Detail — Produces accurate, polished work products consistently
• Adaptability — Remains calm and effective in a dynamic, high-stakes environment
• Relationship Management — Builds trusted relationships with internal stakeholders, congressional staff, and external partners