Staff / Principal Security Engineer, AI Safety and Security

$192K - $306K Cambridge, MA, US Senior AI Safety

Interested in this AI Safety role at Lila Sciences?

Apply Now →

About This Role

AI job market dashboard showing open roles by category

Your Impact at LILA

Lila is developing AI systems for scientific discovery, connecting advanced models with scientific tools and automated laboratories. As these systems become more capable, we need to understand how they could be misused or compromised, which protections matter most, and how to establish that those protections work together.

We're hiring an experienced security practitioner to lead this work at the boundary between AI safety and security. You will investigate real systems, develop threat models, test important assumptions and recommend the protections needed for particular capabilities and deployments. Your work will address both dangerous misuse and the protection of sensitive models, data and research.

This is an opportunity to help shape a developing area of AI safety through security practice. You will combine technical investigation with careful analysis and clear writing, working closely with our established Security team, researchers and engineers to turn findings into implemented improvements.

You will bring security depth and develop your understanding of model capabilities, evaluations and safeguards through that work. Prior experience in AI safety is welcome, but not required.

What You'll Be Building

  • Threat-model consequential systems and capabilities. Investigate how dangerous misuse or compromise could occur across models, deployed services and scientific workflows. Identify the relevant actors, access routes and failure paths, and the protections that could interrupt them.
  • Test the assumptions behind our defenses. Examine architectures, code, configurations and operational evidence. Run focused adversarial tests, build prototypes and investigate weaknesses, including situations where several protections depend on the same underlying system.
  • Shape the protections needed for safe deployment. Turn dangerous-capability assessments into concrete security requirements. Assess how compromise of models, sensitive assets or supporting systems changes the safety assessment, drawing on personnel, physical and other security specialists where relevant.
  • Make evidence-based recommendations. Define what evidence would demonstrate that important controls and safeguards work together. Combine findings from model evaluations and security testing, explain the remaining uncertainty, and recommend improvements or conditions for use.
  • Carry findings through to practical improvements. Work with Security, Safety and engineering owners to prioritize changes, specify acceptance criteria and verify the results. Make clear recommendations about which gaps require attention first and why.
  • Anticipate what more capable systems will require. Identify where changes in capability, access or deployment call for stronger protections. Help teams plan security work with long lead times, and update assessments as systems change or new evidence emerges.
  • Develop methods that others can use. Turn your investigations into clear threat models, assessment methods and technical guidance. Help Safety and Security colleagues apply the approach to new problems and build a shared understanding of the evidence behind decisions.

What You'll Need to Succeed

  • Substantial hands-on security experience and deep expertise in at least one relevant discipline, such as security architecture, application or infrastructure security, offensive security, or incident response. You can reason across disciplines and identify when specialist judgment is needed.
  • A record of resolving consequential security problems. You have investigated ambiguous risks, challenged designs and followed improvements through to validation, including work involving several teams.
  • Strong technical judgment. You can examine unfamiliar systems, scrutinize code and configurations where relevant, and use technical investigation or testing to distinguish plausible claims from demonstrated protection.
  • Curiosity about AI safety and scientific systems. You want to understand how model capabilities and safeguards are evaluated, can learn unfamiliar domains quickly, and reason carefully when evidence is incomplete.
  • Clear writing and effective collaboration. You can explain a technical argument precisely, work through disagreements with specialists and turn a complex investigation into an actionable recommendation.

Bonus Points For

  • Securing ML infrastructure, model assets, research environments or AI agents.
  • Evaluating safeguards, adversarial robustness or defenses against sophisticated threats.
  • Translating risk assessments into technical requirements, assurance evidence or deployment decisions.
  • Working in scientific or other environments where digital access can have significant real-world consequences.

We value demonstrated security depth, sound judgment and the ability to learn. A research PhD or experience in every security discipline is not required.

Your early work will help establish how Lila combines safety and security evidence to make concrete decisions about its systems.

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We're All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

Salary Context

This $192K-$306K range is above the 75th percentile for AI Safety roles in our dataset (median: $230K across 13 roles with salary data).

Role Details

Company Lila Sciences
Title Staff / Principal Security Engineer, AI Safety and Security
Location Cambridge, MA, US
Category AI Safety
Experience Senior
Salary $192K - $306K
Remote No

About This Role

This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.

The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.

Across the 10,827 AI roles we're tracking, AI Safety positions make up 0% of the market. At Lila Sciences, this role fits into their broader AI and engineering organization.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

What the Work Looks Like

Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

Skills in Demand for This Role

Python (19% of roles) Aws (10% of roles) Azure (8% of roles) Rag (8% of roles) Gcp (6% of roles) Pytorch (5% of roles) Claude (5% of roles) Kubernetes (5% of roles)

Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.

Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.

Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

Compensation Benchmarks

AI Safety roles pay a median of $266,050 based on 70 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($249K) sits 6% below the category median. Disclosed range: $192K to $306K.

Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Engineering Manager ($253,461) and Research Engineer ($252,000). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.

Lila Sciences AI Hiring

Lila Sciences has 7 open AI roles right now. They're hiring across AI Safety, AI Product Manager, AI/ML Engineer. Positions span London, England, UK, San Francisco, CA, US, Cambridge, MA, US. Compensation range: $197K - $374K.

Location Context

Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 median).

Career Path

Common paths into AI Safety roles include Software Engineer, Data Scientist, Data Analyst.

From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.

Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.

What to Expect in Interviews

AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.

When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

AI Hiring Overview

The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.

The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

The AI Job Market Today

The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (122) are outnumbered by mid-level (5,256) and senior (3,922) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,527 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 70 roles with disclosed compensation, the median salary for AI Safety positions is $266,050. Actual compensation varies by seniority, location, and company stage.
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
About 6% of the 10,827 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Lila Sciences is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Safety positions include Senior Engineer, AI Architect, Engineering Manager, Principal Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

Get Weekly AI Career Intelligence

Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.