Interested in this AI Safety role at Moonshot?
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Moonshot is recruiting a Head of AI Safety to lead the delivery, development, and growth of our AI Safety portfolio. The role combines Moonshot's expertise in violence prevention, behavioral risk, and online harms with the emerging practice of evaluating and improving the safety of AI systems. The portfolio addresses harm categories including pathways to violence, extremism, child sexual exploitation, abuse and grooming (CSEA), mental health and crisis, and risks affecting children and teenagers.
The Head of AI Safety will serve as Moonshot's primary applied AI safety counterpart for frontier AI companies, governments, and regulators. The role will work closely with model, policy, trust and safety, product, research, and engineering teams. This is not an engineering or data\-science role, but it is a hands\-on position requiring the successful candidate to lead and participate directly in red teaming and adversarial evaluation, working in detail with evaluation methodologies, test scenarios, model responses, safety policies, and intervention frameworks. The role holds responsibility for client and partner relationships, project and staff management, methodological quality, and business development. The Head of AI Safety will build and maintain relationships across the wider AI safety ecosystem, including with governments, foundations, regulators, academics, researchers, and civil society organizations.
Candidates must be based in MA, CO, NY, VA, GA, PA, MD, WI, TN, OR, NJ, or DC. Occasional travel may be required.
Your responsibilities will include:
Applied AI Safety, Evaluation, and Advisory
- Lead and quality\-assure Moonshot's applied AI safety work across harm categories including pathways to violence, extremism, CSEA, abuse and grooming, mental health and crisis, and risks affecting children and teens, using methods such as red teaming and adversarial evaluation of AI systems.
- Advise frontier AI companies on how to improve the safety of their models, products, policies, and intervention systems.
- Translate insights from psychologists, child\-safety specialists, violence\-prevention practitioners, safeguarding experts, and other subject\-matter experts into clear, actionable guidance for model safety, policy, product, research, and engineering teams.
- Set the methodological approach for the portfolio, translating violence\-prevention, safeguarding, and behavioral\-risk expertise into structured and testable evaluation frameworks.
- Lead and participate directly in red teaming and adversarial evaluation, working in detail with test scenarios, model responses, scoring criteria, safety policies, and evaluation results.
- Identify patterns, edge cases, and potential safety failures, and develop practical recommendations for improving model behavior and user protections.
- Maintain rigor and clear documentation across the team's technical deliverables, suitable for technical, government, and foundation audiences.
- Ensure work is delivered within a clear ethical framework and in compliance with contractual, legal, data protection, and ethics obligations.
- Identify, manage, and escalate operational, reputational, delivery, and partnership risks.
Client \& Partner Management
- Serve as Moonshot's primary applied AI safety counterpart for frontier AI company partners, governments, regulators, and the wider ecosystem invested in AI safety.
- Build trusted relationships with model, policy, trust and safety, product, research, and engineering teams.
- Build and sustain relationships across the wider AI safety ecosystem, including governments, foundations, regulators, academics, researchers, civil society organizations, and specialist practitioners.
- Represent Moonshot externally in meetings, briefings, workshops, and sector engagement, including with regulators and policymaker audiences.
Team Leadership \& Management
- Provide direct leadership, coaching, and management to Moonshot's AI safety team.
- Foster a collaborative, accountable, and mission\-driven team culture, with particular attention to wellbeing given the sensitive nature of the work.
- Support workforce planning, performance management, and professional development across the team.
- Ensure effective coordination with internal teams supporting the portfolio, including operations, finance, research, and technical teams.
Portfolio Development \& Growth
- Develop Moonshot's AI safety portfolio, identifying strategic opportunities, partnerships, and funding.
- Lead proposal development, scoping, and renewals with technical credibility, using precise, defensible language suited to technical and government audiences.
- Develop repeatable methodologies, service offerings, and partnerships that allow the portfolio to grow while maintaining methodological rigor and delivery quality.
- Support external communications, publications, briefings, and thought leadership that establish Moonshot as a credible voice in applied AI safety.
- Oversee project planning, staffing, budgeting, forecasting, and delivery timelines across the portfolio.
Requirements Essential:
- Experience in trust \& safety, online harms, or a closely related field such as violence prevention, safeguarding, or public health, and the ability to adapt that knowledge to AI systems.
- Curiosity about AI and the ability to build technical fluency quickly, enough to engage credibly with technical counterparts at AI companies. Much of this work is new, so comfort learning as you go matters more than existing AI safety expertise.
- Experience designing research, evaluation frameworks, or interventions for harm categories such as violent extremism, CSEA, self\-harm and crisis, or targeted violence.
- Demonstrated experience managing projects, teams, budgets, partners, and clients, with strong people management skills.
- Excellent written communication, with experience producing credible (not promotional) material for government, foundation, or enterprise audiences.
- Comfort and demonstrated resilience working with highly sensitive or graphic content (CSEA, extremist material, crisis content), with awareness of wellbeing practices for this kind of work.
- Strong judgment and the ability to navigate ambiguity, competing priorities, and sensitive stakeholder environments, including representing organizations externally.
- Willingness to travel and work outside regular hours where needed to accommodate clients or respond to incidents.
- Highly trustworthy, with discretion and diplomacy, and willing to undertake relevant security clearance procedures.
- Experience supporting business development, grant funding, or procurement.
- Commitment to Moonshot's mission.
- Candidates must be eligible to work in the US, and will be required to undertake and pass any relevant security clearance procedures per client needs.
Desirable:
- Direct experience in model safety, red teaming, or adversarial evaluation of LLMs or other AI systems.
- Understanding of LLM architecture, safety tooling, or trust \& safety policy.
- Prior experience in child safety evaluation, teen\-safety product work, or grooming and CSEA detection.
- Familiarity with government or regulatory engagement, such as briefing officials or supporting policy submissions.
- Experience with intervention or diversion program design that can transfer to AI\-mediated interventions.
- Academic or applied background in radicalization studies, forensic psychology, or violence risk assessment.
- Familiarity with taxonomy or classifier development, including how testing data feeds a classifier.
Benefits
- 15 days paid vacation leave, plus Federal holidays and 1 day additional paid leave for Native American Heritage Day.
- Flexible public holiday policy with the option to work federal holidays in exchange for a day off at another time.
- Full private healthcare package, including coverage for partners and children.
- Dental \& Vision Insurance.
- Life \& Disability Insurance.
- 24/7 access to free counseling via our Employee Assistance Program.
- 3% matched 401k contributions.
- 401(k) Roth Contributions.
- Generous maternity and paternity leave: 26 weeks paid maternity leave, 8 weeks paid paternity leave.
- All permanent employees are granted share options upon employment.
Salary: $110,000 \- $145,000 (depending on skills and experience).
Salary Context
This $110K-$145K range is in the lower quartile for AI Safety roles in our dataset (median: $262K across 8 roles with salary data).
Role Details
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 4,317 AI roles we're tracking, AI Safety positions make up 0% of the market. At Moonshot, 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 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 $287,500 based on 34 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($127K) sits 56% below the category median. Disclosed range: $110K to $145K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include Research Engineer ($272,100) and AI Engineering Manager ($244,000). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Moonshot AI Hiring
Moonshot has 1 open AI role right now. They're hiring across AI Safety. Based in Washington, DC, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
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