AI Safety Careers Guide: Roles and Salary 2026

You want to work in AI, but every job post sounds like it was written by a PhD committee after three coffees. “AI safety researcher,” “model evaluation engineer,” “policy fellow,” “red team specialist,” “governance analyst.” Cool, but what do these people actually do, and can you get paid without being a DeepMind-level genius?

Good news: AI safety is no longer just a tiny academic corner. In 2026, it is a real career path across labs, startups, governments, nonprofits, consultancies, and big tech. The harder part is figuring out where you fit, what skills you need, and whether the salary is worth the effort.

What is an AI safety career?#

AI safety careers focus on making AI systems safer, more reliable, more secure, and better aligned with human goals.

That can mean a lot of things, so let’s make it practical.

You might work on:

1. Testing whether a model helps users make weapons or malware.
2. Reducing hallucinations in AI products used by doctors, lawyers, or banks.
3. Auditing AI systems for bias and discrimination.
4. Writing policy for AI regulation in the EU, UK, or US.
5. Building tools that monitor model behavior in production.
6. Red teaming chatbots before release.
7. Researching alignment problems for advanced AI systems.
8. Helping companies comply with the EU AI Act, NIST AI Risk Management Framework, or internal safety rules.

The field has a weird mix of people. You will meet machine learning engineers, philosophers, cybersecurity folks, public policy grads, data scientists, product managers, lawyers, psychologists, and former trust and safety workers from companies like Meta, Google, TikTok, and OpenAI.

That is the first thing to know: you do not need one perfect background.

You do need to be clear about which lane you want.

Why AI safety careers are growing in 2026#

AI adoption is moving faster than most companies can manage. Tools from OpenAI, Anthropic, Google DeepMind, Microsoft, Meta, Mistral AI, and Amazon are being plugged into customer service, coding, legal review, healthcare workflows, recruiting, education, finance, and government services.

That creates risk.

Companies now need people who can answer basic but serious questions:

• Can this model leak private customer data?
• Can someone jailbreak it?
• Does it discriminate against protected groups?
• Can employees trust the output?
• Who is legally responsible if it causes harm?
• How do we monitor it after launch?
• Are we compliant with AI rules in the EU and US?

In 2026, hiring is especially active in:

1. Frontier AI labs, such as OpenAI, Anthropic, Google DeepMind, xAI, and Meta.
2. AI infrastructure and security companies, such as Scale AI, Databricks, Palantir, CrowdStrike, and Wiz.
3. Government and policy bodies, including the European Commission, UK AI Safety Institute, US AI Safety Institute, and national regulators.
4. Consultancies, such as Accenture, Deloitte, McKinsey, BCG, EY, and PwC.
5. Nonprofits and research groups, such as METR, Center for AI Safety, Rethink Priorities, RAND, and the Ada Lovelace Institute.
6. Regulated industries, including banks, insurance firms, healthcare companies, and defense contractors.

The money can be very good, but it varies wildly by role.

A senior ML safety engineer at OpenAI in San Francisco might earn $250k to $500k total compensation. A policy analyst in Brussels may earn €55k to €90k. A nonprofit researcher in London might earn £45k to £90k. A governance consultant in Germany could earn €65k to €120k.

Same field, very different pay bands.

Main AI safety career paths in 2026#

Let’s break the field into practical job families. This matters because “AI safety” can mean research, engineering, law, product, policy, or operations.

1. Technical AI safety researcher

This is the role most people imagine when they hear AI safety.

You study how advanced AI systems behave, why they fail, and how to make them safer. You may work on alignment, interpretability, scalable oversight, evaluations, model behavior, adversarial robustness, or dangerous capability testing.

Typical employers include:

• Anthropic
• OpenAI
• Google DeepMind
• Meta FAIR
• Microsoft Research
• Redwood Research
• METR
• Center for AI Safety
• academic labs at Stanford, MIT, Oxford, Cambridge, Berkeley, and ETH Zurich

Common tasks:

1. Run experiments on large language models.
2. Study when models deceive, manipulate, or hide reasoning.
3. Build evaluation benchmarks.
4. Write research papers or technical reports.
5. Analyze model internals and activation patterns.
6. Test safety methods before model deployment.

Typical salary in 2026:

• US: $140k to $300k base, often $200k to $600k total compensation at top labs.
• UK: £70k to £180k, higher at top AI labs.
• EU: €75k to €180k, depending on country and employer.
• Nonprofit or academic roles: often lower, around $70k to $160k, or €55k to €120k.

Best fit if you:

• Like math, experiments, and research uncertainty.
• Can code well in Python.
• Understand machine learning deeply.
• Can read papers without needing a nap after page two.
• Are patient with ambiguous problems.

Nice-to-have skills:

• PyTorch or JAX
• Transformers
• Reinforcement learning
• Mechanistic interpretability
• Statistics
• Experiment design
• Paper writing

Do you need a PhD? For top research scientist roles, often yes. For research engineer roles, not always.

If you do not have a PhD, target research engineering, evaluations, data, or applied safety roles first.

2. AI safety engineer

AI safety engineers build tools and systems that make models safer in practice.

This role is more applied than research. You may build evaluation pipelines, moderation systems, monitoring dashboards, red team platforms, data filters, or guardrail tooling.

Employers include:

• OpenAI
• Anthropic
• Microsoft
• Google
• Amazon
• Scale AI
• Databricks
• Hugging Face
• Palantir
• Cohere
• Mistral AI
• enterprise AI startups

Common tasks:

1. Build automated tests for model behavior.
2. Create safety filters and classifier systems.
3. Develop monitoring tools for AI usage.
4. Work with product teams before release.
5. Analyze logs for harmful or unexpected outputs.
6. Improve data pipelines for safer training and fine-tuning.

Typical salary in 2026:

• US: $130k to $240k base, $180k to $450k total compensation at larger firms.
• UK: £65k to £140k.
• EU: €70k to €150k.
• Germany, Netherlands, France: commonly €75k to €130k for senior roles.
• Switzerland: CHF 120k to CHF 220k.

Best fit if you:

• Are a software engineer or ML engineer.
• Want practical impact.
• Like building systems, not only writing papers.
• Can move between research, product, and infrastructure.

Skills to learn:

• Python
• TypeScript or Go, depending on company stack
• PyTorch
• LLM APIs
• Evaluation frameworks
• Data pipelines
• Cloud platforms like AWS, GCP, or Azure
• Security basics
• MLOps tools

This is one of the best entry points if you already work in software engineering.

You can move from backend engineering, data engineering, ML engineering, DevOps, or security engineering into AI safety engineering with the right projects.

3. AI red team specialist

AI red teamers try to break AI systems before users, attackers, or journalists do.

They test models for harmful outputs, jailbreaks, prompt injection, data leaks, bias, fraud support, cyber misuse, and unsafe advice.

This role has grown fast because companies now need proof that their AI products were tested before release.

Employers include:

• Microsoft
• Google
• OpenAI
• Anthropic
• Meta
• CrowdStrike
• Cisco
• Trail of Bits
• Bishop Fox
• Deloitte
• Accenture
• AI audit startups
• banks and defense firms

Common tasks:

1. Write adversarial prompts.
2. Test model limits across risk areas.
3. Simulate attacker behavior.
4. Document vulnerabilities.
5. Work with engineers to fix issues.
6. Create repeatable testing methods.
7. Test prompt injection in AI agents and tools.

Typical salary in 2026:

• US: $110k to $220k.
• Senior US roles: $180k to $300k total compensation.
• UK: £55k to £120k.
• EU: €60k to €130k.
• Cybersecurity-heavy roles in Switzerland or Germany can reach €140k or CHF 180k.

Best fit if you:

• Have cybersecurity, trust and safety, fraud, content moderation, or QA experience.
• Think like a mischievous teenager, but document like an auditor.
• Like testing systems and finding weird edge cases.

Skills to learn:

• Prompt injection techniques
• Web security basics
• OWASP Top 10 for LLM Applications
• Python scripting
• Threat modeling
• Security reporting
• Model behavior evaluation
• Responsible disclosure basics

You do not always need a machine learning degree for this path. Cybersecurity experience can be more important.

More AI safety roles that are hiring#

The technical roles get the hype, but many AI safety jobs are not pure coding jobs. If your background is law, policy, risk, consulting, product, or operations, keep reading.

4. AI governance analyst

AI governance is about rules, processes, risk controls, and accountability.

You help organizations decide how AI should be built, approved, monitored, and audited. This role is booming because executives are worried about regulation, lawsuits, reputational damage, and security incidents.

Employers include:

• Deloitte
• PwC
• EY
• KPMG
• Accenture
• McKinsey
• BCG
• banks like JPMorgan Chase, HSBC, BNP Paribas, Deutsche Bank
• insurers like AXA, Allianz, Zurich
• healthcare companies
• public sector bodies
• AI governance startups

Common tasks:

1. Create AI risk policies.
2. Map company AI systems and risk levels.
3. Help teams comply with the EU AI Act.
4. Review vendors and AI tools.
5. Design approval workflows.
6. Prepare board-level risk reports.
7. Work with legal, security, data, and product teams.

Typical salary in 2026:

• US: $85k to $170k.
• Senior governance or risk roles: $160k to $250k.
• UK: £50k to £120k.
• EU: €55k to €130k.
• Consulting manager roles in Germany, Netherlands, or France: €90k to €150k.

Best fit if you:

• Have experience in risk, compliance, consulting, legal, privacy, data protection, audit, or public policy.
• Like structure and documentation.
• Can explain technical issues to non-technical leaders.

Useful knowledge:

• EU AI Act
• GDPR
• NIST AI Risk Management Framework
• ISO/IEC 42001
• model risk management
• vendor risk
• data protection impact assessments
• internal audit

This is probably the most realistic AI safety path for many career changers.

If you worked in privacy, compliance, financial risk, legal operations, internal audit, or consulting, you may already have half the skills.

5. AI policy specialist

AI policy specialists work on laws, standards, public research, government strategy, and public interest issues.

You might work in a think tank, government agency, civil society group, corporate policy team, or international organization.

Employers include:

• European Commission
• OECD
• United Nations agencies
• UK AI Safety Institute
• US AI Safety Institute
• RAND
• Brookings Institution
• Center for Democracy and Technology
• Ada Lovelace Institute
• OpenAI policy team
• Anthropic policy team
• Google public policy
• Microsoft responsible AI team

Common tasks:

1. Analyze AI laws and proposals.
2. Write policy briefs.
3. Meet with regulators, researchers, and industry teams.
4. Track risks in areas like biosecurity, elections, labor, privacy, and national security.
5. Help design standards and evaluation requirements.
6. Explain technical AI issues to policymakers.

Typical salary in 2026:

• US think tanks: $65k to $140k.
• US corporate policy: $140k to $260k.
• Federal roles: often $80k to $180k, depending on grade.
• UK: £40k to £110k.
• Brussels EU policy roles: €45k to €100k, more in corporate affairs.
• Corporate EU public policy: €90k to €180k.

Best fit if you:

• Write clearly.
• Understand politics and regulation.
• Can talk to technical and non-technical people.
• Care about public impact.
• Are comfortable with slow processes and many meetings.

Helpful backgrounds:

• public policy
• law
• economics
• international relations
• security studies
• data protection
• technology policy
• political science

You do not need to code for many policy roles, but basic AI literacy helps a lot.

Take one practical machine learning course, learn how LLMs work, and read safety reports from OpenAI, Anthropic, Google DeepMind, and METR.

6. AI ethics and responsible AI specialist

Responsible AI roles focus on fairness, accountability, transparency, privacy, explainability, and social impact.

These jobs became common before the current AI safety boom, especially at companies using AI in hiring, lending, insurance, healthcare, policing, and education.

Employers include:

• Microsoft Responsible AI
• Google Responsible AI
• IBM
• Salesforce
• Workday
• LinkedIn
• ServiceNow
• SAP
• Oracle
• banks and insurers
• HR tech companies
• healthcare technology firms

Common tasks:

1. Review AI systems for bias and fairness.
2. Create responsible AI guidelines.
3. Run impact assessments.
4. Work with legal and DEI teams.
5. Document model limitations.
6. Train product teams on safe AI usage.
7. Help customers understand AI risks.

Typical salary in 2026:

• US: $95k to $190k.
• Senior corporate roles: $170k to $280k total compensation.
• UK: £55k to £130k.
• EU: €60k to €140k.
• Big tech roles in Dublin, Amsterdam, Munich, or Paris can go higher.

Best fit if you:

• Care about social impact and user harm.
• Have experience in data, policy, law, product, UX research, or compliance.
• Can manage disagreement between teams.

Skills to build:

• bias testing
• fairness metrics
• impact assessments
• privacy basics
• explainability
• stakeholder interviews
• responsible AI frameworks
• documentation

One warning: some responsible AI jobs are vague.

Before accepting an offer, ask what power the team actually has. Can they block a launch? Can they require changes? Or are they just writing nice slide decks after decisions are already made?

7. Model evaluation specialist

Model evaluation, often called evals, is one of the hottest AI safety areas in 2026.

Evals teams design tests to understand what models can do, where they fail, and whether new versions are safer or riskier.

Employers include:

• OpenAI
• Anthropic
• Google DeepMind
• METR
• Scale AI
• Surge AI
• Turing
• Appen
• AI startups and consultancies

Common tasks:

1. Create benchmark tasks.
2. Measure model performance on safety topics.
3. Test dangerous capabilities.
4. Analyze failure patterns.
5. Build datasets.
6. Compare model versions.
7. Work with domain experts in biology, cybersecurity, law, or finance.

Typical salary in 2026:

• US: $90k to $190k.
• Technical evals roles: $140k to $280k.
• UK: £50k to £130k.
• EU: €55k to €140k.
• Contractor roles vary a lot, sometimes $30 to $100 per hour.

Best fit if you:

• Like tests, rubrics, and clear measurement.
• Can write precise instructions.
• Have domain expertise.
• Are analytical and detail-oriented.

Good backgrounds:

• data science
• QA
• academic research
• cybersecurity
• biology
• law
• linguistics
• education assessment
• technical writing

This is a great path if you are not ready for pure ML research but want to work close to frontier models.

Entry-level AI safety jobs: what can you realistically get?#

Let’s be honest. Entry-level AI safety can be competitive.

A lot of people want to work on meaningful AI problems. Many applicants have strong degrees, famous internships, or previous big tech experience.

But there are still realistic entry points.

Look for titles like:

1. AI Safety Analyst
2. Responsible AI Associate
3. AI Governance Analyst
4. Junior AI Risk Consultant
5. Model Evaluation Analyst
6. Trust and Safety AI Specialist
7. AI Policy Research Assistant
8. Technical AI Safety Intern
9. AI Red Team Analyst
10. Machine Learning Evaluation Associate

Entry-level salary ranges in 2026:

• US: $65k to $120k.
• High-cost tech hubs: $90k to $150k.
• UK: £32k to £60k.
• EU: €35k to €70k.
• Germany and Netherlands: €45k to €75k.
• France, Spain, Italy: often lower, around €35k to €60k.
• Remote contractor eval roles: can range from $25 to $80 per hour.

Best first jobs if you lack experience:

• AI governance analyst at a consultancy.
• Model evaluation contractor or analyst.
• Trust and safety role involving AI content.
• Data analyst role on an AI product team.
• Junior GRC role with AI risk exposure.
• Research assistant at a think tank.
• QA analyst for AI products.
• Cybersecurity analyst moving toward AI red teaming.

Do not only apply to “AI safety researcher” jobs at Anthropic and DeepMind. That is like deciding your first football trial must be Real Madrid.

Apply where you can get close to the work.

Skills you need for AI safety in 2026#

You do not need every skill below. Pick based on your target path.

Technical skills

If you want engineering, research, evals, or red teaming, focus on:

1. Python.
2. Basic machine learning.
3. LLM concepts, including tokens, context windows, fine-tuning, embeddings, and retrieval.
4. PyTorch basics.
5. Data analysis with pandas and SQL.
6. Experiment tracking.
7. Cloud basics.
8. APIs.
9. Security basics for AI agents and prompt injection.
10. Evaluation design.

A good starter project:

• Build a small eval suite for an open model from Hugging Face.
• Test it for bias, jailbreaks, factual errors, or unsafe instructions.
• Publish the code on GitHub.
• Write a short report explaining your method and findings.

That project alone can make your resume more believable.

Policy and governance skills

If you want non-coding AI safety roles, build:

1. AI regulation knowledge.
2. Risk assessment skills.
3. Writing.
4. Stakeholder management.
5. Vendor review.
6. Audit documentation.
7. Privacy and data protection knowledge.
8. Basic technical literacy.
9. Presentation skills.
10. Business judgment.

Good starter project:

• Pick a real AI product, such as ChatGPT Enterprise, Microsoft Copilot, or an AI hiring tool.
• Write a risk assessment.
• Include use cases, possible harms, controls, monitoring, and legal issues.
• Summarize it in a two-page memo.

This gives you something to talk about in interviews besides “I am passionate about AI ethics,” which hiring managers hear all day.

Soft skills that matter more than people admit

AI safety work involves uncertainty, politics, and tradeoffs.

You need to be good at:

• Saying “we do not know yet.”
• Explaining risk without sounding dramatic.
• Working with product teams that want to ship quickly.
• Writing clearly.
• Giving bad news calmly.
• Changing your mind when evidence changes.
• Asking basic questions without ego.
• Prioritizing risks instead of treating every issue as equally urgent.

The best people in this field are not just smart. They are trusted.

Best degrees and backgrounds for AI safety#

There is no single required degree.

Common degrees include:

1. Computer science.
2. Machine learning or AI.
3. Data science.
4. Cybersecurity.
5. Mathematics or statistics.
6. Public policy.
7. Law.
8. Philosophy.
9. Economics.
10. Political science.
11. Cognitive science.
12. Biology, especially for biosecurity-related work.

If you are still in university, try to combine one technical area with one policy or safety area.

Strong combinations:

• CS plus public policy.
• Data science plus ethics.
• Cybersecurity plus machine learning.
• Law plus technology regulation.
• Biology plus AI risk.
• Economics plus AI governance.
• Statistics plus model evaluation.

If you already graduated, do not panic. Hiring managers care more about proof of skill than your exact degree, especially outside pure research.

How to break into AI safety from common careers#

If you are a software engineer

You are in a strong position.

Do this:

1. Learn LLM application architecture.
2. Build evals and monitoring tools.
3. Study prompt injection and AI security.
4. Contribute to open-source AI safety tools.
5. Apply for AI safety engineer, evals engineer, or AI security roles.

Your resume should highlight reliability, testing, security, data pipelines, and production systems.

If you are a data analyst or data scientist

You can move into evals, responsible AI, model monitoring, or governance.

Do this:

1. Learn fairness metrics and model evaluation.
2. Build a bias audit project.
3. Practice writing technical findings for non-technical readers.
4. Apply to model evaluation, responsible AI, or AI risk roles.

Your advantage is measurement. Safety teams need people who can turn fuzzy concerns into evidence.

If you are in cybersecurity

AI red teaming is a natural move.

Do this:

1. Learn OWASP Top 10 for LLM Applications.
2. Study prompt injection and agent security.
3. Build demos of AI attack scenarios.
4. Apply to AI red team, AI security, or adversarial testing jobs.

Your security mindset is valuable. Just add AI-specific knowledge.

If you are in law, compliance, or privacy

Target AI governance.

Do this:

1. Study the EU AI Act and NIST AI RMF.
2. Learn how AI systems are developed and deployed.
3. Create sample AI impact assessments.
4. Apply to AI governance, responsible AI, AI compliance, or tech policy roles.

Your ability to manage rules and risk is already useful.

If you are in policy or research

Target AI policy, governance, or research assistant roles.

Do this:

1. Build technical AI literacy.
2. Read reports from frontier labs and policy institutes.
3. Write public memos or blog posts.
4. Network with AI policy groups in London, Brussels, Washington DC, San Francisco, and Berlin.

Writing samples matter a lot here.

Where to find AI safety jobs in 2026#

Use normal job boards, but do not stop there.

Check:

1. Company career pages:

   • OpenAI
   • Anthropic
   • Google DeepMind
   • Microsoft
   • Meta
   • Mistral AI
   • Cohere
   • Hugging Face
   • Scale AI
   • Databricks

2. Nonprofits and research groups:

   • METR
   • Redwood Research
   • Center for AI Safety
   • RAND
   • Ada Lovelace Institute
   • Center for Democracy and Technology

3. Government and policy:

   • USAJOBS
   • European Commission jobs
   • UK Civil Service Jobs
   • OECD careers
   • national AI safety institutes

4. Specialist boards and communities:

   • 80,000 Hours job board
   • EA Forum job posts
   • AI Safety Jobs
   • Work in AI Safety communities
   • LinkedIn
   • Wellfound for startups

Set alerts for:

• AI safety
• responsible AI
• AI governance
• AI risk
• model evaluation
• LLM evaluation
• AI red team
• AI assurance
• machine learning safety
• AI policy
• trustworthy AI

A boring tip that works: search for “AI risk” and “model evaluation,” not just “AI safety.” Many companies use different labels.

How to make your resume stand out#

Your resume needs to show that you can do the specific job, not just that you care about the topic.

Add a skills section with the right keywords

For technical roles, include:

• Python
• PyTorch
• LLM evaluation
• model monitoring
• prompt injection testing
• red teaming
• data analysis
• SQL
• APIs
• cloud tools
• safety benchmarks

For governance roles, include:

• EU AI Act
• NIST AI RMF
• ISO/IEC 42001
• AI risk assessment
• model governance
• privacy impact assessment
• vendor risk
• audit documentation
• responsible AI
• stakeholder management

Use proof, not vibes

Weak bullet:

• Interested in AI safety and responsible technology.

Better bullet:

• Built a Python evaluation suite testing 5 open-source LLMs across 120 harmful instruction prompts, identified 18 high-risk failure patterns, and wrote mitigation recommendations.

Weak bullet:

• Worked on AI governance.

Better bullet:

• Created an AI risk intake process for 14 internal use cases, mapping systems to EU AI Act risk categories and defining approval steps with legal, security, and data teams.

That is the difference between “nice person” and “interview this person.”

Interview questions you might get#

For technical AI safety roles:

1. How would you design an evaluation for a model’s cyber misuse risk?
2. What is prompt injection, and how would you test for it?
3. How do you know if a safety mitigation actually works?
4. Explain a recent AI safety paper or report.
5. How would you monitor a deployed LLM product?
6. What tradeoff exists between helpfulness and harmlessness?

For governance and policy roles:

1. How would you classify an AI system under the EU AI Act?
2. What makes an AI use case high risk?
3. How would you design an AI risk management process?
4. How should a company handle third-party AI vendors?
5. What should executives know before approving an AI product?
6. How do you communicate AI risk without causing panic?

For red team roles:

1. Show how you would test a chatbot for jailbreaks.
2. What is the difference between a vulnerability and expected model behavior?
3. How would you document a prompt injection issue?
4. How do you prioritize red team findings?
5. What AI agent risks worry you most?

Prepare with examples. Hiring teams love candidates who can explain messy tradeoffs clearly.

Is AI safety a stable career?#

Mostly yes, but with caveats.

The field is growing because AI risk is not going away. Regulation, security concerns, public pressure, and enterprise adoption all push companies to hire safety people.

But some roles are more stable than others.

More stable:

• AI governance
• AI risk and compliance
• AI security
• model monitoring
• responsible AI in regulated industries
• policy roles linked to regulation

More volatile:

• early-stage nonprofit research jobs
• grant-funded roles
• vague ethics roles with no business owner
• startups with unclear revenue
• pure research roles tied to changing lab priorities

If you want stability, look at banks, insurers, healthcare firms, governments, consultancies, and large tech companies.

If you want frontier work and can tolerate uncertainty, look at labs and research nonprofits.

The best AI safety role for you#

Here is the quick matching guide.

Choose technical AI safety research if:

• You love ML research.
• You have strong math and coding.
• You may want a PhD or already have one.

Choose AI safety engineering if:

• You are a software or ML engineer.
• You like building production systems.
• You want strong pay and practical impact.

Choose AI red teaming if:

• You like breaking things.
• You have security, QA, or trust and safety experience.
• You enjoy adversarial testing.

Choose AI governance if:

• You come from risk, compliance, privacy, audit, law, or consulting.
• You like turning chaos into process.
• You want many job openings.

Choose AI policy if:

• You write well.
• You understand government or regulation.
• You care about public decisions.

Choose model evaluation if:

• You are analytical and detail-focused.
• You like testing and measurement.
• You want to work close to AI systems without necessarily being a research scientist.

Final thoughts: yes, you can build a career in AI safety#

AI safety is not one job. It is a cluster of careers around one big question: how do we build and deploy AI without causing avoidable harm?

In 2026, the field needs more than elite researchers. It needs engineers, analysts, auditors, red teamers, policy people, lawyers, product thinkers, security specialists, and clear writers.

Your next step is not to become an expert overnight. Pick one lane, build one relevant project, rewrite your resume around that lane, and apply to roles that match your current strengths.

Before you send applications, run your resume through JobRise’s free ATS checker. It can help you catch missing keywords, weak bullets, and formatting issues before a recruiter ever sees it: https://jobrise.io/en/free-ats-checker/