Top AI Careers: Paths, Skills, and Salaries

Table of Contents

1. Key takeaways
2. Who this guide is for
3. Quick overview: AI career paths vs. job titles
4. Top AI focus areas
5. Weekend projects to start your AI portfolio
6. Why an advanced degree is an advantage in the AI field
7. Launching your AI-focused career

Artificial intelligence is showing up in every industry—from finance and healthcare to retail and media. If you already write code and work with data, you may be wondering which AI roles fit your strengths and how to prepare. This guide outlines the most common AI career paths, the job titles you’ll see in postings, the skills to build next, and typical U.S. salary ranges so you can make an informed plan.

Editor’s note: This guide is updated for 2025 to reflect today’s AI job market and language models.

There are six top “lanes” in AI—job categories with strong demand, good pay, and skills that transfer across industries. Remember RAPIDS and you won’t forget them: Research, Applied Engineering, Platforms (machine learning operations (MLOPs)), Insights (data), Direction (product and solutions), and Safety (governance).

Who this guide is for:

• Early-career software engineers and data-adjacent professionals (one to five years’ experience)
• Those comfortable with Python/Java/JavaScript and basic cloud or data tooling
• Anyone pivoting into AI from a non-AI role who wants clear role options, credible salary ranges, and practical next steps

No matter your starting point, there’s a path to the skills and roles outlined here.

Quick overview: AI career paths vs. job titles (and a growing demand)

In the AI realm, you’ll hear about both focus areas (disciplines) and specific job titles. The sections below each start with a focus area to match how teams are organized, and then list the titles you’ll apply for.

As adoption of artificial intelligence accelerates across industries, so does the career potential for those with skills, experience, and credentials to steer, develop, apply, and govern it. The World Economic Forum’s “Future of Jobs 2025” report projects 170 million new jobs and 92 million displaced by 2030, with multiple drivers at play (technology, demographics, economy, and the transition to green energy). Within that mix, AI and data processing alone are expected to create 11 million jobs and replace 9 million. Employers also expect AI to transform their business and report that nearly 40% of job skills will change, underscoring the need for upskilling.

As of 2025, those with the necessary combination of skills are hard to come by. “The job market is really huge in [AI], but a lot of people aren’t trained for it,” says Bethany Edmunds, associate dean and lead faculty at Northeastern’s Khoury College of Computer Science. That will result in an above-average job outlook for those who do have the skills needed to work in this area. Becoming the person who advances the tech, ships systems, runs the platform, turns data into decisions, steers outcomes, and safeguards use is a scarce, high-leverage differentiator for job seekers.

Top AI focus areas (and the roles within them)

Top focus areas in artificial intelligence include research and discovery, applied engineering, platforms (MLOps), insights (data), direction (product/solutions), and safety (governance). Top job titles include machine learning engineer, AI research scientist, data scientist, AI product manager, robotics engineer, NLP engineer, and computer vision engineer, among others.

In this article, “top” designates focus areas and roles with (1) strong multi-year hiring demand, (2) high pay or influence on product or policy, and (3) transferable skills across industries. Here’s a quick look at the landscape:

• Research (R): You invest in or improve methods and models. If you like papers, proofs, and careful experiments, this is home.

• Applied engineering (A): You ship user-facing features such as copilots, search, RAG, and classifiers that people touch every day.

• Platforms (MLOps) (P): You make AI reliable at scale through data pipelines, deployment, monitoring, and cost control.

• Insights (data science and analytics) (I): You turn messy data into decisions. You design experiments and forecast impact.

• Direction (product and solutions) (D): You decide what to build, why it matters, and prove value with compelling demos.

• Safety (governance) (S): You red-team models, document risk, and align with standards so AI is safe and compliant.

Let’s explore each area in more depth.

1. Research and discovery: Applied and research scientists advance the frontier

Although many top AI careers explore the application or function of AI technology, computer science and artificial intelligence research is more about discovering ways to advance the technology itself. “There will always be somebody developing a faster machine,” Edmunds says. “There’s always going to be somebody pushing the edge, and that [person] will be a computer scientist.”

Research scientists push theory, publish, and prototype. Applied scientists convert new methods into production impact. Many teams blend both, especially on LLMs and multimodal models.

What you do: Create new methods and models, publish results, and unlock capabilities others build on.

Titles: Research scientist, applied scientist

You’ll like this if: You enjoy proofs, papers, reading, writing, math, and careful experiments.

Core skills and tools: Strong math (linear algebra, optimization, probability), deep learning, retrieval, evaluation design, and fast prototyping. Papers-to-code, literature reviews, and reproducibility are expected foundational abilities.

Compensation and outlook: The U.S. Bureau of Labor Statistics (BLS) lists research scientists at a $140,910 median salary, with “much faster than average” growth. Compensation at frontier AI labs and major tech companies can greatly exceed standard salary bands. Job seekers should consider total compensation, freedom to publish, and compute access.

Projects recruiters like:

• Novel evaluation for hallucination reduction
• Domain adaptation for medical images
• Retrieval-augmented generation with safety filters
• Reinforcement learning for ranking or control

Your path forward: If a research-heavy role is your desired track, consider graduate programs like Northeastern University’s interdisciplinary MS in Artificial Intelligence or MS in Computer Science with AI/Data Science tracks. If you are changing fields and lack a computer science background, Northeastern’s Align MS in Computer Science program will help you earn the necessary computer science core before specializing.

2. Applied (product-facing) engineering: Shipping user-facing features

Applied engineering is the hands-on path that turns research and models into dependable features. This is where users feel the value: latency, accuracy, and UX get tuned in production, feedback loops expose gaps, and trust is earned release by release.

The need spans SaaS, fintech, health, education, and industrial software. As AI becomes a default expectation in tools, teams will keep hiring engineers who can wire models to workflows, control cost and speed, and improve outcomes. The tech stack will evolve, but the work will endure.

What you do: Build user-visible features with LLMs/ML (RAG, copilots, classifiers, search).

Titles: AI/ML engineer, Gen-AI engineer, AI product engineer

You’ll like this if: You want users to interact with what you make.

Core skills and tools: Python, PyTorch/TensorFlow, experiment tracking (MLflow/W&B), data versioning, feature stores, distributed training (Ray), vector databases and RAG, evaluation/guardrails, and cloud (AWS/GCP/Azure). Strong code quality (tests, CI/CD) and an understanding of data leakage, drift, and bias are expected.

Compensation and outlook: BLS groups senior R&D roles under the title of computer and information research scientists with a $140,910 median salary (as of May 2024) and 20% growth through 2034. Industry offers for machine learning engineers can be $200K to $400K+ in total compensation, depending on level, with higher outliers.

Projects recruiters like:

• End-to-end LLM app with offline/online eval and A/B tests
• Fine-tuning (LoRA/QLoRA) with clear data governance
• Computer-vision pipeline with robust metrics
• Forecasting with causal or probabilistic methods

Your path forward: Start with a Graduate Certificate in AI Applications, which stacks into an MS in Artificial Intelligence program. Once in the master’s program, choose concentrations best-aligned with your goals (e.g., machine learning, computer vision) and add co-op experience. For those seeking a graduate program built to connect technical skills with industry application, Northeastern also offers the Master of Professional Studies in Applied Artificial Intelligence, designed to prepare professionals to develop, deploy, and manage AI systems in real-world settings.

3. Platforms and MLOps: Making AI reliable at scale

Platforms and MLOps professionals build the runway for data and models to move from notebooks to stable services. These roles are crucial because reliability, reproducibility, and safety depend on versioned data, controlled releases, and clear ownership of uptime, latency, and cost.

Every industry that runs AI at scale needs this discipline: streaming data in retail and media, regulated records in finance and health, sensitive student data in education, and telemetry in the public sector. Model types will change, but the pipelines, deployment, monitoring, and budgets ensure the focus stays essential.

What you do: Make models production-grade, including data/feature pipelines, deployment, monitoring, and cost.

Titles: ML engineer (MLOps), ML platform engineer, AI cloud architect

You’ll like this if: You enjoy systems thinking, reliability engineering, automation, and tuning performance and/or cost.

Core skills and tools: Python/Go, containers, Kubernetes, cloud services (AWS/GCP/Azure), Spark/Beam, Kafka, feature stores, experiment tracking and registries, vector databases, observability (traces, metrics, logs), GPU scheduling and cost allocation.

Compensation and outlook: Aligning this role to computer and information systems managers—a common endpoint for platform leadership—the BLS reports a $171,200 median salary (as of May 2024) with 15% projected growth between 2024 and 2034. A likely national base-pay range spans the 10th to 90th percentiles: $104,450 to $239,200. Individual contributors without direct managerial responsibility are likely to earn salaries closest to the median, while those in managerial roles will see salaries approaching the higher end of this band.

Projects recruiters like:

• Unified train/evaluate/release pipeline with automated model promotion and rollback
• Autoscaling, batched inference that hits strict latency SLOs under spiky load
• GPU cost optimization via caching, distillation, and right-sized hardware
• End-to-end lineage/metadata with audit-ready reports

Your path forward: Consider Northeastern’s MS in AI or MS in Data Science with systems and distributed computing electives. For career changers, the Align MS in Computer Science program builds CS skill depth before you specialize in platforms.

4. Insights (data science and analytics): Turning messy data into decisions

Insights and data functions turn messy events and records into decisions. They set the metrics that define quality, design experiments, explain variance, and link model behavior to business outcomes people care about.

Data analysts need to have a solid understanding of the data itself—including the practices of managing, analyzing, and storing it—as well as the skills needed to effectively communicate findings through visualization. “It’s one thing to just have the data, but to be able to actually report on it to other people is vital,” Edmunds says.

Because choices and tradeoffs exist in every sector, this function persists across economic cycles. As privacy rules tighten and AI features ship faster, organizations need analysts who can use first-party data well, separate correlation from causation, and forecast impact with clear confidence.

What you do: Turn data into decisions, design experiments, and forecast impact and risk.

Titles: Data scientist, decision scientist, analytics engineer, quantitative analyst

You’ll like this if: You enjoy statistics, asking sharp questions, and making complex data legible for decision-makers.

Core skills and tools: SQL, Python, experimentation platforms, causal inference, forecasting, regression and classification, Bayesian methods, dbt (Data Build Tool) and warehouse tech, BI tools, uplift/propensity modeling, LTV and attribution, and drift monitoring.

Compensation and outlook: As a benchmark, the BLS lists data scientists at a $112,900 median salary (as of May 2024) with 34% projected growth between 2024 and 2034. A likely national base-pay range using the 10th-90th percentiles is $63,650–$194,410. Senior roles in tech and regulated industries often add equity or larger bonuses.

Projects recruiters like:

• Full-funnel experiment that isolates causal lift and leads to a scaled rollout
• Uplift/propensity model that improves targeting efficiency
• Marketing mix or resource allocation model tied to budget decisions
• LLM feature evaluation framework with accepted success metrics (e.g., task success, time saved, satisfaction)

Your path forward: Consider Northeastern’s MS in Data Science or MS in AI with analytics-focused electives. For non-CS backgrounds combining stats with computing, the Align MS in Computer Science program is a strong bridge.

5. Direction (AI product, solutions, presales): Decide what to build

Those in direction, product management, and solutions decide what to build and why. They shape problems, prioritize bets, write crisp product requirement documents (PRDs), and create demos that make the value obvious to users, executives, and partners.

The role is durable because adoption hinges on translation between customer needs and shifting AI capabilities. In B2B software, healthcare, finance, education, and the public sector, these leaders align roadmaps, risks, and ROI, and they will be needed as long as organizations buy, integrate, and scale AI.

What you do: Decide what to build and why, write PRDs, and craft memos that win decisions and deals.

Titles: AI product manager, solutions architect, sales engineer (AI), AI strategist

You’ll like this if: You think in terms of customer journeys and enjoy problem framing, storytelling, scoping, and hands-on prototyping to de-risk bets.

Core skills and tools: Customer interviews, jobs-to-be-done and problem statements, prompt/workflow design, evaluation plans, market and competitive analysis, basic coding for demos, stakeholder alignment, and ROI modeling.

Compensation and outlook: The BLS shows computer and information systems managers at a $171,200 median salary (May 2024) with a 15% projected growth between 2023 and 2034. That suggests a national 10th-90th percentile base-pay range around $104,400–$239,200 for leadership-track roles in this area. Solutions roles may include variable pay, such as commissions and bonuses.

Projects recruiters like:

• Narrative demo that shows step-by-step workflow improvement with measurable outcomes
• Business case linking model metrics to KPIs and a clear evaluation plan
• PRD that integrates safety requirements, offline/online evaluations, and rollout strategy

Your path forward: Consider an MS in AI or MS in CS (with an AI track) and add product-focused electives or studio experience. For job switchers who need to gain technical depth first, the Align MSCS program is an effective on-ramp.

6. Safety and governance: Finding failures before customers do

Those in AI safety and governance find failure modes before customers do and reduce harm when things go wrong. This matters because trust, compliance, and reputation rely on documented risks, tested mitigations, and accountable processes.

Need is rising in every domain that touches sensitive data or high-impact decisions, from banking and health to HR, education, and government. Regulations and standards will evolve, but red teaming, evaluations, reporting, and incident playbooks make this work a permanent part of shipping AI.

What you do: Red-team models, document risk, align to frameworks (NIST AI RMF, ISO/IEC 42001), ensure compliance, and establish human-in-the-loop workflows.

Titles: AI safety/Red-team engineer, AI governance lead, AI policy lead

You’ll like this if: You care about reliability, fairness, and real-world harms. You enjoy designing tests, writing clear documentation, and partnering across legal, security, and product.

Core skills and tools: Evaluation design, adversarial testing, policy and compliance frameworks, data governance, PII handling, audit logging, access controls, secure deployment patterns, privacy-enhancing techniques, and incident postmortems.

Compensation and outlook: Using information security analysts as a proxy for safety-focused work, the BLS reports a $124,910 median salary (as of May 2024) with 29% projected growth between 2024 and 2034. A likely national base-pay range spans $69,660—$186,420 for the 10th-90th percentiles, with higher totals in regulated industries.

Projects recruiters like:

• Red-team playbooks and tooling that uncover concrete failure modes
• Safety evaluation suite wired into CI with release-blocking thresholds
• Standardized model/system cards and a launch risk review template
• Incident response tabletop with measurable improvements after remediation

Your path forward: Consider Northeastern’s MS in AI with ethics and policy coursework, and pair technical depth with governance-focused electives. For career changers who first need to gain CS fundamentals, the Align MS in Computer Science program provides foundational skills and knowledge before you specialize in safety.

Weekend projects to start your AI portfolio

Start by picking one of the six focus areas above. If one clearly fits, choose it. If two seem right, choose the lane where you can build a small, working weekend project with your existing skills. A simple end-to-end demo or prototype confirms you’re in the right lane and gives you a real piece for your portfolio that you can share with an enrollment coach or include in application materials should you apply to a graduate-level program. No need to wait—build something you can show this weekend.

Starter projects by focus area:

• Research (R): Reproduce one result from a recent, credible paper on a small dataset. Write a one-page note on what you tried, what changed, and why it helped.

• Applied Engineering (A): Build a tiny assistant for one task (for example, answering FAQs for a product). Add automatic checks for answer quality, response time, and cost per use.

• Platforms (P): Package a model so it runs the same everywhere, set up automatic tests, do a small trial release, and add a basic alert if inputs or outputs start to look different.

• Insights (I): Pick a business metric (e.g., conversion rate), run a simple test, and deliver a two-page decision memo detailing what you found, the tradeoffs, and a clear recommendation.

• Direction (D): Write a one-page product brief for a generative AI feature and record a three-minute click-through demo tied to a clear ROI hypothesis.

• Safety (S): Stress-test a demo AI app for common risks such as prompt injection, personal-data leaks, or harmful content. Log findings, propose fixes, and map them to a known framework.

How do you know if what you shipped is “good enough” to keep going? Use this five-point criteria:

1. Quality: Does it do the job well on typical examples? How do you know?
2. Latency: Is it fast enough for when and where it’s used? Set a target.
3. Cost: What does each run roughly cost, and what stays predictable as usage grows?
4. Safety: What could go wrong, and what simple guardrails are in place?
5. Adoption: Have real users tried it? What did they say, and what did you change?

If you’ve said “yes” to most of those, you’ve got a portfolio-worthy project. Ship it, learn, and iterate. If you’re still torn between paths, ask yourself one question: Do I want to invent new methods or deliver reliable outcomes? “Methods” points toward research, while choosing “outcomes” points toward applied Engineering, platforms, insights, direction, or safety. Either way, start with one weekend project before you commit to a long-term plan. Do this and you’ll have launched the first step in your AI career.

Why an advanced degree is an advantage in the AI field

Those looking to either break into or advance their careers in artificial intelligence can benefit from obtaining an AI-related master’s degree at a top university like Northeastern.

While you are likely capable of self-teaching, a degree multiplies everything you’re already doing in four ways that a DIY route can’t reliably match.

First, it provides a signal and access. Whether we like it or not, hiring processes still use degrees as a filter. Graduating from a strong master’s program implies you are trained, vetted, and durable under pressure. It opens doors to research labs, safety and governance roles, and enterprise teams that need to meet compliance needs. Self-study will rarely get you past those gates.

Second, it accelerates your progress through expert feedback. A good program compresses learning with structured sequences, thorough critique, and faculty who have already made the mistakes you are about to make. That’s months saved on every project. Your cohort becomes an always-on review panel and accountability loop: you ship more, you ship better, and you do it on a schedule.

Third, it expands your network. Schools have pipelines you can’t get access to through Google and LinkedIn requests alone. They provide you with internship slots, capstone sponsors, alumni in decision-making seats, and talks with people who can actually hire you. One warm introduction from faculty can beat 50 cold emails. Your portfolio still matters, but a formal education can help to ensure it’s being seen by the right eyes.

Fourth, it provides resources you won’t get alone. The university might provide you with compute credits, restricted datasets, red-team labs, IRB processes, legal reviews, and secure environments. Programs with serious partners give you the tools and contexts that make your portfolio look (and actually be) enterprise-grade.

So no, you shouldn’t stop tinkering with DIY projects and go study. You should do both. The combination is the fastest, safest, and most reliable path to a real AI role with real responsibility.

Launching your AI-focused career

With a foundational understanding of the AI industry and its top career paths in hand, you’re ready to take the next step. Follow this simple process and you’ll be jumpstarting your career in no time:

1. Pick your preferred area of focus before you apply. Then, shortlist the program best-suited for your interests, based on the program curriculum and required courses, and apply. Be prepared to tell admissions or your enrollment coach exactly what you plan to build and why. You’ll be steered in the right direction from day one.
2. Book a call or info session so your next action is on the calendar.
3. Turn every course into a portfolio project. For each class, find one shippable deliverable, a short demo video, and a README scored by the same five-metric rubric we set (quality, latency, cost, safety, adoption).
4. Choose a capstone with an external sponsor. That gives you real data, real constraints, and a real recommender who can open interview doors.
5. Use student experiences as leverage. If you can, secure a role as a teaching assistant to earn communication and leadership experience. Become a research assistant for research credibility, and secure an industry co-op to ship something that actually gets used.
6. Publish and present. Don’t let work die in the LMS. Open-source what you can, write the case note, present at a meetup, and let the school’s brand amplify your reach.

The bottom line is that while it’s critical to keep your hands in code and your work in front of users, a graduate-level program is the engine to supply the signal, acceleration, network, and resources needed for success. The combination doesn’t just help you learn AI—it helps you get hired for the AI you can already do.