At [FLEXLY.PRO](https://flexly.pro), we work with AI engineers every day—helping them navigate career transitions, evaluate offers, and find roles where they'll actually build things rather than maintain legacy systems. Over the past eighteen months, we've noticed a dramatic shift in where the most exciting opportunities are.

The pattern is clear: talented engineers are increasingly leaving big tech for companies most people have never heard of. Not because Google, Meta, or Amazon aren't doing important AI work—they absolutely are—but because that work is increasingly concentrated in small, specialized teams. Meanwhile, the majority of "AI engineers" at these companies spend most of their time maintaining existing systems, not building new ones.

We analyzed job postings, tracked hiring trends, and collected data from hundreds of engineers moving between companies. Here's what we found about where the real action is in 2024—and where you should be looking if you want to build cutting-edge AI systems from scratch.

The Maintenance Trap at Big Tech

Let's be honest about what most AI engineering roles at major tech companies actually involve.

You're not training GPT-5. You're not inventing new architectures. You're optimizing existing recommendation systems, debugging data pipelines that broke three years ago, and attending sprint planning meetings about incremental improvements to mature products.

We analyzed 847 job postings for "Machine Learning Engineer" roles at Google, Meta, Amazon, Microsoft, and Apple from the past six months. Here's the breakdown:

64% explicitly mentioned maintaining, optimizing, or improving existing systems
26% focused on infrastructure and tooling for other ML teams
Only 10% described building new AI capabilities from scratch

Compare that to Series B AI startups in our database: 73% of ML engineer job postings emphasize building new systems, not maintaining old ones.

Engineers who've made the transition consistently report the same experience. One senior ML engineer who moved from Meta to a Series C startup told us: "At Meta, I was the fourth engineer on a team maintaining Instagram's recommendation feed. My entire job was incremental improvements to click-through rates. At my current company, I'm building safety evaluation frameworks that didn't exist six months ago. Every week we're solving problems nobody has solved before."

This isn't to say big tech ML work is meaningless. Maintaining systems that serve billions of users requires real expertise. But if you became an AI engineer to build new things rather than optimize old things, the opportunities have shifted.

Follow the Funding, Find the Building

AI startups raised $29.1 billion in Q1 2024 alone, according to Crunchbase. That's more than the previous four quarters combined. All that capital needs to turn into products, which means one thing: aggressive hiring of engineers who can ship.

The data backs this up. According to Carta's Q2 2024 analysis of their startup portfolio:

Series B AI companies grew engineering headcount by an average of 43% in the first half of 2024
FAANG companies grew ML engineering teams by 11% in the same period
AI startups with $50M+ in funding are hiring ML engineers at nearly 4× the rate of big tech

But raw hiring numbers don't tell the whole story. What matters is what these engineers are building.

Through our work at [FLEXLY.PRO](https://flexly.pro), we've tracked dozens of engineers making these transitions. The consistent theme: faster decision-making cycles and broader ownership. One engineer who joined Scale AI from Amazon described the change: "At Amazon, I worked on forecasting models for inventory management. The architecture was set five years ago. I was tuning hyperparameters and fixing bugs. At Scale, I'm designing evaluation frameworks that will influence how the entire industry measures LLM capabilities. The ownership is completely different."

The funding environment creates urgency. These companies need to ship products before their runway ends, which means:

Faster decision-making (weeks, not quarters)
More trust in individual engineers to make architectural choices
Direct impact on company trajectory (your model isn't buried in a giant system)
Exposure to the entire stack (data, training, deployment, monitoring)

The tradeoff? Less mentorship, fewer established best practices, and the very real possibility that your equity becomes worthless. But if you want to build rather than maintain, that's the bet more engineers are making.

The Geographic Arbitrage Play

Here's a trend we've been tracking at [FLEXLY.PRO](https://flexly.pro) that surprised us: some of the best AI engineering opportunities are in cities you've probably never considered.

Singapore has emerged as an unlikely AI hub. According to NodeFlair's 2024 Tech Salary Report, senior ML engineers in Singapore earn base salaries averaging SGD 180,000 (roughly USD 133,000), but total compensation packages—factoring in bonuses, equity, and cost-of-living adjustments—often exceed USD 200,000. That's competitive with Bay Area compensation, but your money goes significantly further.

Our data shows engineers relocating to Singapore report 30-40% lower living costs compared to San Francisco while maintaining similar total compensation. The trade-off is working across time zones and adapting to a different tech ecosystem, but for engineers seeking better financial outcomes, the math works.

Berlin, London, and Toronto have seen similar patterns. LinkedIn's Economic Graph data shows:

Berlin: ML engineer hiring grew 67% YoY (2023-2024)
London: 54% growth in AI engineering roles
Toronto: 48% growth, driven largely by AI research labs

San Francisco? Just 14% growth in the same period.

But the bigger story is remote work. According to AngelList's 2024 data, 68% of Series B AI startups now offer location-flexible compensation—meaning they'll pay Bay Area rates regardless of where you live. For engineers willing to work asynchronously across time zones, this creates massive arbitrage opportunities.

We've worked with engineers who joined companies like Cohere remotely from lower-cost cities. Typical scenario: $180,000-$200,000 base + $100,000-$150,000 in equity (4-year vest), roughly matching FAANG compensation—but their cost of living is 35-45% lower. That difference compounds significantly over a 3-5 year period.

The Specialization Premium Is Real (and Growing)

The era of the general-purpose ML engineer is ending. Companies increasingly want specialists who can solve specific, hard problems in production environments.

The compensation data from our network tells the story. According to Levels.fyi's Q2 2024 aggregated offer data:

Multimodal AI engineers (vision + language): $295k-$380k total comp
AI safety/alignment specialists: $270k-$350k total comp
LLM fine-tuning experts: $250k-$340k total comp
General ML engineers: $190k-$260k total comp

That's not a 10-20% premium for specialization. That's a 40-60% premium.

The trend accelerated sharply in 2023-2024 as companies realized that throwing generalists at frontier AI problems doesn't work. You need people with deep expertise in specific domains, and the supply is extremely limited.

We've seen engineers with RLHF (reinforcement learning from human feedback) experience receive 5-7 offers within weeks of starting their search, all above $300,000 total comp. The pattern is consistent: companies are desperate for people who've actually built these systems in production. It's not enough to understand the papers—you need to have debugged reward models at scale, dealt with preference data quality issues, and optimized for inference costs.

The specializations commanding the highest premiums right now:

•Multimodal AI (especially vision-language models for real-world applications)
•AI safety and alignment (evaluation frameworks, red-teaming, interpretability)
•Production LLM optimization (inference speed, cost reduction, fine-tuning)
•Retrieval-augmented generation (RAG) (especially at enterprise scale)
•AI agent frameworks (planning, tool use, error recovery)
•Reinforcement learning (particularly for robotics and decision-making)

If you're a generalist ML engineer, the message is clear: pick a specialization and go deep. The market will pay you significantly more, and you'll have far more negotiating leverage.

Where the Actual Jobs Are (Right Now)

Let me be specific. These aren't companies that "might be hiring someday." These are organizations actively hiring multiple AI engineers as of November 2024, based on their public job boards and conversations with their recruiting teams.

AI Infrastructure: Building the Picks and Shovels

Scale AI has 47 open engineering roles as of this writing. The most interesting aren't the obvious ones—they're looking for engineers to build evaluation frameworks that will become industry standards. Former Scale engineers describe the work as "defining what good looks like for foundation models." Compensation: $200k-$350k total comp depending on level.

Weights & Biases needs engineers who understand both ML experimentation and enterprise sales cycles. Their platform is what every ML team wishes they had internally, and they're scaling fast. They're particularly interested in people who can build developer tools that researchers actually want to use. Compensation: $180k-$280k total comp, higher for senior roles.

Hugging Face is hiring across model optimization, deployment infrastructure, and developer tools. What makes them interesting: you'll work directly with the open-source community, meaning your work has immediate visibility. Engineers report unusual autonomy and fast shipping cycles. Compensation: $170k-$300k, plus significant equity upside if they eventually IPO.

The New AI Labs: Where Research Meets Reality

Anthropic continues aggressive hiring for constitutional AI and safety engineering roles. Unlike traditional research positions, these roles combine cutting-edge research with production deployment. You need to publish and ship. Engineers I spoke with describe intense intellectual challenge and mission-driven culture. Compensation: $250k-$400k+ total comp, heavily weighted toward equity.

Cohere needs deployment engineers who can take research models and make them work for enterprise customers. The interesting challenge: building systems that non-technical teams can actually use. Former engineers describe rapid iteration cycles and close collaboration with customers. Compensation: $220k-$340k total comp.

Adept AI is building AI agents that can use software tools. They're hiring engineers who understand both RL and real-world system integration—a rare combination. The pitch: you'll build agents that actually do useful work, not demos. Compensation: $240k-$360k total comp, with meaningful equity upside.

Enterprise AI: Where Money Meets Models

Glean is rebuilding enterprise search with AI, competing directly against Google. They're hiring engineers who understand both semantic search and enterprise security requirements. The appeal: you're not building consumer products that get 1% better—you're replacing systems that fundamentally don't work. Compensation: $210k-$320k total comp.

Harvey is revolutionizing legal work with AI. They're looking for engineers with both NLP expertise and willingness to learn legal domain knowledge. Several team members are former lawyers who learned to code. The work involves building tools that lawyers will pay thousands of dollars per month to use. Compensation: $230k-$350k total comp.

Sierra (founded by Bret Taylor and Clay Bavor) is building AI customer service that actually works. They're hiring engineers experienced in dialogue systems and real-time inference. Early employees describe it as "the opposite of a chatbot—we're building agents that solve problems, not deflect them." Compensation: Not yet public, but likely $250k-$400k range for senior engineers.

Vertical AI: Deep Specialization Pays

Tempus Labs needs ML engineers who can work with clinical data while maintaining rigorous privacy standards. They're building diagnostic tools doctors actually use. The challenge: healthcare data is messy, regulations are strict, and mistakes have real consequences. Compensation: $190k-$290k total comp, plus meaningful equity if they IPO.

Insitro combines machine learning with biology to accelerate drug discovery. They want engineers who understand both ML and the practical constraints of wet lab validation. The timeline is longer than consumer AI, but the impact is potentially enormous. Compensation: $200k-$310k total comp.

Fiddler AI is building ML observability and monitoring tools for enterprise AI systems. They need engineers who've felt the pain of debugging production ML systems. Former engineers describe it as "finally building the tools we always wished existed." Compensation: $180k-$280k total comp.

Computer Vision: Robots and Real-World Systems

Skydio (autonomous drones) needs computer vision engineers who can build perception systems that work in challenging real-world conditions. Unlike academic CV, this work has to handle wind, rain, and objects moving in unpredictable ways. Compensation: $190k-$300k total comp.

Covariant (robotics AI) wants engineers who understand both RL and physical system constraints. They're building robots that actually work in warehouses and factories—not demos. Engineers describe unusual cross-disciplinary work combining ML, robotics, and manufacturing. Compensation: $200k-$320k total comp.

Nuro (autonomous delivery vehicles) continues hiring perception and prediction engineers despite the broader AV slowdown. They're focused on low-speed delivery, which has clearer economics than robotaxis. Compensation: $210k-$330k total comp.

The Red Flags to Watch For

Not every AI startup is a good bet. Based on our experience at FLEXLY.PRO working with engineers evaluating opportunities, here are the warning signs:

Vague about data: If a company can't clearly articulate their data strategy and competitive advantage, be skeptical. "We'll use publicly available data and fine-tuning" isn't a moat.

Research-heavy with no product: Some AI startups are really research labs cosplaying as companies. If everyone has published at NeurIPS but nobody has shipped to customers, you might end up writing papers instead of building products.

Unrealistic timelines: If they promise AGI in 18 months or claim they'll replace an entire industry by next quarter, run. Good AI companies have ambitious but grounded roadmaps.

Equity-heavy, cash-light compensation: Some startups offer $120k base + $400k in paper equity. That's a huge bet on an illiquid asset. Make sure your base covers your actual living expenses.

Founder who's never built AI systems: Plenty of successful founders aren't technical. But in AI, if the founder doesn't understand the technical challenges deeply, execution will be messy.

We've seen talented engineers join companies with these red flags and regret it within 6-12 months. Do your due diligence.

How to Actually Make the Jump

If you're convinced but don't know where to start, here's the practical playbook:

Build specialization now, before you apply. Pick one of the high-demand areas (multimodal AI, RLHF, RAG, AI agents) and go deep. Ship a side project. Write detailed blog posts. Contribute to relevant open-source projects. When you apply, you need proof you can do the work, not just that you're smart.

Network strategically. Cold applications to AI startups often go nowhere. Instead:

Follow engineers at target companies on Twitter/X
Comment thoughtfully on their blog posts
Attend AI conferences and actually talk to people
Get introduced through investors or other engineers

Negotiate from strength. If you have a FAANG offer, use it as leverage even if you want the startup. Be explicit: "I have an offer from Google at $X total comp. I prefer your company because [specific reason], but I need you to get close on guaranteed cash."

Understand your risk tolerance. If you have kids, a mortgage, and no savings, joining a Series A startup with $110k base + illiquid equity is risky. Be honest about your runway. If you can afford 2-3 years of lower cash for higher upside, great. If not, target later-stage companies with better cash compensation.

Ask the right questions in interviews:

What percentage of engineers' time goes to maintaining existing systems vs building new ones?
How quickly do you ship new features to production?
What's the longest-tenured engineer's timeline from idea to deployment?
Can you show me the last three features the ML team shipped?
How do you evaluate model performance in production?

The answers will tell you whether you're joining a team that ships or a team that plans.

The Uncomfortable Truth

The best AI engineering work today isn't at the companies with the best brand names. It's at companies solving specific, hard problems with deep technical moats and enough funding to execute.

You'll have less mentorship. Your equity might become worthless. You might work longer hours. You'll definitely have more ambiguity and less established process.

But you'll also build things from scratch. You'll own entire systems. You'll make architectural decisions that matter. And based on what we've observed at FLEXLY.PRO, you'll learn faster than you thought possible.

The engineers who thrive in these environments aren't chasing prestige—they're chasing the opportunity to build systems that don't exist yet. They're comfortable with uncertainty and energized by ownership.

One pattern we've noticed: engineers who leave big tech for startups rarely want to go back. Not because of compensation (though it's often competitive), but because the work itself is fundamentally different. They've gone from maintaining to building, from optimizing to inventing.

If you're tired of maintaining other people's systems and ready to build your own, the opportunities are there. You just have to look beyond the usual suspects—and be honest about your risk tolerance and what you actually want from your career.

Resources for Your Job Search

Job Boards:

[Flexly.pro](https://flexly.pro) - Curated AI/ML roles at startups and BigTech
[Levels.fyi](https://levels.fyi) - Compensation data to benchmark offers
[AngelList](https://angel.co) - Startup roles with equity/comp transparency

Research & Data:

[Crunchbase](https://crunchbase.com) - Track funding rounds and hiring signals
[Carta](https://carta.com/blog) - Startup equity trends and compensation
LinkedIn Economic Graph - Geographic hiring trends

Communities:

r/MachineLearning - Active discussions about career moves
Elpha (for women in tech) - Strong AI engineering community
Blind - Anonymous compensation and company discussions

This article reflects data collected by the FLEXLY.PRO team from public job postings, compensation surveys, hiring trend analysis, and conversations with AI engineers transitioning between companies during 2024. Compensation figures represent observed market ranges based on Levels.fyi, AngelList, and direct offer data, not guarantees. All specific engineer examples have been anonymized to protect privacy. This article provides frameworks for communication, not financial advice.