The ML engineers you actually want aren't on Upwork. They're employed at startups — building production pipelines, shipping models to real users — and quietly open to something better. Underdog.io delivers pre-screened, full-time ML engineers with verified production experience to your inbox every Monday. No freelancers. No offshore. No retainer.
Why this hire is different
The gap between "trained a model" and "shipped a model to production and kept it running" is enormous. A significant portion of senior ML engineer resumes describe notebook-only experience — candidates who achieved good validation metrics on a test set but have never dealt with data drift, feature pipeline failures, or a model returning nonsense in production because timestamps were processed differently between staging and prod. The best ML engineers have done all of that. They're the ones you need — and they're employed.
ML researchers are valuable — but a startup doesn't need someone who can read papers. You need someone who can take a working model and deploy it reliably, monitor it, retrain it, and maintain it when the distribution shifts.
A contract ML engineer builds the model and disappears. You need someone who will own the ML layer long-term — watching model performance degrade, building the next version, and mentoring your team on what good ML engineering looks like.
Most ML hiring fails not because the engineer was wrong but because the company's data wasn't ready. The right ML engineer will tell you this upfront. The wrong one will build a beautiful model on bad data and move on.
Underdog filters for production ML experience specifically. Every ML engineer in our network has shipped models to real users, managed end-to-end data pipelines, and has the deployment experience that separates senior engineers from senior researchers. You get a curated shortlist. You only pay when you hire.
ML specializations in the network
Tell us what you're building, your ML maturity, and the specific problem you need solved. We filter the network before you see a single candidate — not just "ML engineer" as a catch-all.
Best for: Startups building LLM-powered products. RAG pipelines, agent orchestration, fine-tuning, prompt engineering, evaluation. Engineers who've shipped LLM features to production users — not just prototyped in a notebook.
Best for: Startups with defined ML use cases — recommendations, fraud, search ranking, forecasting. PyTorch, TensorFlow, Scikit-learn. Engineers who bridge the gap between model research and reliable production systems.
Best for: Teams at Series B+ who need training pipelines, feature stores, model registries, and serving infrastructure. The engineers who make your ML actually reliable in production at scale.
Best for: Startups where language understanding, text generation, or image recognition is core to the product. The highest-compensated ML specializations — and the hardest to source through traditional channels.
Best for: AI-native startups who need strong math background combined with genuine software engineering ability. Engineers who can read a paper and turn it into a production system — not just either.
Best for: Seed and pre-seed AI startups making their first ML hire. They design the stack, pick the frameworks, and set the patterns for everything the ML team builds after them. High equity, total architectural ownership.
Hiring guide
The most common ML hiring mistakes aren't about finding the wrong engineer. They're about misaligning expectations before the first interview. Here's what separates successful ML hires from expensive mistakes.
How it works
No sourcing sprints. No LinkedIn campaigns. No agency fees. A curated shortlist of pre-vetted ML engineers — matched to your specific use case and stack — delivered every week.
Share your ML use case, current data situation, tech stack, seniority, and location. Takes 15 minutes. We filter the network on the specifics — not just the words "ML engineer" on a resume.
Every Monday, we introduce you to ML engineers who match your criteria. Hand-reviewed. Production experience verified. Full-time seeking. Already know about your company before the first conversation.
Request interviews directly through the platform. No agency in the middle. No exclusivity. You pay 11.5% of first-year salary only when you make a hire — and only if you hire.
How we compare
An honest look at every option for hiring a full-time ML engineer at a startup.
Compensation & pricing
ML engineer salaries are rising fast. Traditional agencies charge 20–25% of first-year salary — on a $185K ML hire, that's $37–46K often paid upfront. Underdog charges 11.5%, pay-per-hire only. No retainer.
Base salary ranges at venture-backed startups. Equity is additive. Sources: Wellfound, Glassdoor, Carta H1 2025, SalaryCube 2025.
Companies in the network
Every company has been reviewed and approved. No staffing firms. No agencies. Just real teams building real AI products — hiring ML engineers to help them ship.
Not every company is actively hiring ML engineers at all times. We match you based on your use case and what's open when you join.
Common questions
Ready to hire
Production-verified. Full-time seeking. US-based. Matched to your ML use case and introduced directly to you — no agency, no retainer, no freelancers. First batch within one week.
Start hiring ML engineers →