ML Engineer / Generalist

Who We Are

HypeLab is a small, profitable ad network operating at real marketplace scale. We process more than 1B ad requests per month across hundreds of publishers, with advertisers buying across crypto, fintech, gambling, mobile, and other high-intent consumer audiences.

We are not trying to be a giant, generic ad platform. We are carving out a focused business where sharp execution, strong service, and better performance matter. Our customers care about outcomes: deposits, swaps, app installs, conversions, repeat spend, and revenue.

The team is small enough that one strong engineer can change the trajectory of the company.

The Work

This is an engineering role with ML and data at the center of it.

You will work on the systems that decide which ads we show, how we bid, how we predict performance, how we understand page and app context, and how we measure whether campaigns are actually working.

This is not a narrow research role. You will not be tucked away training models with no connection to the business. You will be close to the full loop: advertisers, publishers, auctions, models, conversions, revenue, and customer feedback.

Some weeks you may be improving predictive CTR or contextual targeting. Other weeks you may be debugging a data pipeline, shipping product code, improving bidding logic, or building internal AI tools that make the rest of the team faster.

Who You Are

You are a strong generalist who likes hard, practical problems.

You may be early in your career, including graduating this year from a rigorous CS or engineering program. What matters more than the logo on your resume is whether you can think clearly, learn quickly, write good code, and take responsibility for real systems.

You are probably a fit if:

• You have strong fundamentals in software engineering, ML, data systems, or backend infrastructure.
• You can ship production code, not just notebooks or class projects.
• You are comfortable reading unfamiliar code until it makes sense.
• You like debugging complex real-world systems.
• You have high agency and do not need a large team around you to make progress.
• You want broad ownership, not a tiny lane.
• You care about whether technical work improves the business.

You are probably not a fit if:

• You want a pure ML research role.
• You need a lot of structure before you can start.
• You only want to work on greenfield projects.
• You dislike touching product code, data pipelines, infrastructure, and customer-facing systems in the same role.
• You want to optimize for prestige more than impact.

What You Will Work On

• ML pipelines for contextual targeting and predictive CTR.
• Bidding and auction optimization systems that affect real advertiser spend.
• Data pipelines that feed targeting, prediction, reporting, and personalization.
• Conversion products for deposits, swaps, app installs, and other advertiser outcomes.
• Internal AI and automation tools for BD, campaign operations, and engineering.
• General product and infrastructure work across SDKs, bidding, payments, personalization, and advertiser/publisher tools.

We need someone who can help carry forward our ML and data systems while also building redundancy across the broader engineering team.

What It Is Like Here

We are lean and pragmatic. We care about shipping useful work, learning quickly, and tying engineering decisions to business outcomes.

You will get real responsibility early. That is the upside. The tradeoff is that there is not much room to hide. We are a small team, so unclear thinking, slow follow-through, and low ownership show up quickly.

We value direct communication, good judgment, low ego, and people who can be trusted with important problems. The work is scrappy, practical, and high leverage.

What Success Looks Like

In your first 3 months:

• You understand the core ad serving, bidding, contextual targeting, and reporting systems.
• You can independently ship fixes and improvements across ML/data and application code.
• You have taken ownership of at least one production pipeline or optimization workflow.

In your first 6 months:

• The team has materially more redundancy across ML and data systems.
• You have shipped improvements that help advertiser performance, campaign operations, or marketplace efficiency.
• You can operate as a trusted generalist across several parts of the codebase.

Useful Experience

None of these are strict requirements, but they are useful:

• Python, Ruby/Rails, TypeScript, SQL, BigQuery, Redis, Docker.
• Ranking, prediction, recommendations, ads, bidding, or marketplace optimization.
• LLM/agent tooling or workflow automation.
• SDKs, APIs, data pipelines, analytics systems, or production ML systems.
• Crypto, fintech, gambling, mobile ads, or performance marketing.

Apply

Send us:

• Your resume or LinkedIn.
• Links to projects, GitHub, papers, demos, or anything else that shows how you think and build.
• A short note about one technically hard thing you have built and what you learned from it.