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ML Engineer
Own the ranking and recommendation systems that decide which deals millions of shoppers see first.
About DealSeek
DealSeek is making coupon finding and deal hunting fun again. More than 5 million people have installed the DealSeek app, and millions use it every month on the web to find better prices across Amazon, Walmart, Best Buy, and other retailers. Our founders have bootstrapped multiple previous companies to over $1M ARR, and have gained 1M+ followers across social platforms.
Why this role exists
DealSeek has more inventory than any person could browse. As we grow and add more retailers, ranking and recommendation will only become more important. Our recommendation system decides whether a shopper finds something genuinely useful or scrolls past noise. We have the users: our A/B tests are decisive within hours, and we have the privilege of no cold-start problem. Now we need a skilled engineer to tinker, deploy better models, and make every session more relevant.
What you'll do
- Own the full ML lifecycle: data analysis, feature engineering, training, evaluation, deployment, monitoring, and iteration
- Build ranking and recommendation models with Python and LightGBM that score millions of deals against real production traffic
- Design A/B tests and causal measurement strategies that connect offline metrics to shopper behavior
- Use embeddings and Milvus vector search to improve discovery, personalization, and cold start
- Monitor model quality, drift, reliability, serving latency, and scalability in production
- Use ClickHouse and Postgres to turn behavioral and catalog data into useful features
- Partner with infra engineers on fast, observable model serving in Go
- Improve our experimentation workflow with Optuna and Weights & Biases
Qualifications
- Hands-on experience shipping ranking, recommender, search, or ads models
- Strong Python and practical experience with boosted trees, embeddings, and vector search
- Experience owning models beyond the notebook, including deployment, monitoring, and iteration
- Strong judgment about A/B testing, causal measurement, and the limits of offline evaluation
- Ability to read production Go and collaborate on model-serving systems
- A bias towards iteration and measurement over a single, long-term research project
Do not meet every qualification? We are open-minded. Use your Why DealSeek response to tell us why your experience makes sense for this role.
Apply for this role
How we work
Hustler Mentality
We work hard to earn our seat at the table every day. No coasting on past wins, and scrappy beats perfect.
Bias Toward Tinkering
We're proactive, not just reactive. Sometimes we break things that are working. We believe that innovation comes from hundreds of failed experiments.
No Egos
You can talk to anyone at DealSeek. No closed doors, no gatekeepers, no politics. Ideas are judged on merit, and credit goes to the team.
Extreme Ownership
We take responsibility for outcomes, not just tasks, and solve problems even when they fall outside our job description. We own the result end to end.
Speed x Craft
We get MVPs out quick, then come back and refine them. We enjoy perfecting the details, and iteration adds up over time.
Abundance Mindset
We don't limit our ambitions to what others have achieved. We know the competition; we are not defined by it.
Use the product
We dogfood DealSeek daily. The best ideas come from shopping for things you actually need.
AI-native engineering
We pay for your AI token spend on frontier models, and you'll build alongside our internal AI agent.
Async-first remote
Discord, Linear and a Gather virtual office. No meetings for things a message can do.