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Intelligems

Machine Learning Engineer

Reposted Yesterday
Remote
Hiring Remotely in United States
Mid level
Remote
Hiring Remotely in United States
Mid level
As a Machine Learning Engineer, you will develop online prediction systems and offline recommendation engines to optimize e-commerce profitability by leveraging machine learning models and data-driven insights.
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About Intelligems

There are millions of e-commerce businesses around the world, and the industry is growing each year. Yet achieving profitability has never been harder. Costs are rising, it’s more tricky to acquire customers, and they have to run with lean teams. As a result, the vast majority of them guess about things like how to price their products, what discounts to offer, whether to charge for shipping, and what content they should show on the site, leaving 6-10% of potential profit on the table.

Intelligems is a profit growth platform that helps these entrepreneurs succeed and find all of that extra profit. The platform provides e-commerce merchants with a wide range of AB testing and personalization tools to help them understand their data, run experiments, and price dynamically. We’re taking technology that has previously been available only to the Amazons and Ubers of the world, and making it available to e-commerce stores of any size.

We have excellent traction. We’re working with 2000+ brands and have access to more than $10B of transaction and shopping session data. As we enter scaling mode, we are looking for highly talented individuals join us and help us continue to scale efficiently.

We have an extremely high-performing team, with a collaborative culture of low-ego + high IQ people. It’s our strongest asset, and we hold a very high bar. You can learn more about working at Intelligems and our values here.

We're backed by Stage 2 Ventures, Vinyl Capital, Matchstick Ventures, Techstars, and the founders of companies like Klaviyo and Postscript, and have raised >$11M of funding.

The Role

As Intelligems’ first Machine Learning Engineer, you will be at the forefront of our mission to revolutionize how e-commerce businesses optimize their profitability. You'll work directly with our product, data, and broader engineering team to develop sophisticated data models that power real-time decision making and actionable insights for thousands of merchants.

You'll build solutions across two critical domains:

  1. Online Prediction & Optimization Systems — Develop and deploy machine learning models for real-time personalization, dynamic pricing, and targeted offers that adapt to customer behavior and market conditions.

  2. Offline Recommendation & Classification — Create data-driven recommendation engines that help merchants identify pricing opportunities, focus on high-ROI store improvements, and classify experiment results to extract maximum value from their data.

This role requires both technical expertise and product intuition. You'll have the autonomy to identify opportunities where our data can create value, then bring those ideas to life in our product.

You're a Great Fit If
  • You have experience applying machine learning and statistical methods to solve real business problems

  • You're comfortable working with large datasets (billions of records)

  • You're entrepreneurial and thrive in ambiguous environments where you can define your own path

  • You have experience building and deploying production machine learning models

  • You're proficient in Python and have worked with data science libraries (scikit-learn, TensorFlow/PyTorch, numpy, pandas/polars, etc.)

  • You have a product mindset and can synthesize business context and customer input into product solutions

  • You're comfortable communicating complex technical concepts to non-technical stakeholders

  • Experience with A/B testing methodologies and experimental design is a bonus

  • You're excited about the e-commerce domain and helping small/medium businesses succeed

Data Stack
  • Cloud-based data lakes/warehouses (Snowflake, ClickHouse)

  • Python data science ecosystem (pandas/polars, numpy, scikit-learn, TensorFlow/PyTorch)

  • Kafka, Kinesis, and Apache Flink for real-time data processing

  • AWS

Culture & Values

As a customer-first, product-oriented company, our success is driven by the caliber of people on the team. Our core values include:

1. Put customers first

Create impact for customers and put the customers' interests above your own with honesty and transparency.

2. Be helpful

What goes around comes around. You never know where someone else is coming from, and being able to help out is a privilege. Whether it's a request from a customer, a teammate, or a partner, do your best to be prompt and generous in your support.

3. Strive for excellence

Commit to being the best in the world at what you do.

4. Build and develop a diverse, world-class team

Set the highest possible standards on hiring, and devote time and energy to giving and receiving feedback.

5. Have a perspective

Everyone on the team is responsible for making us great. If you disagree with something being done, you are expected to voice this and have a discussion, regardless of your role or who is on the other side of the table (or Zoom).

What’s Offered
  • Competitive salary + strong benefits + real equity packages

  • Extremely high degree of product/tech ownership

  • Medical, Dental, & Vision insurance

  • 401(k) with match program

  • Flexible vacation and PTO schedule

As an equal opportunity employer we strongly encourage people from underrepresented groups to apply for this role. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, national origin, sex, age, marital status, sexual orientation, gender identity, disability, political affiliation, personal appearance, pregnancy, family responsibilities, matriculation, or any other characteristic protected under federal, state, or local law.

Top Skills

Apache Flink
AWS
Clickhouse
Kafka
Kinesis
Numpy
Pandas
Python
PyTorch
Scikit-Learn
Snowflake
TensorFlow

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