*Machine Learning Engineer – Search, Ranking & Personalization*
*Stage:* Seed
*Founded:* 2022
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*Key Job Information*
- *Location:* New York, NY / San Francisco, CA (Remote OK)
- *Employment Type:* Full-Time
- *Experience Level:* 3+ years
- *Salary Range:* $190,000 – $260,000 per year
- *Equity:* Competitive equity package
- *Visa Sponsorship:* H-1B, O-1, OPT
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*About the Company*
Client is a fast-growing shopping platform with over 350,000 active users and a 90% retention rate. The company is focused on building intelligent, personalized search and ranking systems to help users discover and trust products at scale. The team is composed of experienced engineers from leading consumer tech companies such as Pinterest and Amazon.
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*Role Summary*
As a Machine Learning Engineer at Client's company, you will join the ML team to design, build, and scale machine learning systems that drive search, ranking, and personalization across a platform serving hundreds of millions of items daily. This is a highly impactful role where your work directly influences user retention and trust. You will collaborate with a world-class team of engineers and play a key part in defining the ML search and personalization strategy from the ground up. The position is open to fully remote candidates.
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*Key Responsibilities*
- Design, train, and deploy large-scale search, ranking, and personalization models.
- Handle hundreds of millions of items daily with high performance and reliability.
- Collaborate closely with backend and infrastructure teams to integrate ML models into production (GraphQL, Prisma, Node.js, Python, gRPC/Protobuf).
- Continuously improve model accuracy and system scalability.
- Contribute to product direction and technical roadmap for Client's ML systems.
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*Requirements*
*Must-Have Qualifications:*
- Minimum of 3+ years professional experience building and deploying ML models in production.
- Proven experience with ranking, recommendation, or personalization systems.
- Proficiency in PyTorch and large-scale data processing for real-time inference.
- Strong backend integration experience (GraphQL, Prisma, Node.js, Python, gRPC/Protobuf).
- Willingness to work in a high-intensity, fast-paced startup environment.
- Based in New York or remote in San Francisco.
*Preferred Background:*
- Current or prior experience at companies like DoorDash, Etsy, Pinterest, Amazon, or eBay.
- Previous work on consumer-facing search or recommendation products.
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*Benefits & Perks*
- $190K–$260K base salary plus competitive equity.
- Direct impact on a core product with a massive, high-retention user base.
- Work alongside top-tier engineers from leading consumer tech companies.
- Fast-paced startup culture with rapid iteration and experimentation.
- Opportunity to build the ML search and personalization strategy from scratch.
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*Interview Process*
1. Intro call with Head of Recruiting
2. Technical Interview
3. Coding Interview
4. CTO Interview
5. Onsite Interview
6. Offer Extended
7. Hire
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*Candidate Guidelines*
*Green Flags:*
- Experience solving large-scale consumer search/ranking challenges (e.g., Pinterest, Meta, TikTok, Amazon Ads).
- Strong track record shipping high-impact ML features in consumer products.
- Early-stage or startup experience with end-to-end ownership of ML pipelines.
- Demonstrated “builder” mindset — side projects, prototypes, hackathon wins.
- High intrinsic motivation and interest in future entrepreneurship.
*Red Flags:*
- Primarily B2B search experience with limited data complexity.
- Research-only background without production deployment.
- Prefers management over hands-on technical work.
- Struggles with ambiguity or high-intensity work environments.
- Unwilling to relocate or adapt to NYC-based team culture.
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*Ideal Companies*
- Amazon
- eBay
- Pinterest
- DoorDash
- Etsy
