Machine Learning Engineer, Ranking & Retrieval (Remote)

MAP SSG; United States; Remote; USD 185,000 - 280,000; Full time

Benefits offered by this job

Equity

Discretionary bonus

Remote work

Job summary

MAP SSG is seeking a Machine Learning Engineer for ranking and retrieval. This fully remote, full-time role offers a base salary of $185K–$280K in USD plus equity and discretionary bonus. You’ll join a small ML team building models that decide what the app retrieves, ranks, and surfaces for each user.

You’ll own training, evaluation, deployment, and real-world feedback loops to improve relevance, latency, and reliability at scale.

Qualifications

• 2+ years of industry ML engineering experience.
• Built and shipped a ranking, retrieval, or recommendation model.
• Strong Python skills; experience with PyTorch or JAX.
• Experience with model serving, monitoring, and deployment.

Responsibilities

• Build and ship models for ranking, retrieval, classification, and personalization.
• Improve search relevance and context retrieval using embeddings and reranking.
• Use behavioral signals to personalize results while preserving privacy.
• Create labeled datasets and evaluation pipelines to measure quality and catch regressions.
• Own training, inference, deployment; monitor live performance and improve latency and cost.

Skills

Python

ML engineering

Ranking models

Model deployment

A/B testing

Tools

PyTorch

JAX

Model serving

Job description

Machine Learning Engineer, Ranking & Retrieval (Remote)

• US, Remote

Machine Learning Engineer, Ranking & Retrieval

Fully remote · Full-time · $185K–$280K U.S. base salary + equity + discretionary bonus

We’re recruiting for an early-stage consumer AI company building a mobile app that helps people stay on top of messages, surface what needs their attention, and coordinate plans.

You’ll join a small ML team and own the models that decide what the app retrieves, ranks, and surfaces for each user. You’ll take models from training and evaluation through deployment, then use real-world feedback to improve relevance, latency, and reliability.

What you’ll do

• Build and ship models for ranking, retrieval, classification, and personalization.
• Improve search relevance and context retrieval using embeddings, hybrid search, and reranking.
• Use behavioral signals to personalize results while respecting user privacy.
• Build labeled datasets and evaluation pipelines to measure quality and catch regressions before release.
• Own training, inference, and deployment. Monitor live performance, debug production issues, and improve accuracy, latency, and cost.

What we’re looking for

• 2+ years of industry experience as a machine learning engineer, applied scientist, or in a similar hands-on role.
• A ranking, retrieval, or recommendation model you personally built and shipped to production. Consumer search, feeds, discovery, and marketplace recommendations are especially relevant.
• Experience with model serving and monitoring, and the ability to explain what you owned, how you measured quality, and what changed after launch.
• Strong Python skills and experience with a deep learning framework. The team uses PyTorch and JAX.

Experience with behavioral personalization, classification, or offline evaluation and online A/B testing is a plus.

Location and compensation

The team is fully remote, with a preference for candidates in the U.S. and Canada. Candidates in Europe are also considered. The U.S. base salary is $185K–$280K, plus equity and a discretionary performance bonus. Compensation outside the U.S. is benchmarked locally. Candidates must already have work authorization in their country of employment. Visa sponsorship is unavailable.