Senior Machine Learning Engineer

Bloomreach is seeking a Senior Machine Learning Engineer to join our Search Scaling team. The team takes the ML and AI behind Bloomreach search and makes it run in production: in the live search path that answers every shopper query, and in the data pipelines that prepare product catalogs for search.

Our search powers online stores for many merchants, each with its own catalog, vertical, language and region. The Scaling team's job is to make our search and discovery features work well for all of them, and to turn models into reliable, low-latency, cost-efficient systems at that scale.

We are looking for someone who combines solid ML expertise with strong software engineering skills and takes end-to-end ownership of technical problems. You will partner closely with our Search Quality team, which develops the relevance and ranking models behind our search.

Your Responsibilities

Design, build and operate ML components in the search serving path, within tight latency budgets.

Build query-time ML that helps search understand and handle each query.

Build and scale ML and LLM data pipelines across large product catalogs, with attention to cost and freshness.

Tune models across merchants, verticals and languages.

Build and scale online features and model serving infrastructure.

Set up offline evaluation and safe rollout for production models: baselines, A/B tests, fallbacks, and latency and cost tracking.

Improve observability and diagnosis of ML systems in production.

Work with Search Quality engineers to bring their models into production within latency and cost targets.

Contribute to technical design and influence decisions across teams.

Qualifications

BE/B.Tech/ME/M.Tech in Computer Science or a related discipline, with a strong mathematical foundation and excellent programming skills in Python. Experience with a backend language such as Go or Java.

5+ years of experience building ML-driven systems in production.

Strong software engineering skills, with hands-on experience serving ML models and LLMs in scalable cloud systems (AWS/GCP) under tight latency and cost budgets.

Experience building large-scale data and ML pipelines.

Strong problem-solving and analytical skills: able to break a problem down into its model-training and backend-engineering parts, and, during development, analyze results to pinpoint the cause of an issue and choose the right next step.

Experience working across multiple customers, domains or languages.

Working knowledge of search, such as query understanding, ranking, autosuggest or faceting.

Experience with deep learning frameworks such as PyTorch or TensorFlow.

Preferred Qualifications

Experience with Solr, Elasticsearch or a similar search engine.

Experience with vector search in production, such as Milvus or Qdrant.

Experience building evaluation, observability or explainability tooling for ML models, LLMs and serving systems.