Staff Machine Learning Engineer

Staff Machine Learning Engineer

India Bloomreach is building the world's premier agentic platform for personalization. We're revolutionizing how businesses connect with their customers, building and deploying AI agents to personalize the entire customer journey.

We're taking autonomous search mainstream, making product discovery more intuitive and conversational for customers, and more profitable for businesses.

We're making conversational shopping a reality, connecting every shopper with tailored guidance and product expertise — available on demand, at every touchpoint in their journey.

We're designing the future of autonomous marketing, taking the work out of workflows, and reclaiming the creative, strategic, and customer-first work marketers were always meant to do.

And we're building all of that on the intelligence of a single AI engine — Loomi — so that personalization isn't only autonomous…it's also consistent. From retail to financial services, hospitality to gaming, businesses use Bloomreach to drive higher growth and lasting loyalty. We power personalization for more than 1,400 global brands, including American Eagle, Sonepar, and Pandora.

Bloomreach is seeking a seasoned Staff Machine Learning Engineer to join our Search Quality team and lead the design and implementation of machine learning and Al-driven solutions that improve search relevance and discovery experiences for our customers. The Search Quality team focuses on building intelligent, scalable solutions that understand user queries, product catalogs, and behavioral signals to deliver the most relevant search results. We work at the intersection of Machine Learning, Information Retrieval, Search Relevance, and Software Engineering to solve challenging problems across the search stack. This is a Staff ML Engineer role, so we are looking for someone who can combine strong ML expertise with solid software engineering skills and take end-to-end ownership of complex technical problems. You will work closely with engineers, architects, and product teams to research, build, evaluate, and productionize ML-driven solutions for search.

Your Responsibilities

Own the technical roadmap and provide architecture leadership for ML and AI-driven relevance solutions. Design, develop, and enhance ML/AI models and algorithms for Search and Relevance. Own model evaluation and experimentation frameworks to ensure high-quality deployments. Work with search queries, product catalogs, user behavior, and other data to identify patterns and improve search quality. Work closely with software and data engineers to integrate and scale ML components into production systems. Analyze search and relevance data to identify problems and drive improvements. Mentor engineers and influence cross-team decisions to align engineering and product goals. Stay up to date with developments in Machine Learning, Information Retrieval, and Generative AI.

Qualifications

BE/B.Tech/ME/M.Tech in Computer Science or a related discipline, with a strong mathematical foundation and excellent programming skills, primarily Python, alongside proficiency in a backend language such as Java or Go. 8+ years of experience building ML-driven systems and algorithms in production. Strong software engineering skills, with experience building low-latency, scalable distributed systems in a cloud production environment (AWS/GCP/Azure). Strong background in Search, Information Retrieval, Ranking, or Search Relevance, working with large-scale data and ML systems. Hands-on experience applying LLMs to Search/ML problems, including quality, latency, and cost trade-offs. Experience working across multiple merchants, domains, and languages. Strong background in NLP and Search Quality techniques, including intent identification, query understanding, data analysis, and domain-specific ML model tuning. Relevant search retrieval experience in at least one area: RAG, lexical search, vector search, or recommendation systems. Experience with deep learning frameworks such as PyTorch or TensorFlow. Strong problem-solving and analytical skills. Effective communication skills in English, both verbal and written.

Preferred Qualifications

Prior experience building or working with LLM evaluation frameworks. Hands-on experience implementing hybrid search architectures.