About the Role
This role is for a hands-on ML Engineer who can design, train, and productionize models powering search relevance, retrieval, personalization, and LLM-based conversational experiences at a massive scale.
You will work closely with backend, platform, and catalog enrichment teams to deliver high-quality ML components under tight performance and latency constraints.
Key Responsibilities
- Build and improve search ranking, retrieval, and query understanding models.
- Develop ML components for Conversational Search:
- Multi-turn context handling
- Query intent detection and classification
- Retrieval-augmented generation (RAG) pipelines
- Reasoning workflows (ReAct, static + dynamic agent flows)
- Design and optimize embedding models, vector stores, and similarity search systems.
- Build personalized ranking and recommendation models using deep learning.
- Work on large-scale ML systems optimized for:
- Low latency
- High throughput
- Cost-efficient inference
- Implement ML pipeline best practices (versioning, monitoring, A/B testing, observability).
- Collaborate with platform teams to integrate ML services across search, recommendations, and conversational agents.
- Develop caching strategies (prompt cache, vector cache, similarity caching) to hit strict SLA targets.
- Contribute to long-term roadmap: foundational retrieval models, multi-objective optimization, and user lifecycle modeling.
Requirements
Required Qualifications
- 4-10 years of experience in Machine Learning / Applied ML engineering.
- Strong foundations in ML, deep learning, Transformers, and neural retrieval.
- Hands-on experience with:
- Search systems (retrieval + ranking)
- Recommendation models
- Embedding models & vector databases
- TensorFlow / PyTorch
- Proven experience building production-grade ML systems at scale.
- Familiarity with LLMs, RAG architectures, prompt engineering, and agent workflows.
- Strong coding skills (Python) and experience with modern ML stack (TensorFlow, PyTorch, Faiss/ScaNN, Triton, etc.).
- Ability to work closely with backend teams to deploy models in distributed systems.
- Excellent problem-solving skills and comfort working on ambiguous, high-impact problems.
Preferred Qualifications
- Experience with conversational AI, chat-based retrieval, or multi-turn dialog modeling.
- Experience in media, streaming, sports data, or large catalog discovery.
- Knowledge of micro-drama, short-video personalization, or multi-objective recommendation systems.
- Strong understanding of scalability patterns: batching, async orchestration, and caching layers.
Benefits
What you get
- Best in class salary: We hire only the best, and we pay accordingly.
- Proximity Talks: Meet other designers, engineers, and product geeks — and learn from experts in the field.
- Keep on learning with a world-class team: Work with the best in the field, challenge yourself constantly, and learn something new every day.
About us
Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world! We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore. Since 2019, Proximity has created and grown high-impact, scalable products used by 370 million daily users, with a total net worth of $45.7 billion among our client companies.
Today, we are a global team of coders, designers, product managers, geeks, and experts. We solve complex problems and build cutting-edge tech, at scale. Our team of Proxonauts is growing quickly, which means your impact on the company’s success will be huge. You’ll have the chance to work with experienced leaders who have built and led multiple tech, product, and design teams. Here’s a quick guide to getting to know us better:
Here’s a quick glimpse of Proximity and what it’s like to be a Proxonaut:
Follow our team's #BTS (behind-the-scenes) updates on our Instagram channels —
- @ProxWrks - @H.Jagda
Originally posted on Himalayas