Machine Learning Engineer job opportunity at Reveal HealthTech.

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Date2026-02-06T08:14:48.581Z bot
Reveal HealthTech Machine Learning Engineer
Experience: 5-years
Pattern: Full-time
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loacation Bengaluru, India
loacation Bengaluru....India

Thank you for considering the Machine Learning Engineer position at Reveal Health Tech. We are an early-stage IT startup based in the US and India, focused on leveraging technology to deliver transformative healthcare solutions.  Location: Bengaluru, India  About the Applied AI Lab  The Applied AI Lab is an internal R&D team at Reveal dedicated to identifying high-impact problems in healthcare, life sciences, and adjacent verticals—and transforming those insights into repeatable, IP-driven AI solutions.  We operate as a nimble product studio within the company: researching emerging technologies, rapidly prototyping AI and ML-powered tools, and building foundational infrastructure to support long-term product plays. Our output ranges from sandbox-ready MVPs to reusable components and SaaS-aligned platforms. Our team is multi-disciplinary—engineering, design, research, and business—and we work closely with client-facing and go-to-market teams to validate our ideas in the real world.  About the Role  We’re looking for a Machine Learning Engineer to join the Applied AI Lab and play a hands-on role in building intelligent, scalable, and production-minded ML systems. You’ll work at the intersection of rapid prototyping and product-minded ML—contributing to both short-cycle innovation and long-term platform stability.  This is an ideal opportunity for someone who enjoys wearing multiple hats, thrives in fast-moving environments, and is excited about shaping how AI can drive meaningful change in healthcare and life sciences.  Key Responsibilities  Contribute to solving business problems with cutting-edge AI solutions and innovate on ways to improve cost-efficiency and reliability of those solutions   Collaborate with Lab lead, product designers, researchers, and architects to scope MVPs and define core capabilities.  Build modular, reusable components for GenAI solutions or traditional ML model training, evaluation, and deployment  Design and develop AI tools that can be called via API or through batch transformations for large datasets.   Implement end-to-end solutions for batch and real-time algorithms along with tooling around monitoring, logging, automated testing, and performance testing.    Develop production-grade solutions following software engineering best practices  Support experimentation, A/B testing, and fine-tuning cycles with the team  Research and stay up-to-date on the latest advancements in generative AI technologies and methodologies.   Document architectures, processes, and technical decisions.  Please note that while you do not need to be an expert in every area, being familiar with most of the following is important. We are looking for someone who can effectively integrate everything, with team support to fill any gaps.    Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.   5+ years of experience in a similar AI/ML engineer role.  Strong knowledge of Python, SQL, and additional data processing languages.   Proficiency in basic machine learning concepts and methods, especially those related to GenAI and NLP.  Experience using open-source LLMs.  Proficiency in software engineering fundamentals (unit testing, modular code development, CI/CD, etc.)  Proficiency in core ML/AI frameworks (e.g. scikit-learn, PyTorch, TensorFlow, HuggingFace)  Familiarity with cloud-based tools (AWS and Azure), containerization (Docker), and version control (Git)  Experience with ETL tools (e.g., Apache Spark, Airflow), Pyspark, data modeling/warehousing and CI/CD tools is a plus.   Experience with vector embeddings, multimodal data, or agentic workflows  Knowledge of LLM fine-tuning, RAG pipelines, and/or hybrid retrieval techniques  Exposure to healthcare or life science datasets (EHR, claims, clinical trials, etc.)  Experience working in startup or lab-like R&D environments  Strong communication skills and comfort working in cross-functional teams 

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