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Umami Bioworks
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Machine Learning Engineer

Machine Learning Engineer

Name: Jobpe
Address: T-Hub, Plot No 1/C, Sy No 83/1, Raidurgam panmaktha, Knowledge City Rd, Hyderabad, Telangana, 500081, IN
Telephone: +91-83339-09630
Price range: Free

Umami Bioworks

0 years

0 Lacs

Bengaluru Karnataka India

Posted:1 day ago| Platform:

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Skills Required

learning development synthesis discovery manufacturing drive cutting engineering ml optimization tuning analysis data software model design architecture flow orchestration logic routing strategies training testing inference efficiency integration monitoring versioning drift layers reinforcement ai pytorch tensorflow xgboost mlflow numpy scipy containerization deployment docker workflow management etl api test

Work Mode

On-site

Job Type

Full Time

Job Description

At Umami Bioworks, we are a leading bioplatform for the development and production of sustainable planetary biosolutions. Through the synthesis of machine learning, multi- omics biomarkers, and digital twins, UMAMI has established market-leading capability for discovery and development of cultivated bioproducts that can seamlessly transition to manufacturing with UMAMI’s modular, automated, plug-and-play production solutionBy partnering with market leaders as their biomanufacturing solution provider, UMAMI is democratizing access to sustainable blue bioeconomy solutions that address a wide range of global challenges.We’re a venture-backed biotech startup located in Singapore where some of the world’s smartest, most passionate people are pioneering a sustainable food future that is attractive and accessible to people around the world. We are united by our collective drive to ask tough questions, take on challenging problems, and apply cutting-edge science and engineering to create a better future for humanity. At Umami Bioworks, you will be encouraged to dream big and will have the freedom to create, invent, and do the best, most impactful work of your career.Umami Bioworks is looking to hire an inquisitive, innovative, and independent

Machine Learning Engineer

to join our R&D team in

Bangalore, India,

to develop scalable, modular ML infrastructure integrating predictive and optimization models across biological and product domains.The role focuses on orchestrating models for media formulation, bioprocess tuning, metabolic modeling, and sensory analysis to drive data-informed R&D.The ideal candidate combines strong software engineering skills with multi-model system experience, collaborating closely with researchers to abstract biological complexity and enhance predictive accuracy.

Responsibilities

Design and build the overall architecture for a multi-model ML system that integrates distinct models (e.g., media prediction, bioprocess optimization, sensory profile, GEM-based outputs) into a unified decision pipeline
Develop robust interfaces between sub-models to enable modularity, information flow, and cross-validation across stages (e.g., outputs of one model feeding into another)
Implement model orchestration logic to allow conditional routing, fallback mechanisms, and ensemble strategies across different models
Build and maintain pipelines for training, testing, and deploying multiple models across different data domains
Optimize inference efficiency and reproducibility by designing clean APIs and containerized deployments
Translate conceptual product flow into technical architecture diagrams, integration roadmaps, and modular codebases
Implement model monitoring and versioning infrastructure to track performance drift, flag outliers, and allow comparison across iterations
Collaborate with data engineers and researchers to abstract away biological complexity and ensure a smooth ML-only engineering focus
Lead efforts to refactor and scale ML infrastructure for future integrations (e.g., generative layers, reinforcement learning modules)

Qualifications

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Computational Biology, Data Science, or a related field
Proven experience developing and deploying multi-model machine learning systems in a scientific or numerical domain
Exposure to hybrid modeling approaches and/or reinforcement learning strategies

Experience

Experience with multi-model systems
Worked with numerical/scientific datasets (multi-modal datasets)
Hybrid modelling and/or RL (AI systems)

Core Technical Skills

Machine Learning Frameworks: PyTorch, TensorFlow, scikit-learn, XGBoost, CatBoost
Model Orchestration: MLflow, Prefect, Airflow
Multi-model Systems: Ensemble learning, model stacking, conditional pipelines
Reinforcement Learning: RLlib, Stable-Baselines3
Optimization Libraries: Optuna, Hyperopt, GPyOpt
Numerical & Scientific Computing: NumPy, SciPy, panda
Containerization & Deployment: Docker, FastAPI
Workflow Management: Snakemake, Nextflow
ETL & Data Pipelines: pandas pipelines, PySpark
Data Versioning: Git
API Design for modular ML blocks

You will work directly with other members of our small but growing team to do cutting-edge science and will have the autonomy to test new ideas and identify better ways to do things.

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Machine Learning Engineer