Job Overview

Adobe is hiring a Machine Learning Engineer in Bangalore, Karnataka, for an engineering opportunity focused on building and deploying advanced machine learning capabilities at scale. The role sits within Adobe Unified Platform and is intended for an experienced ML professional who can establish strong engineering practices while working with a team of senior software engineers.

This is a hands-on position covering the complete machine learning lifecycle. You will help define how models are designed, trained, evaluated, optimized, deployed, monitored, and improved. The work includes deep learning, behavioral sequence modeling, generative AI analysis, personalization, propensity modeling, and closed-loop learning systems.

A major part of the opportunity is the chance to build reusable machine learning capabilities rather than focusing on a single isolated model. Your work can influence production systems and customer-facing experiences at Adobe scale, making strong engineering judgment, experimentation discipline, and production reliability important parts of the role.

Key Responsibilities

• Design and train deep learning models for complex behavioral data, including custom transformer and attention-based architectures.
• Build the complete model training workflow, covering event tokenization, temporal and positional representations, self-supervised learning, and downstream fine-tuning.
• Apply techniques such as masked modeling and contrastive learning where appropriate to improve model capabilities.
• Train large-scale models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation, checkpointing, efficient attention, and distributed training approaches.
• Develop and optimize data and feature pipelines using Databricks and Spark.
• Transform raw behavioral events into reliable, high-quality datasets and model-ready features.
• Move experimental ML solutions into production systems with appropriate scalability, reliability, observability, and operational controls.
• Improve inference performance through model, architecture, and serving-side optimization.
• Contribute to MLOps practices covering experiment tracking, model versioning, continuous integration and delivery, automated retraining, and production monitoring.
• Work with data science, product, platform, and engineering teams to turn ML opportunities into practical solutions.
• Mentor junior engineers on experimentation quality, deployment practices, and responsible AI considerations.
• Follow developments in machine learning and artificial intelligence and evaluate useful innovations for Adobe products.
• Contribute to machine learning solutions involving propensity and conversion prediction, anomaly detection, return-on-investment prediction, prompt classification, personalization, and intelligent administrative experiences.

Required Skills

Candidates should have strong practical experience building and deploying machine learning solutions in production environments. Python programming is required, along with hands-on experience using a deep learning framework such as PyTorch, TensorFlow, or an equivalent framework.

A strong understanding of the full ML lifecycle is important. This includes working with data, developing features, training and evaluating models, optimizing inference, deploying systems, monitoring production behavior, and improving models based on real-world outcomes.

The role also requires knowledge of model optimization and production system integration. Engineers should understand how to make large models efficient, reliable, and practical for real-world workloads rather than treating experimentation and production deployment as separate activities.

Preferred Skills

Experience with large-scale deep learning, transformer architectures, attention mechanisms, self-supervised learning, distributed model training, GPU optimization, and production ML systems is highly relevant.

Knowledge of MLOps, automated retraining, model monitoring, experiment management, model versioning, and CI/CD for machine learning systems will also be valuable.

Experience working with behavioral event data, personalization systems, generative AI analytics, recommendation-oriented modeling, anomaly detection, or customer intelligence can further align with the responsibilities of the role.

Education

The position accepts a Bachelor's degree, Master's degree, or equivalent professional experience in Computer Science, Machine Learning, Data Science, or a related technical discipline.

Experience

The required professional experience range is 4 to 8 years in building and deploying machine learning solutions at scale. Candidates should be able to demonstrate practical ownership of ML systems beyond academic experimentation, including model development, production deployment, optimization, and monitoring.

Required Technologies

• Python
• PyTorch
• TensorFlow or equivalent deep learning frameworks
• Databricks
• Apache Spark
• GPU infrastructure
• Transformers
• Attention-based architectures
• Distributed Data Parallel (DDP)
• Fully Sharded Data Parallel (FSDP) or equivalent distributed strategies
• Mixed-precision training
• MLOps
• CI/CD
• Model versioning
• Experiment tracking
• Automated retraining
• Production monitoring

Soft Skills

Strong collaboration and communication skills are important because the role works across data science, product, platform, and engineering teams. You should be comfortable explaining technical decisions, discussing experimentation results, and working toward practical business and product outcomes.

Ownership is another important quality. The position requires engineers who can establish approaches, make informed technical decisions, identify production risks, and take responsibility for improving the reliability and effectiveness of ML systems.

Mentoring ability is also valued. You should be willing to share knowledge with junior engineers and help establish disciplined approaches to experimentation, deployment, and responsible AI.

Benefits of Working in this Role

This role offers the opportunity to work on advanced machine learning systems within an established technology company while having meaningful influence over how an engineering team develops its ML capabilities.

The work spans deep learning, large-scale model training, generative AI, personalization, data engineering, MLOps, and production optimization. This breadth can help experienced ML engineers strengthen both research-oriented engineering skills and practical production expertise.

The position also provides exposure to ML systems that can support Adobe products and experiences used by a large global customer base, making scalability and production quality central to the engineering challenge.

Work Mode

The provided job description does not specify a remote or hybrid work arrangement. The work mode is therefore not inferred.

Location

The position is based in Bangalore, Karnataka, India.

Who Should Apply

This opportunity is well suited to Machine Learning Engineers, Applied ML Engineers, Deep Learning Engineers, and experienced AI engineers who have worked on production-scale machine learning systems.

Candidates with 4 to 8 years of professional experience and strong Python skills should consider applying, especially if they have practical experience with PyTorch or TensorFlow, distributed training, GPU workloads, feature pipelines, MLOps, and model deployment.

Engineers who enjoy combining advanced ML techniques with strong software engineering practices may find this role particularly relevant. Experience turning prototypes into dependable production services is especially useful.

Career Growth

The role can support growth across several areas of modern machine learning engineering, including large-model training, deep learning architecture, inference optimization, MLOps, production reliability, and AI system design.

Because the position involves collaboration with senior engineers and cross-functional teams, it can also provide opportunities to strengthen technical leadership, mentoring, and system-level decision-making. Building reusable ML capabilities can broaden an engineer's experience beyond individual models toward platform and organizational impact.

Application Advice

Applicants should highlight production ML projects rather than listing algorithms or frameworks without context. Clearly describe the models you built, the data involved, the scale of the workloads, the training methods used, and how the solutions were deployed.

If you have worked with PyTorch, TensorFlow, Spark, Databricks, GPUs, transformers, distributed training, or MLOps, connect those technologies to measurable engineering outcomes. Examples involving model performance, inference latency, training efficiency, reliability, automation, or production adoption can make your experience easier to evaluate.

Also emphasize experience collaborating with data scientists, product teams, and platform engineers. For a role with significant ownership, demonstrate how you made technical decisions, improved existing systems, mentored others, and moved ML work successfully from experimentation into production.