Sr Software Engineer, Data & AI Platform

Dolby Laboratories; Bengaluru; Hybrid; INR 1,800,000 - 2,500,000; Full time

Benefits offered by this job

Flexible work approach

Excellent compensation and benefits

Job summary

Dolby Laboratories in Bengaluru is seeking a Senior Software Engineer to develop a unified data and AI/ML platform. You will design core services, work on API integrations, and address the full AI/ML lifecycle from data preparation to deployment. Ideal candidates should have a BS in a technical field, expert knowledge in Python, and extensive experience with Kubernetes. The position offers competitive compensation and a flexible work environment focused on innovation.

Qualifications

• Proven track record building large-scale distributed systems and integrated data and AI/ML platforms.
• Expert-level proficiency in Python and one of Go/Java/C++ for building production-grade services.
• Extensive hands-on experience with Kubernetes including autoscaling and job scheduling frameworks.

Responsibilities

• Design and build platform primitives to enable reproducible experiments and workflows.
• Create developer tools to elevate the development experience through CLIs and dashboards.
• Implement and scale distributed training systems on top of Kubernetes and cloud orchestration.

Skills

Python

Go

Java

C++

Kubernetes

AI/ML ecosystem

Education

BS in Computer Science, Mathematics, Engineering, or equivalent

Master’s degree preferred

Tools

PyTorch

TensorFlow

Ray

MLFlow

Job description

Join the leader in entertainment innovation and help us design the future. At Dolby, science meets art, and high tech means more than computer code. As a member of the Dolby team, you’ll see and hear the results of your work everywhere, from movie theaters to smartphones. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent. We’re big enough to give you all the resources you need, and small enough so you can make a real difference and earn recognition for your work. We offer a collegial culture, challenging projects, and excellent compensation and benefits, not to mention a Flex Work approach that is truly flexible to support where, when, and how you do your best work.

What You’ll Do

We’re building a unified data and AI/ML model development and training platform—similar in ambition to those platforms at other media technology companies—to accelerate our entire AI/ML lifecycle from data preparation and feature computation to experimentation, large‑scale distributed training, evaluation, deployment, and governance & observability. As a Senior Software Engineer, you will design and implement core platform services exposed through high‑quality APIs and SDKs for a platform that seamlessly integrates data systems, compute orchestration, and AI/ML tooling. These abstractions hide infrastructure complexity and maximize practitioner velocity across Dolby teams working on audio, video, personalization, content understanding, and generative AI. A major focus is creating horizontal, reusable components—feature pipelines, embedding services, training/evaluation frameworks, SDKs, and model management APIs—to simplify the end‑to‑end AI/ML lifecycle.

Key Responsibilities

• Design and build platform primitives—Python SDKs, platform APIs, and templates—that enable reproducible experiments, configuration‑as‑code workflows, model lineage, and artifact tracking, which enable seamless promotion from research to production.
• Create developer tools to elevate development experience—CLIs, UI, dashboards, visualization layers—that simplify platform operation and multi‑stage workflows.
• Implement and scale distributed training systems (multi‑node GPU workloads) on top of Kubernetes and cloud‑based orchestration foundation.
• Build large‑scale evaluation frameworks for offline tests, shadow deployments, and A/B experimentation.
• Implement model/dataset versioning, approvals, lineage tracking, retention, and compliance hooks.
• Partner with AI/ML research, platform engineering/MLOps and infrastructure, and data engineering teams to generalize workflows into reusable frameworks.
• Partner with platform engineering/MLOps and infrastructure to define observability stacks for metrics, drift indicators, performance regressions, training/inference health signals, production reliability (SLIs/SLOs), monitoring, and incident response.

What You Need To Succeed

Desired Background

• BS in Computer Science, Mathematics, Engineering, or equivalent technical field. Master’s preferred.
• Proven track record building large‑scale distributed systems and integrated data and AI/ML platforms (e.g., training, serving, workflow orchestration, data pipelines).
• Expert‑level proficiency in Python and one of Go/Java/C++ and building production‑grade services/APIs/SDKs.
• Extensive hands‑on experience with Kubernetes (EKS, GKE, self‑hosted, etc.) including autoscaling and job scheduling frameworks, GPU infrastructure, and AI/ML‑related AWS/GCP managed services (VertexAI, SageMaker, etc.).
• Deep expertise with AI/ML ecosystem and tooling such as PyTorch, TensorFlow, Ray, experiment/feature/model stores (MLFlow, WnB, Feast, etc.), Hugging Face.
• Proven ability to scale AI/ML workloads and pipelines—pipeline SDKs, feature/model CI/CD, automated evaluation, safe rollouts, monitoring.
• Strong developer‑experience mindset: ability to translate research/engineering friction into elegant APIs, templates, and tools that reduce time‑to‑first‑successful remote run and raise platform adoption.

Preferred Skill

• Previous experience with Databricks.
• Knowledge of multimodal AI/ML (audio, video, text) data preparation, feature extraction, model development, training, and evaluation workflows.
• Experience with LLM/foundation model sizing/estimation, training requirements, pipelines, and deployment.
• Knowledge of LLM/foundation model sizing/estimation, training requirements, evaluation workflows and orchestration and deployment patterns.
• Experience designing feature stores or embedding services tightly integrated with training pipelines.