ML Systems Engineer – Training & Inference

About ZenteiQ

ZenteiQ is a deep-tech company born out of IISc Bangalore, building Scientific Intelligence

Infrastructure: physics-native AI for engineering, manufacturing, energy, mobility and national

systems. Rather than wrapping general-purpose language models, we train foundation models

(BrahmAI) from scratch to reason over thermal, electromagnetic, structural and materials

domains, and put them to work through industrial platforms (KogneX) and a talent OS for

engineers and researchers (AhamX). We're backed by the IndiaAI Mission (MeitY) and work

closely with IISc, ARTPARK and a national AI Hub Network - building the sovereign AI

infrastructure that India's engineering and industrial systems will run on.

About the Role

You will build the systems that let our researchers train, evaluate, and serve large foundation

models reliably at scale. This role sits at the intersection of model research and infrastructure,

with a focus on accelerator-based (TPU) training and inference, performance, reproducibility,

and researcher velocity. You will own the paved path that turns expensive, long-running model

runs into a repeatable, observable, and cost-efficient process.

What You'll Do

• Build and improve the paved path for distributed training, evaluation, experiment tracking,

checkpoint management, model release, and inference on Cloud TPUs.

• Operate long-running ML workloads with strong observability, failure detection, automated

recovery, and practical operational tooling.

• Profile and remove bottlenecks across compute, HBM and host memory, data input,

networking and collectives, XLA compilation, checkpointing, and serving.

• Build validation, representative-scale testing, CI/CD, reproducibility, and lineage systems

that catch problems before expensive model runs or production releases.

• Create reusable APIs, abstractions, and self-service tooling that help researchers move

quickly while preserving useful low-level controls.

• Own accelerator capacity workflows, including TPU provisioning, quotas, reservations,

priorities, topology-aware placement, utilization, and cost efficiency.

• Partner with research teams to debug model and systems failures, translate recurring

pain points into durable platform improvements and define operational standards.

What We're Looking For

• Strong software engineering skills and experience owning systems used by researchers

or engineers in production or research-critical environments.

• Hands-on experience with ML training or inference infrastructure at meaningful scale,

including large accelerator or distributed compute workloads.

• Strong distributed-systems fundamentals and experience with Kubernetes, GKE, or

comparable orchestration for long-running compute jobs.

• Ability to debug across model code, data pipelines, runtimes, cluster services, storage,

networking, and accelerator behaviour.

• Strong observability, reliability, and performance-engineering fundamentals, including

incident response and root-cause analysis.

• Production-quality Python and the ability to build durable platform abstractions rather than

one-off scripts.

• High ownership, pragmatic judgment, and clear collaboration with fast-moving research

teams.

Good to Have / Bonus Points

• Experience operating large Cloud TPU clusters, including TPU VMs or Pods, multislice

jobs, topology, quotas, scheduling, and failure recovery.

• Experience with JAX, PyTorch/XLA, TensorFlow, XLA/HLO, PJRT, MaxText, Pallas, or

another large-scale training stack.

• Experience serving LLMs on TPUs, including batching, partitioning, compilation caching,

autoscaling, latency, and throughput optimization.

• Familiarity with GCP/GKE, Terraform, Vertex AI, XPK, Ray, Argo, Airflow, MLflow, Weights

& Biases, or comparable internal platforms.

• Experience with model registries, distributed checkpointing, data and artifact lineage, and

large artifact distribution.

• Proficiency in a systems language such as Go, Rust, C++, or Java.

Why ZenteiQ

• Build real physics-native foundation models from scratch, not another LLM wrapper -

deep, defensible technical work.

• Be part of a nationally recognised mission: one of 8 startups selected under the

government's IndiaAI Mission to build a sovereign foundation model.

• Work alongside IISc-trained scientists and researchers, in a company founded by an IISc

professor.

• See your work land in real industry pilots across automotive, mobility, defence and

industrial R&D, with measurable impact.

• Join a lean, high-caliber team at an early, high-ownership stage.