Job Description :

We're hiring Junior Members of Technical Staff (MTS) who are strong software engineers and want to work close to production, customers, and the underlying systems powering enterprise AI.

You will be mapped to one of three technical tracks based on your strengths :

- AI Platforms - Enterprise AI platforms, backend systems, LLM applications and agents

- AI Infra - GPU infrastructure, Kubernetes, distributed systems and platform engineering

- Applied Research - AI evaluation, post-training, simulations and data curation

You don't need to choose a track upfront. Your technical depth and demonstrated experience will determine the best fit.

What We're Looking For :

You have 2 - 4 years of professional engineering experience and have shipped production software.

You should be able to take ownership of a well-defined component from design - implementation - deployment - debugging - improvement.

We are looking for engineers with strong fundamentals rather than candidates who simply have a long list of AI tools on their resume.

Core Engineering Requirements :

- Strong programming ability in Python, Go, or Rust

- Experience building and operating production services

- Strong understanding of APIs, databases, distributed systems and software design

- Hands-on experience with Docker and Kubernetes

- Experience with at least one major cloud : AWS, Azure or GCP

- Git, CI/CD and automated testing

- Understanding of reliability concepts such as retries, failure handling, logging and monitoring

- Ability to debug production issues and understand system behaviour beyond writing code

Track 1 - AI Platforms :

Best suited for backend/platform engineers who have hands-on experience building applications and services around LLMs.

You should have experience with several of :

- Python backend development

- REST/gRPC APIs and production services

- PostgreSQL / Redis

- Kafka, NATS or other messaging systems

- Event-driven and distributed architectures

- LLM applications and APIs

- RAG pipelines

- Agentic applications / tool calling

- Vector databases

- LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen or equivalent

- LLM evaluation, observability or inference systems

Track 2 - AI Infra :

Best suited for systems, infrastructure or platform engineers interested in building infrastructure for large-scale AI workloads.

Strong candidates may have experience with :

- Go or Rust

- Linux systems

- Kubernetes

- Docker / containerd

- GPU infrastructure

- CUDA

- NVML / DCGM

- MPS / MIG

- Kubernetes controllers/operators

- gRPC and platform APIs

- Kafka / NATS / RabbitMQ

- PostgreSQL / MySQL

- Prometheus / Grafana / OpenTelemetry

- Distributed systems and fault tolerance

Track 3 - Applied Research :

Best suited for ML engineers who combine strong engineering fundamentals with hands-on experimentation.

Relevant experience includes :

- Python

- PyTorch or JAX

- LLM evaluation and benchmarking

- Fine-tuning / post-training

- Agent evaluation

- RL / RLHF

- Embedding and reranker experiments

- Simulation environments

- Synthetic data generation

- Large-scale data curation

- Model training or serving infrastructure

- Statistical experimentation and evaluation

What Success Looks Like :

As a Junior MTS, you will :

- Ship production code used by real customers

- Own well-scoped engineering problems end-to-end

- Work with senior engineers on architecture and system design

- Debug and improve production systems

- Build reliable AI infrastructure and applications

- Learn quickly and expand your technical scope

- Work across engineering, infrastructure, AI and customer-facing problems

Who Will Stand Out :

The strongest candidates will demonstrate :

- Real production ownership, not just project experience

- Strong coding and systems fundamentals

- Hands-on Docker/Kubernetes/cloud experience

- Genuine AI/ML exposure relevant to one of the three tracks

- Ability to explain technical decisions and trade-offs

- Strong debugging and problem-solving ability

- Curiosity and willingness to work across the stack

- Ability to operate effectively in a fast-moving startup environment

Important :

We are not looking for candidates who have simply built ChatGPT wrappers, completed AI courses, or listed multiple AI frameworks on their resume.

We want engineers who can build, deploy, operate and improve production systems.