Senior Machine Learning Engineer

Senior Machine Learning Engineer

Remote (India) · Full-time

About the Company:

Aerobotics7 (A7) is a growing, stealth-stage startup building AI and robotics to solve some of the critical challenges on the planet. Small team, high ownership, real hardware in the field. We'll share more once we're talking.

Position Overview:

Own every ML system at A7 end-to-end - from limited real + synthetic datasets to models running in real time on the robot - as the only ML engineer, setting the technical direction for our next phase of development and deployment.

This is not a "train a model and hand it off" role. You build the data pipeline, the models, the evaluation, and the deployment stack. You define the ML roadmap and, as we grow, hire and lead the team behind it.

Key Responsibilities

• The full ML lifecycle: custom model development, data to training to evaluation to edge/cloud deployment to monitoring to iteration.

• Object detection and classification on hard, low-signal sensor data (radar, LiDAR, camera), including multi-sensor fusion.

• Our synthetic data strategy: generation, sim-to-real domain gap, domain adaptation and randomization.

• Real-time inference on-device (NVIDIA Jetson AGX Orin) and at scale in the cloud.

• ML infrastructure from scratch: experiment tracking, dataset/model versioning, reproducible training, evaluation harnesses.

• The ML roadmap. You set priorities with the founders and are accountable for outcomes, not experiments.

Must-have

• 5+ years applied ML shipping models into production, not research-only.

• Deep, hands-on work with modern transformer-based detection: DETR family (Deformable DETR, DINO-DETR), self-supervised backbones (DINOv2), or equivalent and newer architectures. You can explain the tradeoffs and when to reach for each. Off-the-shelf YOLO fine-tuning or LLM-generated pipelines you can't defend line by line are not what we're looking for.

• Low-data expertise: transfer learning, self-supervised / semi-supervised / few-shot, augmentation, active learning. You know how to get a strong model from a small labeled set.

• Synthetic-data training: you've trained on simulated data and closed the sim-to-real gap in a real system.

• Edge deployment: NVIDIA Jetson / AGX Orin, TensorRT, ONNX, quantization/pruning, latency and memory optimization.

• PyTorch fluency and strong Python.

• MLOps ownership: experiment tracking (W&B/MLflow), data/model versioning (e.g. DVC), reproducible pipelines.

• Self-direction: comfortable as the only ML person, working through ambiguity, owning decisions.

• Strong async written communication, and several hours of daily overlap with US Pacific time.

Strong plus

• Ground-penetrating radar, radar/signal processing, or geophysics.

• 3D / point-cloud ML.

• Cloud training and serving (AWS or GCP).

• C++ for edge/performance work.

• ROS2 or robotics/perception exposure.

• Track record of growing into a team lead.

Why this role is different

You are the ML function. What you build ships to a robot in the field, not a slide. You'll have datasets that don't exist anywhere else, hard problems worth solving, and the autonomy to solve them your way. If you want scope, ownership, and the chance to build an ML org from its first engineer up, this is that seat.

Logistics

• Location: Remote, India-based.

• Hours: Daily overlap with US Pacific time required.

• Start: Immediate.

• Compensation: ₹18-30 LPA CTC, based on skills and experience.

• Equity: strong stock options based on eligibility, performance and tenure.

Note: This role sits under Aerobotics7 Inventions Pvt. Ltd., our Indian entity. Compensation is aligned to Indian market standards. Our parent company is US-based; this position is for candidates residing and working in India.

How to Apply

Apply in Dover (Aerobotics7/41e08db2-c5a4-4bb6-be52-36c81ed012cd?rs=42706078) using your resume plus a GitHub or portfolio link (required). If any question email us at . A short note on relevant work is welcome but optional. If your best work is closed-source, tell us what you built and what you owned.

Originally posted on Himalayas