ML Lead / Principal Research Scientist – Domain Foundation Models

H2LooP; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

H2LooP, Bangalore, is seeking a Founding Engineer / ML Lead to shape the architecture and engineering culture from day one, building domain-aware foundation models for embedded systems.

You will own data strategy, data preparation, and evaluation, bridging ML research with real hardware deployment in automotive, UAV, and IoT contexts.

PhD preferred; leadership with high ownership; opportunity to define a new category of AI for system engineering.

Job description

PhD Preferred | Founding Engineer Role

Location: Bangalore India
Type: Full-time | Founding Leadership Role

Why This Role Matters

H2LooP is building a category-defining AI platform for system engineers — not generic copilots, but engineering-grade foundation models that understand specifications, constraints, and real hardware behavior.

Our core team comes from Google, Toshiba, Cisco, Bosch, Philips, and NXP, bringing deep experience across AI, semiconductors, automotive, telecom, and embedded systems.

As a Founding Engineer, you will shape the architecture, technical direction, and engineering culture of the company from day one. This is a rare opportunity to define how domain foundation models are built — and see them deployed in real engineering environments.

Role Overview

We are looking for a ML Lead / Principal Research Scientist to own the data, evaluation, and learning strategy for domain-aware foundation models powering embedded, automotive, UAV, and IoT development.

This role sits at the intersection of machine learning research, system engineering knowledge, and real-world deployment. You will turn complex engineering artifacts into learning signals that enable models to reason about specs, constraints, safety, and execution behavior — not just generate statistically plausible code.

If you enjoy foundational research with direct industrial impact, this role is for you.

What You’ll Build & Own

1. Data Strategy for Domain Foundation Models

• Define the end-to-end data strategy for training domain-aware foundation models.

• Design domain vs. general data mixtures to balance specialization and generalization.

• Establish curriculum-style learning progressions aligned with real engineering workflows.

• Own data versioning, lineage, and reproducibility across model iterations.
  

2. Data Preparation & Knowledge Structuring

• Lead pipelines that transform complex technical artifacts into model-ready representations.

• Align engineering documentation with code, behavior, and constraints.

• Drive approaches for semantic structuring and retrieval of engineering knowledge.

• Guide synthetic data strategies to expand rare but critical engineering patterns.
  

3. Domain-Specific Intelligence

• Standardize heterogeneous engineering inputs:

  • Technical documentation

  • Embedded and firmware codebases

  • Open engineering stacks and SDKs

• Preserve implicit engineering logic such as timing, safety, and system constraints.

• Curate negative and failure-mode datasets reflecting real-world engineering mistakes.

• Maintain high-quality datasets from open ecosystems (RTOS, middleware, control stacks).
  

4. Instruction & Workflow-Aligned Datasets

• Design instruction and multi-turn datasets reflecting real engineering problem-solving.

• Define task families such as:

  • Specification-to-code translation
  • Code repair and validation
  • Configuration, integration, and debugging

• Establish quality, correctness, and compliance filters aligned with engineering standards.

• Ensure tight integration with developer tools and workflows.
  

5. Feedback-Driven Learning & Evaluation

• Define strategies to incorporate execution feedback and system signals into learning loops.

• Work closely with system engineers to integrate human feedback into training datasets.

• Build domain-specific evaluation benchmarks beyond generic LLM metrics.

• Track metrics that reflect correctness, recall, safety, and engineering reliability.
  

Who You Are

Required

• PhD (or equivalent research depth) in ML, Systems, CS, or a related field.

• Strong background in data-centric ML or foundation model development.

• Experience working with complex, technical, or domain-heavy datasets.

• Ability to operate in high-ambiguity, early-stage environments.

• Passion for building systems that move from research → production → real users.

Preferred

• Exposure to embedded systems, automotive, robotics, telecom, or safety-critical software.

• Experience designing evaluation methodologies for domain-specific models.

• Familiarity with engineering constraints, standards, or system-level reasoning.
  

Why Join H2LooP

• Build foundational technology, not incremental tooling.

• Define a new category of AI for system engineering.

• Work with a team that has shipped systems at Google, Bosch, Cisco, Philips, NXP, and Toshiba.

• See your work deployed on real hardware, real codebases, and real engineering teams.

• High ownership, deep technical autonomy, and direct influence on company direction.