MTS - Research
RemoteIndia only
- Role
- AI / ML
- Employment
- Full-time
$90k–$150k/yr
Check eligibility
Open to IN only. Set where you work from to check your eligibility.
No BS summary
Research-oriented MTS in Bengaluru to build AI evaluation environments, post-training runs, and scalable ML infrastructure. Needs strong software engineering, Python-friendly CLI work, foundation-model fluency, and research/experiment experience.
Core skills
PythonReinforcement LearningFoundation Models
Required skills
CLICPTSFTRL
Optional skills
RLHFRLAIF
ABOUT THE ROLE
We are looking for a Member of Technical Staff (MTS - Research) to help us build the data engine for frontier AI. In this role, you will bridge the gap between frontier research and production engineering. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. Your work will involve iterating on novel RL approaches and translating them into robust, scalable infrastructure that moves the needle on real-world model metrics.
RESPONSIBILITIES:
- Build Agentic Environments: Design and implement the next generation of "SimLabs", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
- Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
- Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
- Rapid Iteration: Debug and iterate across the full ML stack, from infrastructure to model behavior, ensuring our tools remain "command-line first" and developer-friendly.
- Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
ABOUT YOU
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
- Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated "proof of work" through significant open-source contributions or industry experience.
- Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
- ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
- Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
WHAT WILL MAKE YOU STAND OUT
- Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
- Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
- Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
- Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
- Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver "frontier performance" on open-source benchmarks.
What you'll do
- Build Agentic Environments: Design and implement the next generation of "SimLabs", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
- Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
- Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
- Rapid Iteration: Debug and iterate across the full ML stack, from infrastructure to model behavior, ensuring our tools remain "command-line first" and developer-friendly.
- Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
What they require
- Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated "proof of work" through significant open-source contributions or industry experience.
- Engineering Rigor: A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
- ML Fluency: A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
- Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.
- Preferred: Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
- Preferred: Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
- Preferred: Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.
- Preferred: Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.
- Preferred: Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver "frontier performance" on open-source benchmarks.
AI
$90k–$150k/yr