About the RoleThis is a founding-team ML research engineering role at an AI training and evaluation company serving top frontier AI labs and enterprises. You'll build the research backbone from the ground up, sitting at the intersection of applied machine learning and systems engineering - with direct influence over technical culture and research direction.
What You'll Do- Design, train, and evaluate ML models including LLMs, diffusion models, and domain-specific architectures.
- Develop scalable experimentation pipelines spanning data, model training, and evaluation workflows.
- Collaborate with data and infrastructure teams to optimize training throughput and data quality.
- Contribute to open research, internal benchmarks, and emerging techniques in multimodal and generative AI.
- Rapidly prototype research insights and productionize them into usable tools and models.
- Set foundational standards for research rigor, documentation, and reproducibility across the team.
What We're Looking For- 3-10 years of experience in ML research, applied ML, or ML systems engineering.
- Deep familiarity with PyTorch, JAX, and/or TensorFlow.
- Strong command of model architectures including Transformers, Diffusion models, or RLHF.
- Solid foundations in data processing, distributed training, and evaluation metrics.
- Ability to translate research papers into working prototypes and production-ready code.
- Curiosity for emerging ML paradigms such as multimodality, synthetic data, self-learning, and agentic systems.
- Comfort operating in a fast-moving, early-stage environment where you're building from zero to one.
Compensation & BenefitsBase salary range:
$220,000 - $300,000 USD annually. Visa sponsorship is not available for this role.
LocationOn-site in
Mountain View, California, USA. Remote work is not available for this position.
