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Founding Engineer – ML Research
CleraFounding ML Research Engineer at a well-funded AI/ML startup in Bay Area. Building and scaling AI research backbone with focus on model performance and data quality.
Posted 7/10/2026full-timeMountain View • California • 🇺🇸 United StatesMid-LevelSenior💰 $220,000 - $300,000 per yearWebsite
Tech Stack
Tools & technologiesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- Design, train, and evaluate ML models — including LLMs, diffusion models, and domain-specific architectures.
- Develop scalable experimentation pipelines spanning data ingestion, model training, and evaluation workflows.
- Collaborate with data and infrastructure teams to optimize training throughput and model quality.
- Contribute to open research, internal benchmarks, and new techniques in multimodal and generative AI.
- Rapidly prototype research ideas and productionize them into usable models and tools.
- Define and promote standards for research rigor, documentation, and reproducibility across the engineering org.
Requirements
What you’ll need- 3–10 years of experience in ML research, applied ML, or ML systems engineering.
- Deep familiarity with PyTorch, JAX, or TensorFlow and hands-on experience with architectures such as Transformers, Diffusion models, or RLHF.
- Strong foundations in data processing, distributed training, and evaluation metrics.
- Demonstrated ability to move from research papers to working prototypes to production-ready code.
- Curiosity about emerging ML paradigms — multimodality, self-learning, synthetic data, and agentic systems.
- Passion for building from zero to one in a high-velocity startup environment.
- Nice to have: Open research contributions, published benchmarks, or peer-reviewed publications in relevant ML research areas.
Benefits
Comp & perks- Equity participation as a founding team member
- Visa sponsorship: Not available — candidates must be authorized to work in the US
ATS Keywords
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Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine LearningModel TrainingData ProcessingDistributed TrainingEvaluation MetricsPrototypingProduction-Ready CodeDiffusion ModelsReinforcement Learning from Human FeedbackEmerging ML Paradigms
Soft Skills
CuriosityPassion for InnovationCollaboration