Founding Engineer – ML Research

Clera

Founding 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

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in designing and evaluating machine learning models, particularly in multimodal and generative AI, with a strong focus on productionizing research into scalable solutions. Proficient in optimizing training processes and ensuring research rigor and reproducibility across engineering teams.

Highest-signal resume keywords

Machine Learning ResearchPyTorchTensorFlowModel Evaluation MetricsTransformers

ATS Keywords

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Hard Skills

Machine LearningModel TrainingData ProcessingDistributed TrainingEvaluation MetricsPrototypingProduction-Ready CodeDiffusion ModelsReinforcement Learning from Human FeedbackEmerging ML Paradigms

Soft Skills

CuriosityPassion for InnovationCollaboration

Tools & Technologies

JAXExperimentation PipelinesOpen Research Contributions

Industry Keywords

Generative AIMultimodal AISynthetic DataAgentic SystemsResearch Rigor

Tech Stack

Tools & technologies

PythonPyTorchTensorflow

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