Research Engineer: Scalable ML Pipelines & Experiments

Seldon Technologies, Inc.; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Visa sponsorship

Relocation support

Health, dental, and vision coverage

Professional development budget

GPU access and Mac machine

Team off-sites and conferences

Transit cost support

Gym membership

Meal stipend

Job summary

Seldon Technologies, Inc. in San Francisco seeks an experienced ML Engineer to turn research prototypes into robust, tested data generation, training, and evaluation pipelines.

You will work alongside researchers to scale post-training systems and develop shared software for growing research. You will build distributed experiments, profile GPU workloads, add tests and experiment tracking, and contribute to tooling that ensures reproducibility as research expands.

Qualifications

• Strong Python programming skills and solid software engineering habits.
• Experience building and debugging software for model training, inference, or data processing.
• An understanding of machine learning experiments and how data, numerical precision, and implementation choices affect results.
• Good software engineering habits, including testing, profiling, version control, and documentation.
• You take initiative and collaborate effectively to solve problems.

Responsibilities

• Turn research prototypes into tested, reusable data generation, training, and evaluation pipelines.
• Build support for distributed experiments, including data loading, checkpointing, and collecting agent interactions.
• Profile workloads and improve GPU utilization, memory use, and data throughput.
• Add tests, experiment tracking, and debugging tools, and ensure engineering changes preserve experimental behavior.

Skills

Python

PyTorch/JAX

ML software development

Experimentation understanding

Testing & profiling

Version control

Documentation

Collaboration

Tools

PyTorch

JAX

Job description

Seldon Technologies, Inc. in San Francisco seeks an experienced ML Engineer to turn research prototypes into robust, tested data generation, training, and evaluation pipelines.

You will work alongside researchers to scale post-training systems and develop shared software for growing research. You will build distributed experiments, profile GPU workloads, add tests and experiment tracking, and contribute to tooling that ensures reproducibility as research expands.