ML Engineer (ONSITE IN SF) at PulseRise Technologies

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

31 Jul, 26

Salary

0.0

Posted On

02 May, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

PyTorch, JAX, Transformer training, Distributed training, Debugging, Optimization, Learning dynamics, Research implementation, Megatron-LM, DeepSpeed, Xformers, Pipeline development, Information theory

Industry

Information Services

Description

Dear applicants, please keep in mind that applications without provided salary expectations and active LN profile will not be considered. Hope for your understanding. Location: San Francisco, CA (In-person) Employment Type: Full-Time Equity: 0.5% – 1% Visa: Not available Experience: 1+ years (exceptional new grads welcome) We are hiring ML Engineers to implement research ideas reliably and operate full training pipelines end-to-end. This is not a research-only role. This is research-engineering at scale. A seed-stage research-driven ML company focused on mechanistic understanding of model architectures and optimizers. The team studies: Optimizer–architecture co-design Orthogonalized optimizers and manifold-based training Sparse attention mechanics Data-efficient reasoning models Learning dynamics in data-sparse regimes The environment blends academic rigor with industrial compute and speed. The team is deliberately long-term oriented and avoids premature commercialization pressure. You will: Translate research papers into working PyTorch/JAX implementations Run distributed transformer training Debug divergence and instability Optimize throughput Build full pipelines (data → training → evaluation) Reason about learning dynamics and architecture tradeoffs The bar is slope and research intuition, not years. What You’ll Own Reliable implementation of novel architectures Distributed transformer training at scale Training stability and performance debugging Evaluation frameworks Optimization reasoning alongside researchers Must-Have Requirements Strong PyTorch or JAX proficiency Hands-on transformer training experience Experience with distributed training setups Debugging divergence and instability Ability to read and implement research papers Research intuition around optimization and learning dynamics High growth slope Nice to Have Megatron-LM, DeepSpeed, xformers End-to-end pipeline ownership Research-engineering team experience Mathematical depth (optimization, information theory, etc.) Competitive programming / theory-heavy background

Responsibilities

You will translate research papers into functional PyTorch or JAX implementations and operate full-scale distributed transformer training pipelines. Additionally, you will be responsible for debugging training instability and optimizing throughput for research-driven model architectures.