What are the responsibilities and job description for the ML Engineer (ONSITE IN SF) position at PulseRise Technologies?
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:
You will:
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
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.
- Reliable implementation of novel architectures
- Distributed transformer training at scale
- Training stability and performance debugging
- Evaluation frameworks
- Optimization reasoning alongside researchers
- 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
- Megatron-LM, DeepSpeed, xformers
- End-to-end pipeline ownership
- Research-engineering team experience
- Mathematical depth (optimization, information theory, etc.)
- Competitive programming / theory-heavy background