Member of Technical Staff - Research
About Pointer
Pointer is building the interface between humans and software. Our product lives inside other apps — integrating in minutes, crawling the UI, and reading docs to understand how the product works. Once embedded, it guides users, answers questions, and helps them get things done in real time. The vision is simple: software that understands what you’re trying to do, and just does it.
We’ve recently raised our seed round from incredible investors (to be announced). Now we’re looking for a small group of gritty, fast-learning engineers to help invent this new layer of the stack.
About the role
You’ll own how Pointer thinks — the intelligence that powers our guidance, copilots, and predictions. You’ll work on everything from inference optimization to RL training loops, building systems that make Pointer smarter every week.
This is a role for someone who thrives on turning messy real-world data into production-ready intelligence. You’ll design experiments, run evaluations, and ship models.
Why this is exciting
Pointer is one giant closed loop: observe → understand → guide → learn. You’ll be tightening that loop and making it smarter. You’ll be working on challenges like:
Cutting latency to sub-500ms without sacrificing quality
Designing RL and search-based systems that learn which guidance actually leads to successful user outcomes
Building eval harnesses that run on both synthetic and live traffic, and actually predict real-world performance
Building models that can reason over arbitrary UI structure
Feeding cleaned behavioral data back into training loops to make Pointer more helpful and less noisy over time
What you’ll work on
Design and run experiments to improve Pointer’s speed and accuracy
Prototype and optimize models (LLMs, SLMs, embeddings) for latency and reliability
Develop memory systems that improve Pointer context
Instrument user behavior and feed the data back into training loops for continual improvement
You’ll be a good fit if
You’re strong in Python and ML frameworks, particularly PyTorch
You think in terms of evals and metrics and care about what they mean in production
You’re excited by messy data and figuring out how to make it useful
You like working across the stack: inference optimizations, data pipelines, fine-tuning, and model monitoring
You have experience with RL, retrieval systems, quantization, distillation, and model infra
You've published ML papers or contributed to OSS projects
Average salary estimate
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