Interaction Logo

Interaction

MTS - Research

Reposted One Month Ago
Be an Early Applicant
In-Office
Palo Alto, CA, USA
Mid level
In-Office
Palo Alto, CA, USA
Mid level
Join a small, high-density engineering team to harden and scale an agent platform. Work spans infra, product, extensibility, AI runtime/evals, and post-training/alignment. Stabilize high-concurrency, low-latency distributed systems while shipping features and designing platform primitives to enable future use cases.
The summary above was generated by AI


We’re the makers of Poke.com, a proactive AI agent for everyday life. Interaction is a $300M consumer company backed by $27M from General Catalyst and angels such as Guillermo Rauch (Vercel), Scott Wu (Cognition), Patrick and John Collison (Stripe), Fred Ehrsam (Coinbase), Ken Howery (Co-Founder of PayPal and Founders Fund), and many others.

 

The Context

For the last year, our core engineering team has built this entire agent platform from the ground up. Now, traffic and concurrent agent executions are scaling by orders of magnitude. We need engineers to harden infrastructure without slowing down feature velocity.

 

We only care about technical depth, obsession with your craft, and how well you can build alongside us. Our current team is strongly connected and talent-dense, with backgrounds from Jane Street, MIT/Stanford research, and International Olympiads, but we care more about experience building than your pedigree.

 

The Role

We hire across many strengths under one title. You are a T-shaped engineer who can navigate the whole stack, but you are uniquely experienced in at least one of these areas:

  • Scale & Infra: High-concurrency, low-latency, distributed state under heavy load.

  • Product: Prototyping, shipping, and iterating on agentic workflows zero-to-one.

  • Extensibility & DevTools: APIs, SDKs, and primitives that make Poke programmable by external developers.

  • AI Runtime & Evals: Tool use, model selection, and output quality in production.

  • Post-Training & Alignment. SFT, preference optimization, reward modeling, synthetic data generation, eval design, and improving agent performance through training rather than prompting.

 

We work mostly in Typescript and some Python, but lack of experience in these specific languages is not a deal breaker.

 

What we're looking for

  • Rigor under pressure. You move between infra, product, and debugging. You can stabilize melting systems while still shipping features.

  • Platform thinking. You design primitives, not one-off features. You build solutions that solve today's product needs while unlocking ten future use cases.

  • Low ego. The talent bar is high, but no important problem is beneath us.

 

Something Else

If you don't fit perfectly into a bucket, that’s fine. We care more about ownership and technical depth over specific labels.

 

Tell us what you are unusually good at, what systems you’ve owned, and why it transfers here. Include a link to your GitHub, a project you're proud of, or a breakdown of the hardest technical problem you've solved.

Similar Jobs

29 Days Ago
In-Office
San Francisco, CA, USA
Senior level
Senior level
Artificial Intelligence • Logistics • Machine Learning • Software
Develop and productionize machine learning models for supply chain forecasting and optimization. Responsibilities include creating model architectures, curating proprietary real-world data, integrating LLM reasoning and tool use, designing evaluations, and continuously improving performance as market conditions change. The role owns the full research cycle from hypothesis through production and requires collaboration in a fast-moving, in-person environment.
Top Skills: Large Language Models (Llms)Machine LearningMathematical ModelingOptimizationTime-Series Forecasting
29 Days Ago
In-Office
San Francisco, CA, USA
Entry level
Entry level
Artificial Intelligence • Logistics • Machine Learning • Software
Develop and train machine learning models for supply chain forecasting and optimization. Build proprietary data assets, integrate LLM reasoning capabilities, and improve model performance as market conditions change. The role owns the full research cycle from hypothesis through production deployment and requires working with complex, heterogeneous real-world data in a fast-paced, in-person startup environment.
Top Skills: Large Language Models (Llms)Machine LearningOptimizationTime-Series Forecasting
One Month Ago
In-Office
San Francisco, CA, USA
175K-275K Annually
Mid level
175K-275K Annually
Mid level
Artificial Intelligence • Computer Vision • Hardware • Software
Research Scientist developing vision-language models for construction-site intelligence. Responsibilities include training and evaluating deep learning models, building video and multimodal benchmarks, designing real-world performance metrics, and deploying optimized models to edge, on-premises, and cloud environments. The role involves end-to-end experimentation, long-context video reasoning, post-training, inference optimization, production feedback analysis, and collaboration with research, infrastructure, and hardware teams.
Top Skills: DistillationJaxLoraMlopsMultimodal ArchitecturesPythonPyTorchQuantizationReinforcement LearningSftSparsityTransformer ArchitecturesVideo UnderstandingVision-Language Models

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account