Founding ML Engineer

About Index:

We're building the protocol for private, intent-driven discovery—where agents work on your behalf, match you with the right people, and coordinate actions based on unstructured signals like curiosity, goals, and timing. Think programmable introductions, reputation-driven matching, and context-aware ambient discovery.

Now, we're looking for a Founding ML Engineer to help us push that vision forward.

You won't just be training models—you'll be building learning agents that integrate into real-world social protocols, powering systems that act, remember, and reason. You'll prototype fast, test with real users, and iterate in the wild.

We're looking for product-minded ML engineers who excel at bridging backend infrastructure with agentic behavior patterns, and developers who embrace ambiguity and take pride in building seemingly impossible solutions that eventually succeed.

What You'll Work On:

Building and fine-tuning learning agents for agentic behavior (intent inferring, semantic matching, episodic memory, contextual reasoning)

Implementing reward modeling and preference learning for agent alignment in social contexts

Turning experimental agent behavior into generic SDKs and agent services

You Might Be A Fit If You:

Have 3+ years of hands-on ML experience with PyTorch/TensorFlow/JAX and transformer architectures

Have successfully replicated and implemented cutting-edge ML research papers

Built and scaled production ML systems with robust inference pipelines

Experience with RLHF, PEFT, and other post-training optimization techniques

Strong MLOps background with proven ability to deploy and monitor ML systems

Proficient with Git, Docker, and Unix development environments

Active in the open source AI community and familiar with emerging technologies

Are NYC-based or open to being around the team regularly

Bonus Points:

Skilled in designing incentive-aligned systems using reward models.

Curious about emergent behavior, swarm dynamics, and reinforcement learning in multi-agent environments.

Curiosity about contextual privacy or differential privacy

Background in philosophy, linguistics, cognitive science—or anything that helps understand meaning and context

What We Offer:

Competitive salary and equity

Medical insurance coverage for you, with partial support for dependents

A hybrid setup with office space and remote flexibility—whatever helps you do your best work

Yearly team offsites to connect, align, and have some fun

Opportunities to attend and speak at international conferences

We're early, fast, and a little wild. If that sounds like your speed, we'd love to talk.