Loading...

Founding Applied AI Engineer (lead Personalization & Intelligence)

4 November 2025

🪄

Founding Applied AI Engineer (lead Personalization & Intelligence)

Empty
Empty

2 more properties

Why join koodos labs?

You should join koodos labs for the people and our mission.

We aspire to build the best team of the 2020s. Just like PayPal in the 90s, Google in the 00s, and Stripe in the 10s, we want to be known as “a place where it’s good to be from.” If you join us, we promise to be the best place to grow your career — with the best people you’ve ever worked with. We’re also working on a very bold mission.

We care deeply about helping people connect more deeply with themselves and others. And in the spirit of personal empowerment, we aim to invert the internet’s data model over time and put individuals in control of their digital identities.

Read more about working at koodos labs here.

What we’re building

We’re the Context Company. Context is what turns generic AI into personalized intelligence. It’s all the signals available to a product: environmental cues, device state, user history, intent. Context is what transforms a cold machine into something that knows and understands you.

koodos is building Shelf, a user-controlled data store used several times a week by millions of people to track what they’re consuming and keep up with what others are into. It connects to any platform, aggregates your credentials and consumption data, learns from your activity, and, eventually, will let you share that context with services for more personalized experiences. koodos labs lets you store your digital life in one place — then take it anywhere.

The future is personalized experiences powered by user-controlled context. We’re building a future where apps and services come to you and your memory—not the other way around. See our latest paper here.

The Opportunity

We believe personalization effects are the new network effects.

The better an experience understands and adapts to you, the more irreplaceable it becomes. Also, the smartest AI won’t live in a warehouse of GPUs, but right where your context already lives: on your device. The future is hybrid – cloud scale meets local intelligence.

We need someone who can define what understanding means — and build the systems that make it real.

This is a foundational hire. As the Founding Applied AI Engineer, you’ll work directly with our CEO, CTO and founding team to explore, define, and deliver the insights that power Shelf’s personalization, recommendations, and APIs for external partners. You’ll prototype fast, experiment broadly, and help shape the intelligence layer that makes Shelf’s context useful — for individuals, for developers, and for agents.

And the next leap in personalization won’t happen in the cloud — it’ll happen closer to you.

In this role you will

Define what’s valuable: Partner with product and leadership to explore what insights truly matter — what kinds of signals about taste, identity, and behavior create delight for users and utility for partners.

Prototype fast: Use frontier models (e.g., GPT-4/5, open-weight models) to extract early signals from Shelf’s data — testing hypotheses, not building infra too early.

Turn insights into intelligence: Build the first real-time pipelines that transform consumption data into predictions and preferences — across media domains and behaviors.

Collaborate across pods: Work closely with the Data Ingestion and Interface (product experience) teams to ensure the context aggregated via a user’s Shelf translates into real user understanding.

Lay the foundation for personalization at scale: Once high-value insights are found, work with ML and infra engineers to operationalize them — via fine-tuned models, lightweight inference, and robust evaluation.

Experiment, fine-tune, and reinforce: Design experiments around model prompts, configurations, and fine-tuning strategies. Apply post-inference optimization techniques like SFT, RLHF/RLAIF, and lightweight guardrail or RAG/graph-based systems to ensure outputs are high-quality, aligned, and ethical.

Continuously benchmark intelligence: Build feedback loops for evaluating model variants and personalization hypotheses — treating AI behavior like a living product feature, not a static model.

Shape our intelligence roadmap: Decide what to infer, what to remember, and what to expose — helping define the APIs and feedback loops that make Shelf context-aware.

Be a creative partner: Contribute to company-level strategy about how context and personalization evolve beyond Shelf — into developer APIs, agents, and the broader ecosystem.

We’re early stage, so you’ll have an outsized equity stake and an unparalleled opportunity to define how AI serves humanity. Engineers at koodos labs decide what gets built and why, in addition to figuring out how.

We’re NY-based (we have our own office in West Soho) and we work in-person.

Ideal Candidate

đź’ˇ
We recognize that a confidence gap might discourage amazing candidates from applying. Every job description is a wish list, so please reach out if this role really excites you.

You might be a great fit if you have:

Strong technical depth in Python and modern ML frameworks (PyTorch, JAX) — equally comfortable training and shipping models.

Hands-on experience turning data into product insight: You’ve built recommendation, personalization, or behavioral intelligence systems that users actually felt.

Comfort with open-weight models and orchestration tools (Ray, Airflow, Kubernetes, Slurm) — you know how to go from prototype to scalable pipeline.

Hybrid AI intuition: You’ve thought about where intelligence should live — cloud, edge, or both — and you’re excited about making those trade-offs real.

Fluency in inference frameworks (TorchServe, Triton, ONNX) and model optimization (quantization, distillation, pruning).

Curiosity for agentic systems: Familiarity with frameworks like DSPy or protocols like MCP/A2A — or excitement to explore them as we make Shelf more “context-aware.”

Product mindset: You measure success by user delight, not benchmark wins. You can reason about value, not just accuracy.

Experimentation DNA: You bring the same rigor to model behavior that great teams bring to UX. You’ve run controlled experiments (A/B, bandits, or prompt evaluations) and know how to balance speed of iteration with statistical confidence.

Startup readiness: You thrive in ambiguity, communicate clearly, and move fluidly between product, modeling, and infra.

Builder energy: You prefer prototyping over planning and iteration over analysis paralysis.

What you’ll do

Day 1: You’ll merge your first PR. We want our developer experience to be as smooth as possible, so this first day is a good test of how we’re doing there.

Day 7: You’ll ship your first inference optimization to production. Maybe you’ll cut latency by 30% with better caching, or implement request batching that doubles throughput. You’ll experience our full deployment pipeline and see real users benefit from your work.

Day 50: You’ll have shipped personalization that makes users say “how did it know?” You’ll have built the first version of our real-time recommendation system, integrated with our memory architecture, and created an API that third-party developers are excited to use. You’ll have established strong collaboration patterns with our Memory Engineer, shipping features that showcase intelligent context in action.

In the Future: You’ll define how the world experiences personalized AI. As our Memory Engineer expands what we can remember about users (with their permission), you’ll ensure that knowledge translates into delightful, instant experiences. You’ll build the inference infrastructure that makes “context liquidity” real — where any app can instantly understand and serve users perfectly. Your systems will prove that personalization effects are indeed the new network effects.

How we interview

We aim to move fast — typically two weeks end-to-end. We find this works best for both us and you!

And we focus on real-world experience, demonstrated ability, and references. No riddles or binary tree puzzles — just a thoughtful look at what you’ve done and where you’re headed.

Introduction: We’ll kick things off with a call with one of our co-founders.

Technical Screening: A conversation with our CTO to dig into your experience with ML systems, inference optimization, and personalization. We’ll discuss your thoughts on the evolution from traditional RecSys to context-aware AI.

Take-Home Exercise: A short presentation (video or written) on either: (1) a personalization system you’ve built and its impact, or (2) how you’d design instant personalization APIs for third-party developers.

Onsite: You’ll meet the team. We spread this across two days to reduce load:

Day 1: Systems design deep dive — build a personalization system that scales to millions while maintaining <50ms latency

Day 2: Lunch with the team, behavioral interview, and a collaborative session designing the interface between memory and inference systems

References: We’ll ask for 2–3 references, and may also reach out to others you’ve worked with (let us know if there are any sensitivities). We’ll keep it brief and respectful — usually ~15 mins per call. You’re also welcome to reference check us.

Decision: We’ll move quickly on our end.

Our Team

We’re a small, but mighty team with backgrounds at YouTube, Coinbase, Harvard, and Cambridge, as startup founders and as early members at companies like Improbable and Lyft. We’ve come together around a shared vision and are dedicated to creating important and positive experiences for cyberspace.

We’re well resourced (unannounced rounds) & backed by top-tier investors, including the backers of companies like Airbnb, Pinterest, Snap, and Twitter, as well as the founders of companies like Zynga, VSCO, and Scale and the people behind artists like Miley Cyrus, Justin Bieber, Lorde, Logic, and Panic! At The Disco, and many others. Our team is advised by the founders of Pinterest, Dubsmash (now Reddit), as well as pioneers of digital marketing and market design from Harvard.

We care about building a genuinely diverse team. We are a majority-first gen immigrant team and sponsor visas — we think that’s important as we build towards enabling easier digital migration. We share the same values of individuality, empathy, reliability, kindness and humility. One big overlap among our life experiences is contrasts: contrasts between our own upbringing and the world around us, contrasts between what was expected of us and what we ended up pursuing and our bringing together of contrasting, interdisciplinary worlds.

More about us here.

How to apply

If interested, please drop us a line on joinus@koodos.com with your resume and your thoughts on how personalization could work for Shelf.

FAQs

Where will I work?

What tech stack do you currently use for ML/inference?

What’s unique about personalization at koodos?

Are you hiring interns?

Are you open to part-time?

Where can I find more info?

Do you sponsor visas?

Employment Type
On-site

Related Jobs

Other similar jobs that might interest you