Most people will hear
about us in 2027.

About the role

We are hiring an engineer who moves fluently between applied research and production. This role owns the end-to-end quality loop for our conversational systems — agent design, evaluation methodology, prompt and context engineering, dataset curation, and quantitative analysis of model behavior on multi-turn tasks. You will set the bar for what our product can do, and defend that bar with data.

What you'll do

• Design and ship agents that handle multi-turn dialogue in noisy, real-world conditions.
• Build rigorous evaluation suites — structured benchmarks, regression sets, and principled failure-mode analysis — that compound quality over time.
• Own the full quality loop, from dataset curation to prompt design to post-hoc error analysis.
• Make principled tradeoffs between latency, cost, and quality across model providers.
• Drive roadmap and architectural decisions based on rigorous experimentation, not intuition.

You're a good fit if you

• Hold an MS or PhD in Computer Science, Machine Learning, or a closely related field (greatly preferred).
• Have deep, hands-on experience with modern language models in production — not demos.
• Are an expert in Python and comfortable operating in a fast-moving, high-ownership codebase.
• Can design an evaluation, defend its methodology, and articulate its limitations.
• Have a track record of shipped, published, or open-sourced work that demonstrates depth of craft.

Strong candidates also have

• Background in reinforcement learning, fine-tuning, or post-training.
• Prior work on agentic systems or structured extraction from unstructured inputs.
• Peer-reviewed publications or substantive open-source contributions.

About the role

This is a high-accountability role for a closer who has carried a real enterprise quota and wants early-stage leverage — the ability to define a motion rather than inherit one. You will own complex, multi-stakeholder sales cycles end-to-end, translate field insight directly into product direction, and help hire the team that follows you.

What you'll do

• Own the full sales cycle — prospecting, discovery, technical demo, procurement, close — for enterprise accounts.
• Navigate multi-stakeholder buying committees and long, nonlinear sales cycles with discipline and patience.
• Build and iterate on the sales playbook, qualification criteria, and ideal-customer-profile definition.
• Partner tightly with product and engineering to convert field insight into roadmap signal.
• Hire, onboard, and coach the account executives who come after you.

You're a good fit if you

• Have 7+ years of closing experience in enterprise B2B SaaS with a consistent, documented record of exceeding quota.
• Have personally closed six- and seven-figure deals across multi-month sales cycles with executive buyers.
• Are an exceptional communicator — written, verbal, and presentation — and can hold your own with CFOs, CIOs, and operators.
• Hold a Bachelor's degree at minimum; an MBA or advanced degree is strongly preferred.
• Thrive in ambiguity and operate at the pace of a venture-backed early-stage company.

Strong candidates also have

• Prior experience as an early GTM hire at a company that scaled from seed through Series B or beyond.
• A technical or analytical background alongside sales experience.
• A network of senior operators you can draw on from day one.

About the role

We are hiring a senior platform engineer who has built production systems in which reliability, latency, and correctness are all non-negotiable. You will own the platform end-to-end — data plane, control plane, deployment, observability, and the on-call culture that keeps everything running. This is a high-autonomy role with a correspondingly high bar.

What you'll do

• Own the reliability, scalability, and performance of our production platform.
• Set the standard for observability, incident response, and operational rigor across the company.
• Lead architectural decisions that compound over years, not quarters.
• Design for strict latency budgets under unpredictable, bursty load.
• Own the deployment and delivery pipeline end-to-end.
• Establish the engineering practices — code review, testing discipline, release hygiene — that scale with the team.

You're a good fit if you

• Hold an MS or PhD in Computer Science (greatly preferred); exceptional candidates with a BS and equivalent production experience will be considered.
• Have 7+ years of backend or platform engineering experience at companies operating latency-sensitive or real-time systems at meaningful scale.
• Are an expert in Python.
• Have a first-principles understanding of distributed systems, storage, and networking.
• Have owned production systems end-to-end — not just written code that ships, but carried the pager for it and raised the bar on operational excellence.

Strong candidates also have

• Published technical writing, peer-reviewed papers, or substantive open-source contributions.
• Prior founding-engineer or very-early-stage experience.
• Deep experience with high-throughput messaging, queueing, or event-driven architectures.
• Fluency in modern observability tooling and the discipline to use it well.