Enterpret
At Enterpret, we're building customer feedback intelligence for product development teams. We are solving complex problems in natural language processing, serverless computing, analytics on frontend, pushing the envelope of what’s possible by applying first principle thinking.
We are backed by some of the most storied VC firms like Kleiner Perkins, and Sequoia Capital who share our conviction. We are also lucky to have some of the most respected product development teams like Notion, Loom, and Cameo, as our customers.
We love working with folks who are resourceful, thrive in ambiguity, and display a strong sense of ownership.
If have a role in mind that is not mentioned below, you can also reach out to us at recruiting@enterpret.com
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About Enterpret
Enterpret is at the forefront of AI-native applications, unlocking the power of customer feedback for businesses. We centralize feedback from every channel and transform it into actionable insights that drive customer-centric decisions for teams at the world's leading companies like Perplexity, Notion, Canva, and Figma. Backed by investors such as Kleiner Perkins and Peak XV, we're redefining how businesses understand and act on the voice of their customers.
Read more about our team, core values, and operating principles - here.
What You’ll Do ?
- Build and scale core backend systems that power our customer feedback intelligence platform.
- Take ambiguous product problems and turn them into clear backend systems with strong data and correctness guarantees.
- Own critical system decisions around data models, event flows, and service boundaries.
- Build and harden ML and NLP infrastructure so features work reliably in real customer environments.
- Balance speed, cost, and long-term system health while shipping continuously.
- Instrument systems deeply to detect failures early and learn from production behaviour.
- Influence technical direction by identifying leverage points and proposing platform-level improvements.
What It Takes ?
- 1-3 years in software engineering, with a proven record of building and shipping reliable systems
- Proven ability to design and ship backend systems that operate at scale and stay reliable in production.
- Strong systems thinking: you reason clearly about data models, boundaries, failure modes, and tradeoffs.
- Track record of owning complex projects end to end, from problem framing to production impact.
- Comfort working in ambiguity and making decisions with incomplete information.
- Depth in at least one core backend or infra area and the ability to go deep when things break.
- High engineering judgment: you know when to move fast and when to slow down.
- Strong product sense: you understand how technical decisions affect users and business outcomes.
- Obsession with quality, correctness, and long-term maintainability.
- Ability to influence and raise the bar through code, reviews, and mentorship.
- Bias toward action, learning, and continuous improvement.
Why Enterpret?
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High Impact: Build foundational systems at an early-stage startup where every line of code matters.
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Ownership: End-to-end responsibility for features and systems.
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Complex Challenges: Work on state-of-the-art ML platforms, distributed infra, and real-time analytics.
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Growth: Learn and grow with a high-caliber team and expand into leadership or deep technical expertise.
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Culture: Open, collaborative, and values-driven environment with autonomy.
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Benefits: Competitive salary, equity, premium healthcare, and more.
What We Value ?
At Enterpret, we operate with a deep sense of ownership - we play for the team and do what it takes to win together. We care personally for our teammates while pushing each other with honest, actionable feedback. Above all, we approach everything with humility and a drive to keep learning and getting better.
Equal Opportunities
We are an equal opportunity employer. We ensure that none of our employees or prospective employees receives less favourable treatment as a result of age, sex, disability, marital status, colour, race, religion or ethnic origin. Equally we aim to ensure that no such employee is disadvantaged by terms and conditions of employment which cannot be justified.
Ready to apply?
Apply to Enterpret
About Enterpret
Enterpret is redefining how businesses understand and act on customer feedback. We are building an AI-native platform that centralises feedback from every source surveys, reviews, support tickets, communities and turns it into clear, actionable insights that drive business growth.
We are trusted by some of the world’s most customer-obsessed companies like Canva, Descript, Notion, Perplexity and many more, and backed by leading investors like Kleiner Perkins, Canaan Partners, and Peak XV Partners. Our mission: to unlock the voice of the customer for every product team on the planet.
About the Role
This is a hands-on role for someone who’s equally strong in research thinking and engineering execution. You will define what “good” looks like for AI-powered features, build systems that meet that bar, and own them through launch and beyond. From writing eval plans to debugging failures in production, you will work across the stack and across functions to ship reliable, high-quality LLM-driven systems.
What You Will Do ?
You will design, build & ship AI-backed features that are reliable in production
- Define the quality bar: design eval rubrics, test plans, and rollout criteria. Make sure they’re measurable and enforced.
- Build with real-world constraints: write and extend production code, set up monitoring, and add tests that catch regressions before users do.
- Own features end to end from problem framing to modeling, from system design to rollout and iteration.
- Debug failures across the stack including data, infra, model, prompt logic and harden the system with what you learn.
- Design and implement systems: retrieval pipelines, agents, or hybrid patterns, based on what the problem actually needs.
- Work across functions: collaborate with product, infra, and engineers to ship features that actually stick.
You have built and shipped AI systems before and carried the load when things broke post-launch.
- Strong research instincts: you are good at defining what “working” means and designing evaluations that reflect real-world usage.
- Solid engineering skills: you write clean, testable Python, debug at system boundaries, and know your way around production stacks.
- LLM understanding: you have worked with modern models and know how to prompt, fine-tune, or wrap them with tooling and evaluation.
- Systems mindset: you think in interfaces, data contracts, failure modes, and rollout plans, not just model tweaks.
- Practical bias: you care more about what ships and survives than what’s novel.
- Ownership: you take initiative, communicate clearly, and push for quality without being asked.
- We build AI systems that people can trust because they have been tested, monitored, and hardened through real usage.
- We care deeply about quality. Eval plans, incident retros, and per-tenant guardrails aren't checkboxes, they're the core of how we build.
- We believe research belongs in production, not just papers. You will see your ideas live in running systems within weeks, not years.
- We are small enough that every engineer matters, and focused enough that there's no busywork, just high-impact problems.
- You will be part of a team that runs toward hard, ambiguous challenges and sees them through to working, reliable systems.
- There is no playbook here, we are writing it as we go. If that excites you, not scares you, you will thrive here.
Ready to apply?
Apply to Enterpret
About Enterpret
Enterpret is at the forefront of AI-native applications, unlocking the power of customer feedback for businesses. We centralize feedback from every channel and transform it into actionable insights that drive customer-centric decisions for teams at the world's leading companies like Perplexity, Notion, Canva, and Figma. Backed by investors such as Kleiner Perkins and Peak XV, we're redefining how businesses understand and act on the voice of their customers.
About the Role
As LLMOps Architect at Enterpret, you'll be responsible for how LLM models are fine-tuned, how prompts are managed, how we run evals, how we optimize for cost, and how we optimize for speed—both at the experimentation stage and in production. This is a foundational, high-ownership role where you'll work directly with the OpenAI and Anthropic teams (whom we partner with closely), as well as AWS (whom we partner with closely), to build world-class ML infrastructure.
You'll work closely with ML researchers, backend engineers, and product teams to ensure our AI systems are resilient, secure, and cost-efficient as we grow 10x. Key success metrics include improving the speed of experimentation, time to productionization, and the quality of models. You'll report directly to the CTO.
What You'll Do
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Design and evolve Enterpret's ML platform for training, serving, and retraining our encoders and LLM models using AWS/Terraform/OpenAI/Anthropic.
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Build CI/CD pipelines tailored for ML—including model versioning, testing, canary releases, rollbacks, and gated production deploys.
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Deploy and manage model serving systems for both real-time inference (e.g., tagging support tickets on the fly) and batch pipelines (e.g., analyzing historical product feedback).
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Set up observability for model performance and data drift—using Braintrust and custom alerts to catch issues before they affect customers.
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Lead incident response, root cause analysis, and postmortems for ML systems—ensuring uptime for insights that product teams rely on, alongside governance and security.
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Track and optimize cloud usage for ML workflows, making model delivery cost-aware and aligned with product usage.
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Implement governance and security across the stack—owning IAM, data access, auditability, and model explainability where needed.
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Partner with ML and product teams to productionize GenAI and AI models powering our Knowledge Graph and Adaptive Taxonomy engine, tackling problems on retrieval, encoder LLM fine-tuning, and reinforcement learning.
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Evaluate tools for model registry, feature stores, and orchestration—and build where needed to keep the feedback loop fast.
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Champion best practices in MLOps across the org—mentoring engineers and setting scalable foundations for the future.
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Act as a coach to our team of researchers who are transitioning into engineering, helping them self-serve their capabilities and self-service these tools rather than doing it yourself.
What It Takes
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A minimum of 6 years' experience in MLOps and ML infrastructure, ideally with exposure to designing, deploying, and scaling machine learning systems in fast-paced, product-driven environments such as startups or high-growth companies.
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Deep expertise with AWS (SageMaker, EC2, EKS, S3, IAM), infrastructure-as-code (Terraform), and container orchestration (Docker, Kubernetes).
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Strong Python skills, with bonus points for Go, Bash, or Rust scripting where appropriate.
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Hands-on experience with CI/CD systems like GitHub Actions, ArgoCD, or Jenkins—especially for ML model delivery.
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Proven ability to monitor and maintain production ML systems, including model drift, latency, uptime, and alerting.
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Comfort with cloud cost optimization, resource provisioning, and auto-scaling for ML-heavy environments.
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Familiarity with model serving stacks and experimentation tools (MLflow, Langsmith, etc.).
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Bonus: exposure to GenAI workflows (LangChain, vector DBs, RAG), encoder/LLM model tuning, reinforcement learning, or responsible AI practices.
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Track record of mentoring, collaborating across functions, and taking full ownership of systems in production.
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You hate repeated manual work and have a strong drive to automate everything.
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Proficiency with AI coding agents like Claude and Cursor to work multiple times more effectively than normal.
Why Enterpret
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ML at the Core: You won't be supporting ML—you'll be enabling the core product.
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High Impact, Early Ownership: Define our MLOps foundation, influence every model's path to production, and shape how product teams experience insights.
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Work With Sharp People: Collaborate with researchers, engineers, and product builders solving complex problems every week.
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Focused, Fast Environment: No heavy process—just smart, principled builders shipping high-quality infrastructure.
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Comp + Culture: Competitive salary, meaningful equity, full-stack healthcare, generous leave, and a team-first culture built on trust and ownership.
What We Value
At Enterpret, we operate with a deep sense of ownership — we play for the team and do what it takes to win together. We care personally for our teammates while pushing each other with honest, actionable feedback. Above all, we approach everything with humility and a drive to keep learning and getting better.
Equal Opportunities
We are an equal opportunity employer. We ensure that none of our employees or prospective employees receives less favourable treatment as a result of age, sex, disability, marital status, colour, race, religion or ethnic origin. Equally we aim to ensure that no such employee is disadvantaged by terms and conditions of employment which cannot be justified.
Ready to apply?
Apply to Enterpret