Machine Learning Engineer

HappyRobot; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Healthcare

Equity

Autonomy & ownership

High-growth startup environment

Job summary

HappyRobot in San Francisco is building the next generation of AI workers that think, decide, and act in real time. As an early ML hire, you will own the full lifecycle of ML systems from data ingestion to production deployment, shaping product capabilities and the company’s technical direction.

You’ll design scalable ML pipelines, implement MLOps practices, and collaborate with product and engineering teams to deliver robust, observable AI solutions that scale with customer demands.

Qualifications

• Strong experience in machine learning, deep learning, and NLP.
• Solid background in MLOps and data pipelines — e.g., model deployment, monitoring, and scaling in production environments.
• Proficiency in Python and familiarity with Go.
• Experience with ML lifecycle management tools (e.g., MLflow, Kubeflow, Weights & Biases).
• Ability to design ML systems for robustness, scalability, and automation.
• Strong coding, debugging, and data engineering skills.
• Passion for AI infrastructure and its real‑world impact.
• Founder mindset: ownership, independence, and willingness to go deep.

Responsibilities

• Design, build, and maintain scalable ML systems — from data ingestion and preprocessing to training, testing, and deployment.
• Develop and optimize end-to-end ML pipelines (data collection, labeling, training, validation, monitoring) to ensure reliability and reproducibility.
• Implement robust MLOps practices, including model versioning, experiment tracking, CI/CD for ML, and continuous monitoring in production.
• Collaborate with product and engineering teams to integrate and deploy models into real‑time products with a focus on efficiency and scalability.
• Ensure data quality, observability, and performance across all AI systems.
• Stay current with the latest in AI infrastructure, tooling, and research — helping us stay ahead of the curve.

Skills

Machine learning

Deep learning

NLP

MLOps

Python

Go

Data pipelines

Experiment tracking

Tools

MLflow

Kubeflow

Weights & Biases

Airflow

Dagster

Docker

Kubernetes

AWS

GCP

Azure

Job description

About HappyRobot

HappyRobot is the infrastructure for enterprises to build and orchestrate AI workforces. Our AI workers don't just communicate - they make decisions, take action, and run operations autonomously across voice, email, and enterprise systems. Born in Y Combinator (S23) and backed by a16z and Base10 with over $60M raised, we power critical operations for global enterprises worldwide.

Our platform is battle-tested in the most demanding environments - where AI has real consequences. We started in logistics, built our own voice stack, models, and orchestration layer from the ground up, and are now bringing that infrastructure to every enterprise that runs the real economy. Learn more about our vision in our manifesto.

About the Role

You’ll be building AI models that make human-like conversations possible. You’ll work at the intersection of speech, language, and intelligence, taking cutting‑edge research and transforming it into real‑time, scalable systems that power our core products. You’ll have the unique opportunity to make a huge impact as one of our first ML hires, shaping not only the technology but also the direction of our company. From designing robust models to deploying them in production, you’ll own the entire lifecycle of ML systems and help us stay ahead of the curve in AI innovation.

About the Role

• Design, build, and maintain scalable ML systems — from data ingestion and preprocessing to training, testing, and deployment.

• Develop and optimize end-to-end ML pipelines (data collection, labeling, training, validation, monitoring) to ensure reliability and reproducibility.

• Implement robust MLOps practices, including model versioning, experiment tracking, CI/CD for ML, and continuous monitoring in production.

• Collaborate with product and engineering teams to integrate and deploy models into real‑time products with a focus on efficiency and scalability.

• Ensure data quality, observability, and performance across all AI systems.

• Stay current with the latest in AI infrastructure, tooling, and research — helping us stay ahead of the curve.

Must Have

• Strong experience in machine learning, deep learning, and NLP.

• Solid background in MLOps and data pipelines — e.g., model deployment, monitoring, and scaling in production environments.

• Proficiency in Python and familiarity with Go.

• Experience with ML lifecycle management tools (e.g., MLflow, Kubeflow, Weights & Biases).

• Ability to design ML systems for robustness, scalability, and automation.

• Strong coding, debugging, and data engineering skills.

• Passion for AI infrastructure and its real‑world impact.

• Founder mindset: ownership, independence, and willingness to go deep.

Nice to Have

• Experience in speech recognition, TTS, or audio processing.

• Familiarity with LLMs, generative AI, or real‑time inference systems.

• Hands‑on experience with data orchestration frameworks (e.g., Airflow, Prefect, Dagster).

• Prior experience in startup environments with fast iteration cycles.

• Knowledge of cloud infrastructure (AWS/GCP/Azure) and containerization tools (Docker, Kubernetes).

Why join us?

• Opportunity to work at a high‑growth AI startup, backed by top investors.

• Rapidly growing and backed by top investors including a16z, Y Combinator, and Base10.

• Ownership & Autonomy - Take full ownership of projects and ship fast.

• Top‑Tier Compensation - Competitive salary + equity in a high‑growth startup.

• Comprehensive Benefits - Healthcare, dental, vision coverage.

• Work With the Best - Join a world‑class team of engineers and builders

Operating Principles

Extreme Ownership

We take full responsibility for our work, outcomes, and team success. No excuses, no blame‑shifting — if something needs fixing, we own it and make it better. This means stepping up, even when it’s not “your job.” If a ball is dropped, we pick it up. If a customer is unhappy, we fix it. If a process is broken, we redesign it. We don’t wait for someone else to solve it — we lead with accountability and expect the same from those around us.

Craftsmanship

Putting care and intention into every task, striving for excellence, and taking deep ownership of the quality and outcome of your work. Craftsmanship means never settling for “just fine.” We sweat the details because details compound. Whether it’s a product feature, an internal doc, or a sales call — we treat it as a reflection of our standards. We aim to deliver jaw‑dropping customer experiences by being curious, meticulous, and proud of what we build — even when nobody’s watching.

We are “majos”

Be friendly & have fun with your coworkers. Always be genuine & honest, but kind. “Majo” is our way of saying: be a good human. Be approachable, helpful, and warm. We’re building something ambitious, and it’s easier (and more fun) when we enjoy the ride together. We give feedback with kindness, challenge each other with respect, and celebrate wins together without ego.

Urgency with Focus

Create the highest impact in the shortest amount of time. Move fast, but in the right direction. We operate with speed because time is our most limited resource. But speed without focus is chaos. We prioritize ruthlessly, act decisively, and stay aligned. We aim for high leverage: the biggest results from the simplest, smartest actions. We’re running a high‑speed marathon — not a sprint with no strategy.

Talent Density and Meritocracy

Hire only people who can raise the average; ‘exceptional performance is the passing grade.’ Ability trumps seniority. We believe the best teams are built on talent density — every hire should raise the bar. We reward contribution, not titles or tenure. We give ownership to those who earn it, and we all hold each other to a high standard. A‑players want to work with other A‑players — that’s how we win.

First‑Principles Thinking

Strip a problem to physics‑level facts, ignore industry dogma, rebuild the solution from scratch. We don’t copy‑paste solutions. We go back to basics, ask why things are the way they are, and rebuild from the ground up if needed. This mindset pushes us to innovate, challenge stale assumptions, and move faster than incumbents. It’s how we build what others think is impossible.

Data Privacy

The personal data provided in your application and during the selection process will be processed by HappyRobot, Inc., acting as Data Controller.

By sending us your CV, you consent to the processing of your personal data for the purpose of evaluating and selecting you as a candidate for the position. Your personal data will be treated confidentially and will only be used for the recruitment process of the selected job offer.

In relation to the period of conservation of your personal data, these will be eliminated after three months of inactivity in compliance with the GDPR and legislation on the protection of personal data.

If you wish to exercise your rights of access, rectification, deletion, portability or opposition in relation to your personal data, you can do so through [e-mail] subject to the GDPR.

For more information, visit https://www.happyrobot.ai/privacy-policy

By submitting your request, you confirm that you have read and understood this clause and that you agree to the processing of your personal data as described.