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Who we are Lab37 Robotics , is a technology company focused on the development and deployment of robots designed specifically for direct-to-customer food production. Our mission is to revolutionize the food industry by creating innovative robotic solutions that enhance efficiency, quality, and customer satisfaction. We are passionate about pushing the boundaries of technology to deliver cutting-edge products that meet the evolving needs of our clients. What you'll do Take on ownership of the data and ML platform layer that powers Lab37's robotics fleet. This spans building ETL pipelines that turn raw robot telemetry into actionable analytics, managing the ML training infrastructure that produces the computer vision models running on our robots, and ensuring the observability and reliability of the entire data stack. Work with a small, high-impact platform team to build the systems that every product team at Lab37 consumes — from data scientists training models, to kitchen operations teams viewing dashboards, to engineers deploying new ML models to robots in the field. Responsibilities Build and maintain ETL pipelines that ingest, validate, transform, and load robot telemetry data into BigQuery for analytics and ML training Manage ML training infrastructure using Argo Workflows on Kubernetes — from data extraction through model training, evaluation, and registration Design and maintain data quality checks and observability dashboards (the "glass panel" for data pipeline health) Own the data warehouse layer — schema design, incremental loading, dbt transforms, and BigQuery/Athena query optimization Build dashboard infrastructure (Superset, Grafana) that kitchen operations and leadership teams rely on for real-time insights Collaborate with data scientists to productionize model training pipelines — from notebook experiments to reproducible, automated workflows Contribute to infrastructure-as-code (Terraform) and CI/CD pipelines for data and ML workloads Participate in on-call rotations for data pipeline reliability What we're looking for 2+ years in data engineering, ML infrastructure, or analytics engineering Strong experience with workflow orchestration tools (Argo Workflows, Airflow, Prefect, or similar) Python data stack proficiency — pandas, SQL, dbt, and comfort writing production-quality data pipelines Experience with cloud data services (BigQuery, Athena, S3, or GCP equivalents) Experience building or maintaining ML training pipelines (not just using them as a consumer) Docker containerization and Kubernetes basics SQL fluency — you'll write complex queries daily and care about query performance Familiarity with documentation tools and writing design documents Desirable Skills ETL debugging, data quality frameworks, and anomaly detection Experience with hybrid cloud environments (AWS + GCP) Robotics or IoT data experience — understanding the challenges of real-world sensor data Experience with dbt for data transformations and warehouse modeling Why join us Demand for online food delivery is growing really fast! In the last 5 years, just in the US, the overall market has expanded 10X from $10B to $100B, and could expand to $500bn- $1T by 2030. Changing the restaurant industry: You’ll be part of a team that helps restaurants succeed in online food delivery. Collaborative environment: You will receive support and guidance from experienced colleagues and managers, helping you to learn, grow and achieve your goals, and you’ll work closely with other teams to ensure our customer’s success. What else you need to know This role is based in our Pittsburgh office. As a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That’s why all of our office-based teams work onsite, five days a week. The base salary range for this role is $130,000 - $164,500 per year. Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications. Base salary is just one part of your total rewards package. You may also be eligible for equity awards and an annual performance-based bonus. Benefits Summary (USA Full-Time Exempt Employees): Medical, dental, and vision insurance (multiple plans, incl. HSA options). Company-paid life and disability insurance (short- and long-term). Voluntary insurance: accident, critical illness, hospital indemnity. Optional supplemental life insurance for self, spouse, and children. Pet insurance discount. 401(k). Health Savings Account (HSA) Flexible Spending Accounts (Healthcare, Dependent Care, Commuter) Time Off policies: Discretionary vacation days 8 paid holidays per year Paid sick time Paid Bereavement leave Paid Parental Leave Benefits are subject to change at the company's discretion. Atoms accepts applications on an ongoing basis. Ready to join us as we serve those who serve others? #LI-Onsite
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.
