Eval Ops Program Manager

About the role

The role organizes analytics and execution for robotics evaluations within the Data team. It connects machine learning, analytics, and operations to support evaluation integrity. The position focuses on building operational systems that enable scalable, trustworthy evaluation processes.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Evaluation goals are defined and data quality is safeguarded through collaboration with ML, Software, Hardware, Product, and Operations stakeholders, with findings translated into actionable insights for decision makers.

Processes for robotics assessments are designed and refined to improve quality, repeatability, and scale, addressing current limits and future evaluation needs.

End-to-end evaluation logistics are managed, covering sourcing and standardization of props, garments, equipment, and environments to keep tests consistent and reliable.

Solutions, automations, or new processes are built to close operational gaps, ensuring evaluations run smoothly and can scale as the platform grows.

Requirements

The posting states a bachelor's degree requirement. A degree is required as stated in the listing.

Analytical skills turn evaluation and operational data into clear, actionable insights for teams and stakeholders.

Ownership is shown through a bias for execution and reliable delivery on commitments to timelines and quality.

Communication is clear in both written and verbal forms across diverse teams and stakeholders.

Comfort working across analytics and hands-on operations supports day-to-day evaluation work in a hybrid setting.

A resilient, thick-skinned mindset accepts direct feedback and maintains steady performance under pressure.

An intermediate-level understanding of ML concepts guides collaboration with technical eval partners and data scientists.

Practical notes

The role works across analytics and operational tasks within the Data team at Sunday Robotics in Redwood City.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Roles in robotics evaluation often combine analytical review with hands-on testing logistics.

ML evaluation workflows rely on metrics, dashboards, and experimentation methods to measure system behavior.

Technical program management and light tooling are common for coordinating cross-functional eval efforts.

Human centered design and diverse perspectives shape better evaluation practices for real world use.

Automation and AI assistance can streamline repetitive steps in evaluation setup and analysis.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.