Tesla

Makes electric cars, robotaxis and grid batteries

Senior Manager - Service Engineering Intelligence & Analytics

Engineering LeadershipUpdated on Nov 9

Full-timePalo Alto, CA, USANo salary listedIn-person

About the Job

Requirements

A degree in Computer Science, Engineering, Data Science, or equivalent is required.
At least 10 years of experience in data, machine learning, or software, including at least 5 years leading engineering teams; experience managing both managers and senior individual contributors is preferred.
A proven record of building and scaling production data and machine learning platforms that drive decisions, including pipelines, detection and anomaly models, and supporting infrastructure.
Fluency across modern data and machine learning technologies, including streaming and batch pipelines, data warehousing, machine learning systems with feedback loops, and generative AI or large language model applications; sufficient depth to guide architecture and interrogate data directly.
Experience connecting data and analytics to operational outcomes in a hardware, Internet of Things, fleet, or reliability context, such as uptime, predictive maintenance, diagnostic coverage, or cost.
Ability to define a team's direction and guide a multidisciplinary, multi-site organization through ambiguity, prioritizing user needs and fleet health over technology for its own sake.
Strong ownership and ability to influence without authority, partner across engineering, field operations, and programs, and uphold data integrity and measurable impact.

Responsibilities

Set the multi-year vision and technical strategy for how Service senses, diagnoses, and acts on fleet health, converting fleet telemetry and service signals into detection models, pipelines, dashboards, and service intelligence tools.
Lead, grow, and coach a 20-plus-person multidisciplinary organization across data engineering, machine learning, generative AI, and analytics at multiple sites, and build growth paths for technical and leadership careers.
Own the fleet-health data and intelligence platform end to end, including real-time and batch detection, the data backbone, hardware performance, and generative AI and Service Intelligence tooling products; develop it as a durable self-service asset rather than a collection of one-off products.
Tie the team's work to operational outcomes by measuring uptime and availability against service-level agreements, time to detect, diagnostic coverage, and cost; set key performance indicators that measure fleet health rather than ticket closure.
Partner with Residential, Industrial, and Charging engineering verticals, field operations, support, programs, and parts organizations to ensure each detection leads to the appropriate action, part, location, capability, and timing.
Set engineering standards for architecture, the software development lifecycle, data quality and governance, reliability, and on-call operations; review modeling approaches while remaining technically engaged.
Represent Service Engineering's intelligence and analytics strategy to leadership and cross-functional partners, including firmware, platform engineering, product management, DevOps, finance, and design engineering.

Desired Qualifications

Experience managing both managers and senior individual contributors is preferred.

Category	Energy Engineering
Location	Palo Alto, California
Requisition ID	287028
Type	Full-time

Job Description

Tesla Energy's deployed fleet — Powerwall, Megapack, solar, and charging — is growing fast. Service Engineering exists for one goal: maximize fleet health with minimal intervention. The Service Engineering Intelligence & Analytics (SEIA) team turns the fleet's telemetry, service actions, customer reports, and escalations into the pipelines, detection models, dashboards, and SI tools Tesla can use to optimize fleet performance. You will lead that team — a 20+ person multidisciplinary organization spanning data engineering, machine learning, GenAI, and analytics across Palo Alto and Amsterdam. You own the data and intelligence backbone behind how Service senses a problem, diagnoses it, and acts on it with the right part, in the right place, with the right capability, at the right time. You lead from the user and the mission, not the tooling: you can see where fleet-health intelligence needs to go next and build the team to get there. You will be measured by outcomes — product uptime, time-to-detect, diagnostic coverage, and cost to serve — not by dashboards shipped. You stay technical enough to set architecture and keep the trust of your most senior engineers while growing both the people and the platform.

Job Responsibilities

Set the multi-year vision and technical strategy for how Service senses, diagnoses, and acts on fleet health — turning fleet telemetry and service signals into the detection models, pipelines, dashboards, and SI tools the organization relies on
Lead, grow, and coach a 20+ person multidisciplinary organization spanning data engineering, machine learning, GenAI, and analytics across multiple sites — building growth paths on both technical and leadership tracks
Own the fleet-health data and intelligence platform end to end — real-time and batch detection, the data backbone, hardware performance, and the GenAI / Service Intelligence tooling products — as a durable, self-service asset rather than a collection of one-off products
Bind the team's work to operational outcomes. You will measure uptime and availability against SLAs, time-to-detect, diagnostic coverage, and cost. You will set the KPIs that prove the fleet actually got healthier, not just that tickets closed
Partner with the engineering verticals (Residential, Industrial, Charging), field operations, support, and the programs and parts organizations so every detection flows into the right action with the right part, place, capability, and time
Set the engineering bar for architecture, software development lifecycle, data quality and governance, reliability, and on-call — staying hands-on enough to review modeling approaches
Represent Service Engineering's intelligence and analytics strategy to leadership and cross-functional partners like firmware, platform engineering, product management, devOps, finance, design engineering, and myriad other software groups

Job Requirements

Degree in Computer Science, Engineering, Data Science, or equivalent
10+ years in data, ML, or software, including 5+ years leading engineering teams, ideally as a manager of both managers and senior individual contributors
Proven record building and scaling data and ML platforms that ship to production and drive real decisions — pipelines, detection and anomaly models, and the infrastructure behind them
Fluency across the modern data and ML stack — streaming and batch pipelines, warehousing, ML systems with feedback loops, and GenAI / LLM applications — with enough depth to guide architecture and interrogate the data yourself
Track record of tying data and analytics directly to operational outcomes in a hardware, IoT, fleet, or reliability context — uptime, predictive maintenance, diagnostic coverage, or cost
Starts from the user and the fleet-health mission, not the tech — can name where the team needs to go and carry a multidisciplinary, multi-site org through ambiguity to get there
Extreme ownership and the ability to influence without authority — partners across engineering, field, and programs, and holds the line on data integrity and measurable impact

About the Company

Tesla

Makes electric cars, robotaxis and grid batteries

Tesla makes electric cars and batteries that store power for homes and utilities, and it develops self-driving software and humanoid robots. In 2023, its Model Y became the first electric vehicle to rank as the world's best-selling car. Buyers order the Model 3 sedan, Model Y SUV, or Cybertruck pickup directly from Tesla instead of through dealers, and they charge on its Supercharger fast-charging network. It also sells Megapack and Powerwall batteries, runs a robotaxi service, and charges drivers a monthly fee for self-driving software that still needs their supervision. Tesla was founded in 2003, with Elon Musk as its lead early investor and a recognized co-founder, and it is based in Austin, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Austin, TX
Founded: 2003

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