Platform Engineer Jobs in Palo Alto, CA
Platform Engineer jobs in Palo Alto are concentrated along University Avenue, the Stanford Research Park corridor, and the downtown core, with strong demand from enterprise software, semiconductor, and AI infrastructure companies. Employers hiring right now include Tesla, GEICO, and Rivian. Scan the live roles below and apply to whichever ones fit.
Find Platform Engineer JobsOverview
Open roles47
Top employerTesla
Work type77% Hybrid
Top credentialMaster's degree
Companies hiring23
Showing 5 of 47+ Platform Engineer jobs
Senior Software Engineer, Autonomy Evaluation & Metrics Platform
We won't show you this job again
New 23h ago
Rivian
Senior Software Engineer, Autonomy Evaluation & Metrics Platform
Palo Alto, California
New 23h ago
Software Engineer - Virtual Platform
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Added 1d ago
Rubrik
Software Engineer - Virtual Platform
Palo Alto, California
Added 1d ago
Software Engineer II, Autonomy Evaluation & Metrics Platform
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Added 1d ago
Rivian
Software Engineer II, Autonomy Evaluation & Metrics Platform
Palo Alto, California
Added 1d ago
Sr. Software Engineer, ML Platform
We won't show you this job again
Added 3d ago
CURSOR
Sr. Software Engineer, ML Platform
Palo Alto, California
Added 3d ago
Internship, Software Engineer, SI ML Platform (Winter/Spring 2027)
We won't show you this job again
Added 3d ago
Tesla
Internship, Software Engineer, SI ML Platform (Winter/Spring 2027)
Palo Alto, California
Added 3d ago
New 23h ago
Senior Software Engineer, Autonomy Evaluation & Metrics Platform
Rivian·Palo Alto, California
Job functions:
10,000+ employees
Job Description
Rivian’s Autonomy Eval Infrastructure team builds the software that helps engineers understand
and improve the ML models behind our driving systems and gives release teams evidence they
can trust. Within Autonomy’s AI Platform organization, we connect model development with
evaluation workflows, metrics, triage and visualization across simulation and real-world vehicle
data.
and improve the ML models behind our driving systems and gives release teams evidence they
can trust. Within Autonomy’s AI Platform organization, we connect model development with
evaluation workflows, metrics, triage and visualization across simulation and real-world vehicle
data.
As a senior metrics-platform engineer, you will design, build and operate a substantial part of
the platform that makes autonomy evaluation consistent and reproducible. Working directly with
Perception, Planning and Systems engineers, you will connect model experiments to reliable
metrics and help turn insights about model behavior, failure modes and generalization into
improvements in models and data.
This is a hands-on software engineering role. You will own projects through design,
implementation, rollout and operation, make practical system-design trade-offs, and mentor
teammates. As an early member of a newly formed team, you will help establish the engineering
practices and interfaces that make the platform useful across Autonomy.
-
Own the design and delivery of a metrics-platform area, such as the metric registry,
computation services or comparison and reporting APIs, in partnership with the team’s
technical lead. -
Design versioned metric definitions and data models for inputs, aggregation, slicing and
results. Preserve provenance linking official evaluation runs to code, model, dataset and
scenario versions. -
Build distributed computation and storage paths with idempotent execution, incremental
processing and controlled backfills. Make informed trade-offs across throughput, latency,
cost and reproducibility. -
Build comparison tools that expose coverage, sample sizes, uncertainty and changes in
evaluation inputs. Help users distinguish model regressions from data, simulator or
infrastructure failures. -
Partner with model developers to design evaluation experiments, inspect predictions and
analyze failure patterns. Use the findings to guide model and data improvements, and
build reusable evaluation harnesses that shorten the iteration cycle. -
Integrate metrics and regression checks into replay and closed-loop evaluation
workflows and CI. Partner with domain owners on metric semantics and thresholds, and
with Systems on evidence for release decisions. -
Deliver self-service APIs and scorecards backed by consistent definitions and results.
Migrate existing consumers incrementally while preserving reporting continuity and
traceability. -
Own reliability and performance for your services through testing, observability, incident
investigation and operational documentation; measure improvements in turnaround time,
correctness and cost. -
Write and review production code, lead design reviews for your projects, mentor
engineers, and collaborate across Palo Alto, London and Belgrade to deliver software
that users adopt.
-
5+ years of professional software engineering experience, or equivalent demonstrated
experience and impact, including ownership of production backend or data-intensive
systems. -
Hands-on experience building software for autonomy or robotics, with direct exposure to
evaluation, simulation, perception, planning or validation workflows. -
Strong Python programming skills, with the ability to write, test, profile and debug
maintainable production software. -
Practical SQL skills, with AI coding tools expected to support writing and refining
queries; ability to validate query logic, results and performance. -
Demonstrated system-design ability across service APIs, data modeling, distributed
processing and storage, including failure recovery, consistency and performance
trade-offs. -
Experience taking a project from requirements and design through implementation,
rollout and operation, coordinating dependencies and communicating trade-offs clearly. -
Practical understanding of model inference and evaluation, including held-out datasets,
generalization, reproducibility and model versioning, and the differences between
open-loop replay, closed-loop simulation and real-world measurements. -
Ability to reason about sampling, confidence intervals, noisy metrics and valid
comparisons, and to work with domain experts when stronger statistical analysis is
needed. -
Experience with cloud infrastructure, containers, CI/CD and production observability,
plus a track record of constructive code review and mentoring.
Bonus Points -
Experience building metric registries, evaluation services, experiment-tracking systems
or automated regression gates used by multiple engineering teams. -
Experience with Spark, Databricks, Delta Lake or comparable distributed data systems,
and with AWS, Docker or GitLab CI. -
Experience migrating analytical or reporting systems without breaking downstream
consumers, or linking metrics to scenario-level debugging and triage. -
Experience with PyTorch or similar ML frameworks, evaluation of end-to-end driving
models, or connecting offline model metrics to closed-loop and real-world driving
behavior.
Rivian
Senior Software Engineer, Autonomy Evaluation & Metrics Platform
Palo Alto, California
New 23h ago10,000+ employees
Job functions:
Job Description
Rivian’s Autonomy Eval Infrastructure team builds the software that helps engineers understand
and improve the ML models behind our driving systems and gives release teams evidence they
can trust. Within Autonomy’s AI Platform organization, we connect model development with
evaluation workflows, metrics, triage and visualization across simulation and real-world vehicle
data.
and improve the ML models behind our driving systems and gives release teams evidence they
can trust. Within Autonomy’s AI Platform organization, we connect model development with
evaluation workflows, metrics, triage and visualization across simulation and real-world vehicle
data.
As a senior metrics-platform engineer, you will design, build and operate a substantial part of
the platform that makes autonomy evaluation consistent and reproducible. Working directly with
Perception, Planning and Systems engineers, you will connect model experiments to reliable
metrics and help turn insights about model behavior, failure modes and generalization into
improvements in models and data.
This is a hands-on software engineering role. You will own projects through design,
implementation, rollout and operation, make practical system-design trade-offs, and mentor
teammates. As an early member of a newly formed team, you will help establish the engineering
practices and interfaces that make the platform useful across Autonomy.
-
Own the design and delivery of a metrics-platform area, such as the metric registry,
computation services or comparison and reporting APIs, in partnership with the team’s
technical lead. -
Design versioned metric definitions and data models for inputs, aggregation, slicing and
results. Preserve provenance linking official evaluation runs to code, model, dataset and
scenario versions. -
Build distributed computation and storage paths with idempotent execution, incremental
processing and controlled backfills. Make informed trade-offs across throughput, latency,
cost and reproducibility. -
Build comparison tools that expose coverage, sample sizes, uncertainty and changes in
evaluation inputs. Help users distinguish model regressions from data, simulator or
infrastructure failures. -
Partner with model developers to design evaluation experiments, inspect predictions and
analyze failure patterns. Use the findings to guide model and data improvements, and
build reusable evaluation harnesses that shorten the iteration cycle. -
Integrate metrics and regression checks into replay and closed-loop evaluation
workflows and CI. Partner with domain owners on metric semantics and thresholds, and
with Systems on evidence for release decisions. -
Deliver self-service APIs and scorecards backed by consistent definitions and results.
Migrate existing consumers incrementally while preserving reporting continuity and
traceability. -
Own reliability and performance for your services through testing, observability, incident
investigation and operational documentation; measure improvements in turnaround time,
correctness and cost. -
Write and review production code, lead design reviews for your projects, mentor
engineers, and collaborate across Palo Alto, London and Belgrade to deliver software
that users adopt.
-
5+ years of professional software engineering experience, or equivalent demonstrated
experience and impact, including ownership of production backend or data-intensive
systems. -
Hands-on experience building software for autonomy or robotics, with direct exposure to
evaluation, simulation, perception, planning or validation workflows. -
Strong Python programming skills, with the ability to write, test, profile and debug
maintainable production software. -
Practical SQL skills, with AI coding tools expected to support writing and refining
queries; ability to validate query logic, results and performance. -
Demonstrated system-design ability across service APIs, data modeling, distributed
processing and storage, including failure recovery, consistency and performance
trade-offs. -
Experience taking a project from requirements and design through implementation,
rollout and operation, coordinating dependencies and communicating trade-offs clearly. -
Practical understanding of model inference and evaluation, including held-out datasets,
generalization, reproducibility and model versioning, and the differences between
open-loop replay, closed-loop simulation and real-world measurements. -
Ability to reason about sampling, confidence intervals, noisy metrics and valid
comparisons, and to work with domain experts when stronger statistical analysis is
needed. -
Experience with cloud infrastructure, containers, CI/CD and production observability,
plus a track record of constructive code review and mentoring.
Bonus Points -
Experience building metric registries, evaluation services, experiment-tracking systems
or automated regression gates used by multiple engineering teams. -
Experience with Spark, Databricks, Delta Lake or comparable distributed data systems,
and with AWS, Docker or GitLab CI. -
Experience migrating analytical or reporting systems without breaking downstream
consumers, or linking metrics to scenario-level debugging and triage. -
Experience with PyTorch or similar ML frameworks, evaluation of end-to-end driving
models, or connecting offline model metrics to closed-loop and real-world driving
behavior.
See All 47 Platform Engineer Jobs in Palo Alto
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Find Platform Engineer JobsPlatform Engineer Job Market in Palo Alto
Who's Hiring
Top Industries Hiring
- Technology & Software
- Insurance
- Science & Research
Platform Engineer Jobs in Palo Alto: Frequently Asked Questions
How do I get a platform engineer job in Palo Alto?
Focus your search on the Stanford Research Park corridor and downtown Palo Alto, where enterprise software, AI infrastructure, and semiconductor firms concentrate their engineering teams. Hands-on experience with Kubernetes, Terraform, or cloud-native tooling gives you a clear edge here. Employers in this market move quickly, so having a portfolio of deployed infrastructure projects and strong system design fundamentals puts you ahead of most applicants.
Which companies hire platform engineers in Palo Alto?
Palo Alto platform engineer roles are posted by Tesla, GEICO, and Rivian and others right now, based on current listings on Migrate Mate as of November 2026. The local market skews toward mature tech companies and well-funded growth-stage startups, many tied to Stanford's research ecosystem or backed by Sand Hill Road venture capital.
Are there remote platform engineer jobs in Palo Alto?
Yes, though availability depends on the role, since positions requiring hands-on data center or on-site infrastructure work tend to be in-person while cloud and automation-focused roles are often remote-eligible. About 77% of platform engineer openings tied to Palo Alto are remote or hybrid as of November 2026, reflecting how many local employers have adopted flexible arrangements. Cloud platform and SRE-adjacent roles are the most consistently remote-eligible in this market.
How can I get a platform engineer job in Palo Alto with little or no experience?
The most realistic entry path in Palo Alto is through a junior site reliability engineer or infrastructure associate role at one of the many growth-stage startups in the area, where smaller teams give early-career engineers broader exposure faster. Building public projects on GitHub using tools common in the local stack, such as Kubernetes or Terraform, and pursuing cloud certifications helps compensate for limited professional history. Some Palo Alto firms also run structured rotational programs that move engineers from support or DevOps roles into platform teams.
Which industries hire the most platform engineers in Palo Alto?
Palo Alto platform engineer roles concentrate in Technology & Software, Insurance, and Science & Research, based on current listings on Migrate Mate as of November 2026. The city's proximity to Stanford and its deep roots in enterprise software and semiconductor development have made those sectors the primary drivers of platform engineering demand locally.
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