Senior Machine Learning Engineer

Unity

. Build and maintain data and feature pipelines that generate training datasets for machine learning models and experimentation .

Posted 10/9/2026full-timeUnited StatesSenior💰 $140,700 - $232,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining machine learning infrastructure and data pipelines, with strong proficiency in Python and experience with ML frameworks. Capable of managing end-to-end projects while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Machine Learning Infrastructure DevelopmentPython ProgrammingDistributed Systems ExperienceData Pipeline ManagementModel Serving Frameworks

ATS Keywords

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Hard Skills

PythonMachine Learning FrameworksData PipelinesDistributed ComputeModel Training WorkflowsDataset ValidationMonitoring and AlertingTestingPattern RecognitionCloud Infrastructure

Soft Skills

Clear CommunicationCollaborative ApproachProblem-Solving

Tools & Technologies

PyTorchRaySparkKubernetesGCPAWSPrometheusGrafanaAirflowKafka

Certifications & Qualifications

Bachelor's Degree in Computer ScienceBachelor's Degree in Machine LearningBachelor's Degree in Systems

Industry Keywords

Data PlatformsFeature StoresExperimentation PlatformsLarge-Scale Data PlatformsData LakesData WarehousesStreaming Systems

Tech Stack

Tools & technologies

AirflowAWSCloudDistributed SystemsGoogle Cloud PlatformGrafanaKafkaKubernetesPrometheusPythonPyTorchRaySpark

About the role

Key responsibilities & impact

Build and maintain data and feature pipelines that generate training datasets for machine learning models and experimentation
Develop and improve infrastructure for distributed training workflows
Build and operate multi-stage ML pipelines using workflow orchestration tools
Contribute to model serving and deployment infrastructure, helping models move reliably from training into production
Improve reproducibility and reliability across the stack through monitoring, alerting, dataset validation, and testing
Take on manager-scoped projects and carry them through end-to-end, from design to rollout
Investigate uncommon or inconsistent issues across pipelines and training systems and turn patterns into concrete improvements
Balance immediate delivery needs with the broader direction of the platform
Partner with ML engineers, researchers, and peers across teams
Share progress and tradeoffs with managers and directors
Learn new tools and parts of the stack as team priorities shift

Requirements

What you’ll need

Experience building ML infrastructure, data platforms, or distributed systems in production
Strong Python skills and experience working with data-intensive workloads
Hands-on experience with ML frameworks (e.g., PyTorch) and distributed compute (e.g., Ray, Spark)
Experience with data pipelines, model training workflows, or large datasets beyond academic settings
Comfort working across several layers of the ML stack, with a track record of learning new tools and systems quickly
Ability to spot patterns in ambiguous or inconsistent problems and translate them into practical technical needs
Clear communication and a collaborative approach with peers, managers, and partner teams
Bachelor's degree in Computer Science, Machine Learning, Systems, or a related field, or equivalent practical experience
Professional verbal and written English proficiency
Nice to have: Experience with Kubernetes and cloud infrastructure (GCP or AWS)
Nice to have: Experience with observability tooling (e.g., Prometheus, Grafana)
Nice to have: Experience with workflow orchestration systems (Airflow, Prefect, etc.)
Nice to have: Experience with model serving frameworks (e.g., Triton, KServe, Ray Serve)
Nice to have: Exposure to large-scale data platforms, such as data lakes, warehouses, and streaming systems such as Kafka
Nice to have: Familiarity with feature stores or experimentation platforms

Benefits

Comp & perks

Comprehensive health, life, and disability insurance
Commute subsidy
Employee stock ownership
Competitive retirement/pension plans
Generous vacation and personal days
Support for new parents through leave and family-care programs
Office food snacks
Mental Health and Wellbeing programs and support
Employee Resource Groups
Global Employee Assistance Program
Training and development programs
Volunteering and donation matching program
Equity awards
Participation in company incentive plans, such as annual discretionary bonuses or sales commissions