Staff Machine Learning Engineer - Data Intelligence Job at Culture Amp (Sydney)

Staff Software Engineer - Backend

As the Staff Software Engineer for our SaaS platform team, you will be crucial i...

Location

United States , Mountain View

Salary:

198000.00 - 225000.00 USD / Year

Cyngn

Expiration Date

Until further notice

Requirements

10+ years of software development experience, with a strong focus on backend systems and distributed architectures
Extensive experience in building and scaling cloud-native SaaS platforms, preferably in the IoT or robotics domains
Expert-level proficiency in at least one of Python, Go, Java, or C++, with working knowledge of others
Deep understanding of cloud technologies and services (AWS, Azure, or GCP)
Proven experience with event-driven architectures and message queuing systems (e.g., Kafka, RabbitMQ, Apache Pulsar)
Strong background in database design and optimization, including both SQL and NoSQL solutions
Proficiency in developing scalable WebSocket-based real-time communication systems
Expertise in developing real-time data processing pipelines and analytics systems
Proficiency with containerization and orchestration technologies (Docker, Kubernetes)
Experience with infrastructure-as-code and CI/CD practices (e.g., Terraform, GitOps)

Job Responsibility

Architect and lead the development of a sophisticated, cloud-native fleet management system capable of real-time control and monitoring of numerous autonomous vehicles
Design and implement scalable, distributed systems that can handle high-volume, real-time data processing and decision-making
Develop robust APIs and microservices to support integration with various autonomous vehicle platforms and customer systems
Create efficient algorithms for route optimization, task scheduling, and resource allocation across vehicle fleets
Implement advanced data analytics and machine learning capabilities to provide predictive maintenance, performance optimization, and business intelligence features
Ensure system reliability, security, and compliance with industry standards and regulations
Lead a team of skilled engineers, fostering a culture of innovation, code quality, and continuous improvement
Collaborate with product managers, UX designers, and customers to translate business requirements into technical solutions
Mentor junior developers and contribute to the technical growth of the engineering team
Participate in the entire software development lifecycle, from concept and design to testing, deployment, and maintenance

What we offer

Health benefits (Medical, Dental, Vision, HSA and FSA (Health & Dependent Daycare), Employee Assistance Program, 1:1 Health Concierge)
Life, Short-term, and long-term disability insurance (Cyngn funds 100% of premiums)
Company 401(k)
Commuter Benefits
Flexible vacation policy
Remote or hybrid work opportunities
Sabbatical leave opportunity after five years with the company
Paid Parental Leave
Daily lunches for in-office employees
Monthly meal and tech allowances for remote employees

Fulltime

Distinguished Engineer

As a Distinguished Engineer at Capital One, you will be a part of a community of...

Location

United States , McLean; New York

Salary:

269100.00 - 335100.00 USD / Year

Capital One

Expiration Date

Until further notice

Requirements

Bachelor’s Degree
At least 9 years of experience in software engineering (including solution architecture and enterprise design patterns)
At least 7 years of experience in Data Engineering or Machine Learning Engineering, specifically building and maintaining large-scale production data pipelines (PySpark, SQL, Airflow)
At least 5 years of experience in Cloud Native Architecture, including container orchestration (Kubernetes or KubeFlow) and serverless design
At least 3 years of experience in a Technical Leadership role (Principal, Staff, or Distinguished Engineer), leading cross-functional initiatives without direct reporting lines

Job Responsibility

Articulate and evangelize a bold technical vision for your domain
Decompose complex problems into practical and operational solutions
Ensure the quality of technical design and implementation
Serve as an authoritative expert on non-functional system characteristics, such as performance, scalability and operability
Continue learning and injecting advanced technical knowledge into our community
Handle several projects simultaneously, balancing your time to maximize impact
Act as a role model and mentor within the tech community, helping to coach and strengthen the technical expertise and know-how of our engineering and product community
Partner with the Capital One Travel Data & Personalization team to architect the next generation of our Travel Intelligence Platform
Serve as the primary architect for the foundational data schemas and orchestration layers
Establish a holistic Agentic AI framework

What we offer

Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

Fulltime

Staff Software Engineer, Data Infrastructure

At Docker, we make app development easier so developers can focus on what matter...

Location

United States , Seattle

Salary:

195400.00 - 275550.00 USD / Year

Docker

Expiration Date

Until further notice

Requirements

8+ years of software engineering experience with 3+ years focused on data engineering and analytics systems
Expert-level experience with Snowflake including advanced SQL, performance optimization, and cost management
Deep proficiency in DBT for data modeling, transformation, and testing with experience in large-scale implementations
Strong expertise with Apache Airflow for complex workflow orchestration and pipeline management
Hands-on experience with Sigma or similar modern BI platforms for self-service analytics
Extensive AWS experience including data services (S3, Redshift, EMR, Glue, Lambda, Kinesis) and infrastructure management
Proficiency in Python, SQL, and other programming languages commonly used in data engineering
Experience with infrastructure-as-code, CI/CD practices, and modern DevOps tools
Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Proven track record designing and implementing large-scale distributed data systems

Job Responsibility

Define and drive the technical strategy for Docker's data platform architecture, establishing long-term vision for scalable data systems
Lead design and implementation of highly scalable data infrastructure leveraging Snowflake, AWS, Airflow, DBT, and Sigma
Architect end-to-end data pipelines supporting real-time and batch analytics across Docker's product ecosystem
Drive technical decision-making around data platform technologies, architectural patterns, and engineering best practices
Establish technical standards for data quality, testing, monitoring, and operational excellence
Design and build robust, scalable data systems that process petabytes of data and support millions of user interactions
Implement complex data transformations and modeling using DBT for analytics and business intelligence use cases
Develop and maintain sophisticated data orchestration workflows using Apache Airflow
Optimize Snowflake performance and cost efficiency while ensuring reliability and scalability
Build data APIs and services that enable self-service analytics and integration with downstream systems

What we offer

Freedom & flexibility
fit your work around your life
Designated quarterly Whaleness Days plus end of year Whaleness break
Home office setup
we want you comfortable while you work
16 weeks of paid Parental leave
Technology stipend equivalent to $100 net/month
PTO plan that encourages you to take time to do the things you enjoy
Training stipend for conferences, courses and classes
Equity

Fulltime

Head of Technology, Intelligence Ventures

The Head of Technology serves as the chief technology officer of a new behaviora...

Location

United States , New York

Salary:

263200.00 - 393800.00 USD / Year

Spectrum

Expiration Date

Until further notice

Requirements

Deep expertise across the full intelligence platform stack — including distributed data pipelines, ML platform architecture, embedding systems, feature stores, agent-to-agent API design, and LLM-powered application layers — with demonstrated ability to architect and ship all layers as a coherent, production-grade product
Demonstrated experience architecting and operating consumer data intelligence or data product platforms underpinned by complex machine learning systems and built on modern, cloud-native data infrastructure
Hands-on proficiency with Snowflake (including Cortex, Native Apps, and data sharing frameworks), cloud data platforms (AWS, Azure, or GCP), and production ML/AI systems at scale
Experience building agentic AI systems and LLM-powered product interfaces — including agent-to-agent APIs, retrieval-augmented generation architectures, and natural language UIs grounded in proprietary data — with strong product instincts around accuracy, trust, and user experience for non-technical enterprise audiences
Proven ability to translate complex technical architecture into clear executive and partner-facing communications
comfortable engaging at the C-suite level and in strategic partner negotiations with hyperscalers and technology platforms
Strong understanding of privacy-preserving data architecture, including differential privacy, de-identification techniques, zero-copy and clean room frameworks, and the regulatory landscape governing consumer behavioral data
Track record of recruiting and developing exceptional engineering talent in competitive markets
experience building high-performance teams from early-stage through scaled operations
Experience managing external development partners and outsourced engineering resources alongside an internal team in a fast-moving, build-from-scratch environment

Job Responsibility

Own the end-to-end technical architecture of the platform — from large-scale network signal ingestion and processing through behavioral embedding generation, feature store construction, and zero-copy intelligence delivery to enterprise partners — ensuring the platform is production-grade, built for household-scale throughput, and designed for long-term extensibility across new signal sources and use cases
Lead the build of the platform’s cloud-native data and application infrastructure — including ingestion and transformation pipelines, ML/AI compute environments, zero-copy partner access frameworks, and a real-time agent-to-agent API layer that enables external AI systems (marketing agents, commerce agents, customer service agents) to query household-level intelligence and receive contextually grounded responses
Serve as the technical lead in strategic partner integrations with cloud, data, and AI platform providers, ensuring each integration is architecturally differentiated and aligned with the platform’s cloud-agnostic, API-first design principles
Architect and deliver a business-facing agentic intelligence interface — a natural language UI that allows non-technical marketers, planners, and business users to query household behavioral intelligence, surface demand signals, and take action without requiring data or engineering support
Build and manage a world-class engineering organization capable of competing for talent with the leading technology and data infrastructure companies

Fulltime