Staff Machine Learning Engineer

Staff Machine Learning Engineer - Data Intelligence

We're building the platform that makes AI possible across Culture Amp. This is a...

Location

Australia , Sydney

Salary:

Not provided

Culture Amp

Expiration Date

Until further notice

Requirements

Strong platform engineering fundamentals, with experience building and operating services that other teams depend on
Expertise in Python and experience with ML tooling and infrastructure
Deep experience with large-scale data systems (streaming, batch processing, data lakes)
Proven experience with ML infrastructure: model serving, vector databases, embedding pipelines
Strong understanding of cloud platforms (AWS preferred) and backend architecture
Experience building and optimising RAG and retrieval systems
The communication skills to work across teams and influence technical direction beyond your own team

Job Responsibility

Designing and operating the platform services that power AI features across Culture Amp, including inference pipelines, embedding storage, and retrieval systems
Building a scalable approach to vector search across diverse categories of unstructured data (survey responses, performance feedback, company documents)
Driving MLOps and LLMOps practices across the organisation, including observability, cost management, and reliability
Ensuring AI is used responsibly: implementing guardrails, security controls, and data compliance measures
Partnering with data scientists on the team to productionise models and evaluate new AI capabilities

What we offer

Employee Share Options Program
Programs, coaching, and budgets to help you thrive personally and professionally
Access to external providers for mental wellbeing and coaching support
Monthly Camper Life Allowance
Team budgets dedicated to team building activities and connection
Intentional quarterly wellbeing pauses
Extended year-end breaks
Excellent parental leave and in work support program available from day 1
5 Social Impact Days a year
MacBooks for you to do your best & a work from home office budget

Staff Artificial Intelligence Machine Learning Engineer

The Role: General Motors is seeking a Staff AI/ML Engineer for the Vehicle Mech...

Location

United States , Austin

Salary:

Not provided

General Motors

Expiration Date

Until further notice

Requirements

Graduate degree (Master's or PhD) in Computer Science, Data Science, Machine Learning, Statistics, Engineering, or a closely related quantitative field
7+ years of hands-on experience designing, building, and operating machine learning systems in production environments
Strong proficiency in Python (production-quality code, testing, packaging) and SQL, with experience working in shared, multi-developer codebases
Practical experience with core ML frameworks such as PyTorch, TensorFlow, or scikit-learn, and with MLOps tooling (e.g., MLflow, CI/CD, model registries, experiment tracking)
Experience building data and ML workloads on cloud platforms, preferably Microsoft Azure, and working with Databricks, Spark, or similar distributed processing frameworks
Demonstrated ability to turn ambiguous real-world problems into shippable AI/ML solutions, owning the details from data exploration through deployed service and ongoing operation
Strong understanding of ML system behavior in production (data issues, non-stationarity, latency, throughput, failure modes) and comfort debugging with logs, metrics, and traces
Excellent communication and collaboration skills, with a track record of influencing decisions and mentoring other AI/ML practitioners

Job Responsibility

Design, build, and operate end-to-end AI/ML solutions (data pipelines, models, services, and tools) for diagnostics, prognostics, and test analytics
Implement production-grade ML pipelines on platforms such as Azure and Databricks, covering data ingestion, feature engineering, training, evaluation, and inference for batch and streaming workloads
Develop and maintain robust, observable ML services and internal tools that make complex vehicle and field data easy to use for engineers and technical stakeholders
Apply practical ML and statistical methods (e.g., tree-based models, time-series and anomaly detection, deep learning where appropriate) with a focus on reliability, explainability, and impact
Own model and data observability in production, including metrics, dashboards, alerts, and remediation workflows for drift, data quality, and performance regressions
Partner with data engineering to define and use industrialized and vectorized data products that support search, RAG, and analytics at scale
Review designs and code, mentor AI/ML practitioners, and help set high standards for testing, logging, deployment, and documentation
Collaborate with diagnostics/prognostics SMEs, validation, safety, and program teams to prioritize work, define success metrics, and embed solutions in day-to-day engineering workflows

Fulltime

Staff Applied Machine Learning Engineer - Intelligent Data, Signals & Systems

As a Staff Applied Machine Learning Engineer focused on Intelligent Data, Signal...

Location

United States , Bay Area

Salary:

276800.00 - 415200.00 USD / Year

Cash App

Expiration Date

Until further notice

Requirements

12+ years building and operating production software and ML systems for business-critical products
Deep expertise in intelligent systems such as ranking/retrieval, recommendations, search, personalization, growth and lifecycle ML, customer intelligence, propensity/churn/LTV, next-best-action, or model-derived risk signals
Strong production ML judgment across feature pipelines, model serving, experimentation, monitoring, feedback loops, online/offline consistency, and reliable signal interfaces
Ability to evaluate impact beyond short-term conversion, including trust, fairness, access, risk, compliance, and long-term engagement
Experience using AI-assisted engineering tools with appropriate verification, testing, and review for customer-impacting systems.

Job Responsibility

Build and operate production ML systems that turn customer and product context into trusted signals, rankings, recommendations, and decision capabilities
Design production data and signal contracts that define intended use, freshness, provenance, confidence, eligibility, and calibration for downstream consumers
Own ranking, retrieval, recommendation, search, propensity, and next-best-action systems end to end, from feature and candidate generation through serving, experimentation, monitoring, and feedback loops
Evaluate customer and business impact beyond short-term conversion, including trust, fairness, access, risk, compliance, long-term engagement, and segment-level performance
Partner across product, growth, data, platform, modeling, risk, and compliance to translate ambiguous goals into measurable ML system designs
Use AI and agents to accelerate development, analysis, testing, documentation, and operations while exposing reusable capabilities to product services, internal tools, and AI-assisted workflows.

What we offer

Remote work
Medical insurance
Flexible time off
Retirement savings plans
Modern family planning

Fulltime

Staff Machine Learning Engineer, Fulfillment Planning

The Fulfillment Planning team builds the intelligence that powers DoorDash's log...

Location

United States , San Francisco, CA; Sunnyvale, CA

Salary:

137100.00 - 299300.00 USD / Year

DoorDash

Expiration Date

Until further notice

Requirements

8+ years of industry experience building and deploying production-scale machine learning systems
Strong machine learning fundamentals and know how to apply them to large-scale production systems
Fluent in Python
Hands-on experience with modern ML frameworks, especially deep learning frameworks
Designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance
Can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations
Communicates clearly with both technical and non-technical audiences
Comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems
Has built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains
Experience with knowledge distillation from large teacher models into efficient production models

Job Responsibility

Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash
Work on challenging, real-world machine learning problems, including real-time assignment, routing, and fulfillment estimation
Lead 0→1 ML initiatives, defining how machine learning and optimization are applied across fulfillment products
Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash's logistics platform
Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment
Establish best practices for model development, deployment, monitoring, retraining, and governance
Define and lead DoorDash's cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics
Mentor other engineers and raise the technical bar for logistics ML across the organization

What we offer

401(k) plan with employer matching
16 weeks of paid parental leave
Wellness benefits
Commuter benefits match
Paid time off and paid sick leave in compliance with applicable laws
Medical, dental, and vision benefits
11 paid holidays
Disability and basic life insurance
Family-forming assistance
Mental health program

Fulltime

Member of Technical Staff - Machine Learning Engineer

As Microsoft continues to push the boundaries of AI, we are on the lookout for p...

Location

United States , Mountain View

Salary:

119800.00 - 234700.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
3+ years of experience building and deploying ML models in production environments
Strong coding skills in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow)
Familiarity with data processing tools (e.g., Spark, Pandas) and cloud platforms (e.g., Azure, AWS)
Experience with classification, recommendation, or personalization systems
Experience using large language models (LLMs) for machine learning and AI applications
Hands-on experience in growth engineering, driving improvements in user acquisition, engagement, and retention
Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
Expertise in personalization strategies and user behavior modeling
Strong problem-solving skills and the ability to independently design solutions to complex challenges

Job Responsibility

Develop and Deploy Models: Design, develop, and implement machine learning models for high-performance recommendation systems and personalized feeds
Large Language Model Expertise: Leverage large language models (LLMs) to create scalable, intelligent solutions for content understanding, user engagement, and relevance ranking
Experimentation and Analysis: Drive data-driven experimentation using A/B testing, advanced analytics, and statistical techniques to identify growth opportunities and refine algorithms
Infrastructure Optimization: Develop and optimize pipelines, tools, and infrastructure to support real-time decision-making, personalization, and predictive analytics
Technical Leadership: Mentor team members and foster collaboration within cross-functional teams, including engineers, product managers, and designers
Continuous Innovation: Stay informed on emerging trends in AI and machine learning, and integrate them to drive innovation and improve product offerings
Cross-functional Collaboration: Articulate findings and recommendations to technical and non-technical audiences, influencing decisions across teams and leadership
Embody our Culture and Values

Fulltime

Tonal is looking for a Staff Machine Learning Engineer to help expand Tonal’s in...

Location

United States , San Francisco; Toronto

Salary:

200000.00 - 235000.00 USD / Year

Tonal

Expiration Date

Until further notice

Requirements

7 plus years of experience in software engineering or applied ML
5 plus with a Master’s degree
PhD with 3 plus years of experience
Strong coding skills in Python
Experience with frameworks such as PyTorch, TensorFlow, or JAX
Experienced in ML training, evaluation, and deployment workflows such as Sagemaker, MLFlow, Databricks, or similar
Deep understanding of time series modeling, human motion, or sensor based learning from devices such as force transducers, position encoders, IMUs, or cameras
Familiar with MLOps best practices and scalable model training pipelines
Strong communicator who can collaborate with scientists, product managers, and engineers
Track record of delivering performant ML systems from prototype to production

Job Responsibility

Design, implement, and optimize machine learning training pipelines and model serving infrastructure for real time applications
Develop algorithms and ML models that enable personalized training, adaptive coaching, and performance prediction
Fine tune and evaluate transformer based or self supervised learning models using Tonal’s multimodal dataset
Build data driven systems that measure training effectiveness, effort, and progression beyond traditional weight based metrics
Prototype, train, and deploy ML models that run efficiently at scale or on device
Collaborate cross functionally with Exercise Science, Product, and Software teams to deliver intelligent features that improve the member experience
Contribute to the development of automated tools for experimentation, model validation, and continuous retraining
Write high quality, maintainable Python code and work closely with backend engineers to integrate models into Tonal’s production systems
Mentor teammates and help shape Tonal’s growing AI and ML best practices

What we offer

Offers Equity
Health insurance
Retirement savings benefits
Life insurance
Disability benefits
Flexible paid time off
Parental leave
Other additional benefits (location dependent)

Fulltime

Staff Machine Learning Research Scientist Engineer Agents

This role is at the intersection of cutting-edge AI research and practical appli...

Location

United States , San Francisco; Seattle; New York

Salary:

275000.00 - 350000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow
Adept at interpreting research literature and quickly turning new ideas into prototypes
A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.)
At least three years of experience addressing sophisticated ML problems, either in a research setting or product development
Strong written and verbal communication skills and the ability to operate cross-functionally

Job Responsibility

Explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation
Contribute to impactful research publications on agents
Collaborate with customer researchers
Work alongside the engineering team to translate these advancements into real-world, scalable solutions

What we offer

Comprehensive health, dental and vision coverage
Retirement benefits
A learning and development stipend
Generous PTO
Equity based compensation

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

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Staff Machine Learning Engineer - Data Intelligence

Job Description

Job Responsibility

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What we offer

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