Machine Learning Systems

Machine Learning Systems Engineer Jobs

5 jobs found

Today

Machine Learning Engineer, Perception

Path Robotics Columbus, OH

Columbus, OH

Today

Full time Artificial Intelligence Computer Vision Computer Vision Ml

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Job Description

Machine Learning Engineer, Perception builds weld perception for embodied robotics through computer vision, deep learning, and production deployment.

Responsibilities

Implement, validate, and iterate on ML algorithms for weld perception tasks including point cloud registration, seam detection, and joint geometry estimation, expanding coverage across joint types and part geometries.
Build and maintain data pipelines for training and evaluation, including annotated 3D scan ingestion, synthetic data generation, and structured dataset management for iterative improvement.
Run rigorous model evaluation experiments with failure mode analysis, FP/FN rate characterization, and benchmarking against quantitative registration accuracy thresholds; communicate findings to guide next steps.
Integrate trained models into production ROS-based robotics services, ensuring low-latency inference and compatibility with deployed cell configurations.
Write clean, well-tested Python code; participate in code and experiment reviews; maintain clear documentation of methods, parameters, and results.
Lead research, development, and production deployment of advanced perception algorithms covering point cloud registration, seam detection, and real-time in-process tracking across structured light, RGB, and stereo sensors.
Design and lead experiments evaluating state-of-the-art deep learning models, including transformer-based and geometric feature learning architectures.
Design and lead real-time perception systems for during-weld seam tracking, using sensor fusion with probabilistic state estimation (e.g., Kalman filtering) to support robust weld performance.
Own the end-to-end ML lifecycle, from dataset design and annotation strategy through training, benchmarking, and fleet deployment, using clear go/no-go evaluation frameworks.
Architect distributed training and hyperparameter optimization workflows; drive strategy for data acquisition, annotation tooling, and synthetic vs. real scan usage.
Mentor engineers across levels by providing technical leadership on perception systems and ML methodology.

Requirements

Master’s or Ph.D. in CS, Robotics, or related field (Computer Vision, ML, or Perception); Bachelor’s with strong production ML experience also considered.
3+ years (Experienced) / 7+ years (Senior/Staff) in real-world robotics or industrial ML.
Strong Python fluency and hands-on PyTorch experience training, evaluating, and deploying deep learning models in production.
Experience with 3D point cloud data and libraries such as Open3D, including geometric concepts like surface segmentation, spatial queries, and point-wise labeling.
Familiarity with 3D deep learning architectures such as PointNet++, GeoTransformer, or similar transformer-based or graph-based approaches on geometric data.
Comfort integrating ML models into production robotics services within ROS-based architectures and containerized deployment environments.
Senior/Staff: Demonstrated track record leading end-to-end ML projects from dataset design through fleet deployment with rigorous go/no-go frameworks; experience architecting distributed training and hyperparameter optimization workflows.

Technologies

Python
PyTorch
Open3D
ROS
Kalman filtering
PointNet++
GeoTransformer
Transformer-based architectures
Graph-based approaches
Containerized deployment environments
Structured light
RGB
Stereo sensors

Benefits

Daily free lunch
Flexible PTO
Comprehensive medical, dental, and vision coverage
6 weeks fully paid parental leave, plus an additional 6–8 weeks for birthing parents (12–14 weeks total)
401(k) retirement plan through Empower
Generous employee referral bonuses

Location: Columbus, OH (onsite) | Minimum experience: 3 years

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3d ago

Staff Machine Learning Engineer

Integral Ad Science, Inc. San Francisco, CA

San Francisco, CA

3d ago

$135k - $232k/yr Full time Application Security Artificial Intelligence Automation

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Job Description

Integral Ad Science is hiring a Staff Machine Learning Engineer (Data Science Team) to lead large-scale AI/ML systems and production deployments across the open web, social networks, video, and mobile.

Responsibilities

Set the architectural direction and system design for core AI/ML services
Serve as technical lead for multi-quarter initiatives from inception through global deployment
Design and build large-scale deep learning infrastructure and platforms for distributed model training
Deliver low-latency, high-availability ML systems at enterprise scale
Coordinate with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy
Convert abstract product requirements into concrete technical designs
Mentor and guide senior and mid-level data scientists and engineers
Define code quality expectations, establish standard methodologies, and lead architecture design reviews
Work with large-scale AI training infrastructure components (accelerators, network fabrics, CUDA, NCCL, RDMA)
Integrate with big data and infrastructure ecosystems (Databricks, Spark, Kubernetes, Kafka, Prometheus)
Build and support robust CI/CD pipelines that move models from research to production endpoints reliably

Requirements

PhD/Master’s degree in a technical field (e.g., computer science, mathematics, statistics) or equivalent years of experience
7+ years of machine learning experience in industry with a demonstrated history at Lead, Principal, or Staff level
Deep expertise designing complex ML systems, including ranking infrastructures and high-throughput data pipelines
Production-hardened experience with frameworks including PyT orch, JAX, or T ensorFlow
Strong production-grade programming skills in Python, Go, Java, or C++, with solid software design principles and algorithms knowledge
Ability to operate in ambiguity, take large unstructured problems, and drive them to technical resolution independently
Strong track record of collaborating with, elevating, and mentoring engineering talent

Technologies

PyT orch, JAX, T ensorFlow
Python, Go, Java, C++
CUDA, NCCL, RDMA
Databricks, Spark, Kubernetes, Kafka, Prometheus

Benefits

Paid time off
Health insurance (medical, dental, vision)
PPO, HSA and FSA options
401k with employer matching contributions
Annual bonus and/or other incentive plans (eligible)

Compensation & Location

Location: San Francisco, CA (onsite)
Salary range: USD 135,100 - 231,600 per year

IAS Pay Transparency

Primary Location: US - New York, NY
Primary Location Base Pay Range: $135,100.00 - $231,600.00 Annual
Additional Locations: US - Chicago, IL; US - San Francisco, CA
Additional Locations Pay Range: $119,000.00 - $204,000.00 USD Annual

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3d ago

Principal Machine Learning Engineer, Business Process Optimization

Workday Pleasanton, CA

Pleasanton, CA

3d ago

$191k - $342k/yr Full time Ai Ml Artificial Intelligence Automation

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Job Description

The Principal Machine Learning Engineer will lead architecture and implementation work on next-generation AI capabilities for Workday’s Business Process Optimize team within Admin Central. This role focuses on agentic frameworks, multi-tenant ML infrastructure, and enterprise-grade reliability to support billing integrity across global regions.

Role Focus

Agentic AI & Orchestration: Design, implement, and maintain advanced agentic capabilities, including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
Scalable ML Infrastructure: Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native technologies. Architect multi-tenant runtimes for fast, low-latency inference to millions of global users.
MLOps & Enterprise Reliability: Establish end-to-end MLOps standards, including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails. Ensure systems remain explainable, secure, and production-resilient.
Technical & Strategic Leadership: Collaborate with software engineers, Product Managers, Data Engineering, SRE, and Legal or Compliance stakeholders to convert ambiguous business needs into scalable technical designs and long-term data-governance strategies.

Key Responsibilities

Design, implement, and maintain advanced agentic capabilities, including tool calling, multi-agent workflows, long-range planning, reasoning frameworks, and human-in-the-loop integrations.
Build secure, high-throughput REST services and event-driven data pipelines using Python, Kubernetes, and cloud-native technologies.
Architect multi-tenant runtimes that deliver fast, low-latency inference to millions of global users.
Establish end-to-end MLOps standards, including CI/CD automation, observability, evaluation frameworks, model monitoring, and privacy or anonymization guardrails, ensuring systems are explainable, secure, and production-resilient.
Partner with software engineers, Product Managers, Data Engineering, SRE, and Legal or Compliance stakeholders to produce scalable technical designs and long-term data-governance strategies.

Required Qualifications

12+ years in ML development and R&D leadership, including 8+ years with advanced ML methodologies, distributed frameworks, and cloud-based platforms, delivering complex, scalable, production-grade ML systems.
6+ years building and hosting scalable ML services, microservices, and high-throughput data pipelines in production using Java, Go, Scala, or Python.
4+ years working with LLMs, generative AI, RAG pipelines, and autonomous agent frameworks for real-world enterprise applications.
Expertise in high-throughput streaming (Kafka), real-time feature extraction (Spark/Flink), and distributed columnar storage (ClickHouse, Iceberg) to architect large-scale ML pipelines.
Deep knowledge of deep learning, LLM orchestration, RAG, and agentic systems, applying ML algorithms and Model-Based Design (including MCP integrations and tool-use frameworks) for multi-agent coordination, intent classification, and workflow optimization.
Proven ability to lead EDA on multi-tenant agentic workflows, including high-dimensional embeddings, vector database management, and data isolation.
Proven ability to lead model building, fine-tuning, and deployment of domain-specific foundation models, including Kubernetes/Docker deployment, automated CI/CD retraining, and managing drift and latency at scale.
Mastery of Python and the ML ecosystem (PyTorch, TensorFlow, LangChain, LangSmith, LlamaIndex, NumPy, Pandas, Scikit-learn) and strong experience deploying production systems on PostgreSQL, Redis, Kafka, and cloud infrastructure (AWS/GCP/Azure).
Exceptional collaboration and mentoring skills, including technical leadership across cross-functional pods, driving sprint planning, and translating technical and governance decisions for executives.

Education

Bachelor’s degree (or equivalent experience) in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Master’s or PhD strongly preferred.

Location and Work Model

Pleasanton, CA (hybrid)

Compensation

USD 190,600 - 342,000 per yearly

Workday Pay Transparency Statement

Primary Location: USA.CA.Pleasanton
Primary Location Base Pay Range: $228,000 USD - $342,000 USD
Additional US Location(s) Base Pay Range: $190,600 USD - $342,000 USD

Flexible Work Approach

Spend at least half (50%) of your time each quarter in the office or in the field with customers, prospects, and partners (depending on role).
Remote “home office” roles may have opportunities to come together in Workday offices for important moments.

Technologies

Python, Kubernetes
Java, Go, Scala
REST services, event-driven data pipelines
CI/CD automation, observability, model monitoring
Privacy and anonymization guardrails
LLMs, generative AI, RAG pipelines
Autonomous agent frameworks, tool calling, multi-agent workflows, long-range planning, reasoning frameworks, human-in-the-loop integrations
Kafka, Spark, Flink
ClickHouse, Iceberg
MCP integrations, Model-Based Design, EDA
High-dimensional embeddings, vector database management
Docker
PyTorch, TensorFlow
LangChain, LangSmith, LlamaIndex
NumPy, Pandas, Scikit-learn
PostgreSQL, Redis, AWS, GCP, Azure

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3d ago

Senior Machine Learning Engineer - Pricing

Uber San Francisco, CA

San Francisco, CA

3d ago

$202k - $224k/yr Full time Artificial Intelligence Big Data Data Pipeline

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Job Description

Uber’s Dynamic Supply Pricing (DSP) team is building machine learning models and pricing algorithms that power real-time driver pricing at massive scale. As a Senior Machine Learning Engineer - Pricing, you’ll lead end-to-end work that takes advanced ML and optimization approaches from design through productionization, helping create earnings opportunities for drivers while supporting large-scale marketplace performance.

What you’ll work on

Design, develop, and productionize end-to-end ML solutions for large-scale distributed systems that serve billions of trips
Create novel pricing approaches for online marketplaces using a combination of machine learning, algorithmic game theory, and optimization
Collaborate with team leads to set technical direction and own implementation, including technical mentorship for junior engineers
Partner with engineers, product managers, and scientists to deliver high-impact solutions to complex business problems

Responsibilities

Lead design, development, and productionization of end-to-end ML solutions for large-scale distributed systems serving billions of trips
Develop pricing approaches for online marketplaces using machine learning, algorithmic game theory, and optimization to provide earnings opportunities for millions of drivers
Collaborate with team leads on technical direction and own implementation, providing technical mentorship to junior engineers
Work with engineers, product managers, and scientists to design and deliver high-impact technical solutions to complex business problems

Requirements

Ph.D., M.S., or Bachelor’s degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact
4+ years of experience developing and deploying machine learning models and optimization algorithms in large-scale production environments, delivering measurable business impact over multiple quarters and making significant technical contributions
Proficiency in Python, Scala, Java, or Go
Experience with large-scale data systems (Spark, Ray), real-time processing (Flink), and microservices architectures
Experience developing, training, productionizing, and monitoring ML solutions at scale, from offline pipelines to online serving and MLOps
Deep understanding of modern ML algorithms (DNNs, multi-task models, transformers) and mathematical optimization (LP, convex optimization)

Preferred Qualifications

Experience building and deploying pricing algorithms for multi-sided real-time marketplaces with strategic agent behavior
Experience leading complex technical projects and influencing scope and output of others
Track record of translating ambiguous business problems into technical solutions and driving multi-functional projects
Excellent communication skills to lead initiatives and collaborate with cross-functional partners
Experience in reinforcement learning and causal machine learning

Technologies

Python, Scala, Java, Go
Spark, Ray, Flink
Microservices architectures
MLOps
DNNs, transformers

Compensation and benefits

Base salary: USD 202,000 - 224,000 per year

Benefits: eligible to participate in Uber’s bonus program; may be offered an equity award and other types of comp; eligible to participate in a 401(k) plan; eligible for various benefits.

Work location

San Francisco, CA (onsite)

Unless approved for full remote work, employees must spend at least 50% of their time in-office.

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6d ago

Sr Machine Learning Engineer, ML Systems Engineering - Proactive

Apple Pittsburgh, PA

Pittsburgh, PA

6d ago

Full time Agentic Ai Ai Agent Artificial Intelligence

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Job Description

At Apple, this role supports Proactive Intelligence, applying machine learning and agentic AI to deliver personalized user experiences across Apple devices. You will help build and refine agentic machine learning applications that operate in production, focusing on evaluation, optimization, and reliable delivery.

Based in Pittsburgh, PA (onsite), the Senior Machine Learning Engineer will design and improve multimodal ML systems, partner with the team on experimentation and debugging, and contribute to engineering practices that support fast, confident progress.

Responsibilities

Design, implement, and evaluate data and machine learning systems using multimodal inputs for production systems.
Plan and conduct experiments to debug and optimize agentic machine learning applications.
Establish scalable automated processes for evaluation and monitoring.
Contribute to a team culture grounded in respect, empowerment, and growth.
Commit to engineering excellence by upholding best practices that enable shipping quickly with confidence.
Place user experience first and use storytelling to communicate complex ideas clearly.
Develop innovative tools and techniques that change how problems are approached.
Ship software that customers depend on every day.

Requirements

Experience shipping machine learning and agentic systems with a strong software engineering background.
Experience designing, implementing, and evaluating data and machine learning systems on multimodal inputs for production systems.
Ability to plan and conduct experiments and to debug and optimize agentic machine learning applications.
Willingness to ask questions, with an eagerness to teach and learn together.
4+ years of experience building production machine learning systems.
MS in Computer Science or equivalent practical experience.
Proficiency in Python.
Proficiency in at least one compiled language: C, C++, Objective-C, Swift, Go, Java, Kotlin, C#, or Rust.

Technologies

Python
C
C++
Objective-C
Swift
Go
Java
Kotlin
C#
Rust

Preferred Qualifications

Production experience with training, deploying, and evaluating autonomous agentic systems.
Production experience working with ML pipelines and large datasets.
Experience in resource constrained computing (for example, embedded systems or mobile development).
Proficiency in using AI agent assistance effectively as part of software development workflows.

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Machine Learning Systems Engineer Jobs

Machine Learning Engineer, Perception

Staff Machine Learning Engineer

Principal Machine Learning Engineer, Business Process Optimization

Senior Machine Learning Engineer - Pricing

Sr Machine Learning Engineer, ML Systems Engineering - Proactive

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