Machine Learning Engineer (MLOps Lead) Job at RED Commerce - The Global SAP Solutions Provider (London)

Lead Machine Learning Engineer (Gen AI, Python, Go, AWS)

As a Capital One Machine Learning Engineer (MLE) on the GenAI Workflows Serving ...

Location

United States , San Francisco; McLean; New York; Cambridge

Salary:

197300.00 - 245600.00 USD / Year

Capital One

Expiration Date

Until further notice

Requirements

Bachelor's Degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
At least 4 years of experience programming with Python, Scala, Go or Java
At least 2 years of experience building, scaling, and optimizing ML systems

Job Responsibility

Design, build, and deliver GenAI models and components that solve complex business problems, while working in collaboration with the Product and Data Science teams
Design and implement cloud-native ML Serving Platforms leveraging technologies like Docker, Kubernetes, KNative, and KServe to ensure optimized and scalable deployment of models
Solve complex scaling and high-availability problems by writing and testing performant application code in Python and Go-lang, developing and validating ML models, and automating tests and deployment
Implement advanced MLOps and GitOps practices for continuous integration and continuous deployment (CI/CD) using tools like ArgoCD to manage the entire lifecycle of models and applications
Leverage service mesh architectures like Istio to manage traffic, enhance security, and ensure resilience for high-volume serving endpoints
Retrain, maintain, and monitor models in production
Construct optimized, scalable data pipelines to feed ML models
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
Use programming languages like Python, Go, Scala or Java

What we offer

performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
comprehensive, competitive, and inclusive set of health, financial and other benefits

Fulltime

Lead Machine Learning Engineer

Machine Learning Engineers specializing in Inference Optimization focus on maxim...

Location

Singapore , Singapore

Salary:

Not provided

Thoughtworks

Expiration Date

Until further notice

Requirements

Deep practical expertise in model and runtime optimization techniques (quantization, pruning, distillation, batching, caching)
Proven experience optimizing inference workloads using frameworks such as vLLM, NVIDIA Triton/Dynamo
Strong proficiency in deep learning frameworks (e.g. PyTorch, TensorFlow) with production deployment experience
Ability to diagnose and optimize performance using profiling tools (e.g. Nsight, PyTorch/TensorFlow profilers)
Solid understanding of GPU and accelerator architectures, and experience tuning workloads for cost and performance efficiency
Experience designing and benchmarking scalable inference systems across heterogeneous environments (GPU clusters, serverless, edge)
Familiarity with observability stacks, telemetry, and cost instrumentation for AI workloads
Demonstrated ability to lead small-to-medium engineering teams or technical workstreams
Skilled at balancing hands-on delivery with architectural oversight and mentorship
Strong communication and stakeholder engagement skills and are able to connect low-level optimizations with business impact

Job Responsibility

Lead the design and implementation of advanced model optimization pipelines, including quantization, pruning, and distillation
Architect and tune inference runtimes and serving frameworks to achieve optimal performance across deployments
Guide teams in implementing high-throughput serving strategies (continuous batching, KV caching, speculative decoding, asynchronous scheduling)
Develop benchmarks and performance dashboards to measure and communicate system-level efficiency improvements (throughput, latency, GPU utilization, cost)
Evaluate trade-offs across accuracy, performance, and cost, and design architectures to meet target SLAs across varied hardware environments (cloud, on-prem, edge)
Collaborate with infrastructure, MLOps, and product teams to embed inference optimization into production workflows and platform designs
Provide technical leadership and mentorship to engineers, fostering a culture of experimentation, rigor, and continuous performance improvement
Contribute to the development of internal frameworks, reference architectures, and playbooks for scalable and cost-efficient inference
Engage with clients to translate optimization outcomes into business value and articulate the ROI of technical improvements

What we offer

Learning & Development
Interactive tools
Numerous development programs
Teammates who want to help you grow
Empowering our employees in their career journeys

Fulltime

Senior Machine Learning Engineer (Team Lead)

As our Artificial Intelligence (AI) and Machine Learning (ML) Team Leader, you w...

Location

Australia , South Bank

Salary:

Not provided

Flight Centre Brand

Expiration Date

Until further notice

Requirements

7+ years delivering production grade ML or AI systems with proven commercial impact
3+ years Leading and Mentoring engineers
Experience building AI agents, RAG systems or LLM powered applications in production
Demonstrated experience leading technical teams and managing complex AI programmes
Strong hands on experience across ML infrastructure, distributed systems and scalable AI architecture
Experience building and governing AI agent platforms including endpoints, gateways and tool orchestration
Familiarity with MCP servers and emerging agent communication standards and protocols
Experience defining evaluation frameworks, safety mechanisms and governance for LLM and agent based systems
Deep knowledge of Python, modern AI/ML frameworks and scalable AI platforms including Databricks
Strong expertise in Kubernetes and cloud native production environments

Job Responsibility

Lead the development and productionisation of ML models, LLM powered systems and agent based applications
Define and build end to end MLOps including CI CD, model registry, monitoring, drift detection and retraining for predictive ML systems
Establish LLMOps standards including context engineering, automated evaluation pipelines, red teaming, safeguards and policy guardrails
Architect and build AI agent workflows, endpoints, gateways and orchestration layers enabling secure tool access, structured reasoning and multi agent collaboration
Design and govern MCP servers and modern agent communication protocols to ensure interoperability, security and scalability
Implement strong observability across ML and GenAI systems including reliability, latency, evaluation metrics, usage tracking and cost control
Drive scalable ML infrastructure, feature stores and data platforms on Databricks
Oversee Kubernetes based deployments and cloud native AI infrastructure
Partner with senior stakeholders to prioritise and deliver multiple high impact AI initiatives
Coach and grow a high performing AI engineering team

What we offer

Individualised, ongoing Learning & Development via communities of practice
Innovation Days
Dedicated Engineering Days
Access to 'LinkedIn Learning' for ongoing skills development
Women in PM&E group
Exclusive Staff Discounts
Travel Discounts
Career opportunities in a network of brands and businesses across the globe
Corporate Health Discounts
Mental Health Support and Employee Assistance Program for staff and family

Fulltime

Sr Machine Learning Engineer, Pricing

Uber's Marketplace is at the heart of Uber's business and the Dynamic Supply Pri...

Location

United States , New York; Seattle; San Francisco; Sunnyvale

Salary:

202000.00 - 224000.00 USD / Year

Uber

Expiration Date

Until further notice

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 in 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 programming languages such as Python, Scala, Java, or Go
Experience with large-scale data systems (e.g. Spark, Ray), real-time processing (e.g. Flink), and microservices architectures
Experience in the development, training, productionization and monitoring of ML solutions at scale, ranging from offline pipelines to online serving and MLOps
Deep understanding of modern ML algorithms (e.g. DNNs, multi-task models, transformers) and mathematical optimization (e.g. LP, convex optimization)

Job Responsibility

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

What we offer

Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
Eligible to participate in a 401(k) plan
Eligible for various benefits

Fulltime

Senior Engineer, Machine Learning Engineering

Mastercard’s Business & Market Insights (B&MI) group empowers organizations to a...

Location

India , Pune

Salary:

Not provided

Mastercard

Expiration Date

Until further notice

Requirements

Master’s/bachelor’s degree in computer science or engineering
Considerable work experience with a proven track-record of successfully leading and managing complex projects/products and delivering to aggressive market needs
Expert-level hands on experience designing, building and deploying both conventional AI/ML solutions and LLM/Agentic solutions
Strong analytical and problem-solving abilities, with quick adaptation to new technologies, methodologies, and systems
Strong applied knowledge and hands on experience in advanced statistical techniques, predictive modelling, machine learning algorithms, GenAI and deep learning frameworks
Experience with AI and machine learning platforms such as TensorFlow, PyTorch, or similar
Strong programming skills in languages such as Python/SQL is a must
Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus

Job Responsibility

Implement multi-agent intelligence frameworks (LangGraph, CrewAI, AutoGen) to enable reasoning, coordination, and adaptive decision-making across specialized AI agents
Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, etc.) for unified intelligence
Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs (Neo4j, AWS Neptune) to enable contextual retrieval, semantic linking, and entity-aware reasoning
Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction tasks leveraging Hugging Face, PyTorch, and TensorFlow ecosystems
Implement advanced Python-based backend services for inference orchestration, async job handling, and distributed data workflows supporting high-throughput AI operations
Establish end-to-end LLMOps and MLOps pipelines on Databricks (AWS) integrating MLflow, feature stores, model lineage, prompt evaluation, and continuous retraining frameworks
Apply traditional AI/ML and statistical modeling techniques (regression, clustering, forecasting, ensemble methods) alongside deep learning models for hybrid interpretability and explainability
Engineer state and memory management subsystems that preserve context, track embeddings, and enable agents to reason temporally across multiple modalities and interactions
Implement Responsible AI practices—bias detection, explainability dashboards, data ethics checks, and performance governance ensuring fairness and transparency of deployed models
Continuously research, benchmark, and productionize innovations in multimodal transformers, generative modeling, and agentic orchestration to drive enterprise-scale intelligence and automation

Fulltime

Senior Machine Learning Engineer - Earner Incentive

Uber’s Marketplace is at the core of the business. The Earner Incentive team in ...

Location

United States , Seattle; San Francisco; Sunnyvale

Salary:

202000.00 - 224000.00 USD / Year

Uber

Expiration Date

Until further notice

Requirements

Ph.D., M.S., or Bachelor’s degree in Computer Science, Statistics, Mathematics, Machine Learning, Operations Research, or a related field, or equivalent practical experience with demonstrated impact
5+ years of experience across the end-to-end ML lifecycle, including data analysis, feature engineering, model development, deployment, monitoring, and iteration in large-scale production systems
Proven ability to deliver measurable business impact and strong understanding of MLOps best practices
Strong understanding of a broad range of ML and statistical techniques, including deep learning (e.g., multi-task learning, transformers), tree-based models, and classical approaches
Proficiency in at least one production language (Python, Scala, Java, or Go) and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
Solid software engineering skills, including system design, writing and reviewing production-quality code, testing, and operating ML systems in production
Strong ownership, learning mindset, collaboration and communication skills
able to work independently and effectively in cross-functional teams

Job Responsibility

Design, develop, productionize, and operate end-to-end ML solutions and data pipelines for large-scale systems that power driver incentives
Develop and apply advanced ML and optimization techniques to design incentive mechanisms for online marketplaces
Build deep domain expertise in incentives, pricing, and marketplace dynamics, and understand how these systems interact with Operations
Help set the team’s technical direction and drive execution in partnership with technical leads
Collaborate closely with engineers, product managers, scientists, and Operations to drive clarity, alignment, and delivery of high-impact solutions
Own projects end-to-end, from ideation and design through production rollout and iteration, and drive measurable business impact across teams

What we offer

Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
All full-time employees are eligible to participate in a 401(k) plan
Eligible for various benefits

Fulltime

Senior Machine Learning Engineer - Maps

The Places Data Team owns Uber's "Ground Truth" — the definitive dataset of POIs...

Location

Netherlands , Amsterdam

Salary:

Not provided

Uber

Expiration Date

Until further notice

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 in 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 programming languages such as Python, Scala, Java, or Go
Experience with large-scale data systems (e.g. Spark, Ray), real-time processing (e.g. Flink), and microservices architectures
Experience in the development, training, productionization and monitoring of ML solutions at scale, ranging from offline pipelines to online serving and MLOps

Job Responsibility

Design, develop and productionize end-to-end ML solutions for places data conflation (POI, addresses, BFP, etc.) and attribute inference using a mix of classical ML, deep learning, and generative AI
Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap
Conduct rigorous experimentation and A/B testing to validate model performance and iterate on improvements
Own projects from initial mathematical formulation through to prototyping, algorithm implementation, and large-scale experimentation in production
Raise the technical bar for the team. You will mentor L3/L4 engineers, lead complex code reviews, and foster a culture of engineering excellence and scientific rigor

Principal Machine Learning Engineer

As a Principal Machine Learning Engineer in ZMS, you will be the tech lead worki...

Location

Germany , Berlin

Salary:

Not provided

Zalando

Expiration Date

Until further notice

Requirements

Excellent software development engineering skills to design computationally effective solutions for machine learning operationalization and maintenance (MLOps/MLaaS) in large-scale production environments (data engineering, data version control, model serving, continuous monitoring & alerting)
Strong verbal and written communication and presentation abilities when discussing complex ideas with both technical and non-technical stakeholders
Hands-on professional experience in programming, using Python, Java Flink, pySpark, PyTorch, and TensorFlow
Strong programming skills with a high performance language (Java, Scala, Go, etc) and experience working with Python in production
Experience building, deploying and operating data-driven systems in a cloud environment, including experience with feature stores & feature engineering pipelines, data ingestion & transformation, machine learning workflow orchestration
Thrive to coach and mentor senior engineers, and work closely with applied scientists, senior machine learning engineers and data scientists

Job Responsibility

Drive the operationalization of solutions deployed in production, and help the team grow and cultivate best practices in software development and MLOps
Architect and lead the development of machine learning solutions that can handle low latency, high availability and high volume scenarios
Mentor engineers and provide technical guidance across multiple projects simultaneously while managing competing priorities effectively within agreed-upon timelines
Apply techniques and create processes to optimise deployed models for better performance, latency, and memory usage
Work closely with applied science and engineering teams, product managers and other business stakeholders to bring our state-of-the-art solutions to customers and to discover and identify new opportunities

What we offer

27 days of holiday a year to start for full-time employees (+1 day for every calendar year up to 30 days)
2 paid volunteering days a year
Hybrid working model with up to 60% remote per week
Work from abroad for up to 30 working days a year
Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners
Relocation assistance available (subject to prior agreement)
Family services, including counseling and support
Health and wellbeing options (including Wellhub, formerly Gympass)
Mental health support and coaching available

Fulltime

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Machine Learning Engineer (MLOps Lead)

Job Description

Requirements

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