Ml Platform Engineer Job at myPOS (Sofia)

Ml Platform Engineer

As an ML Platform / MLOps Engineer, you will design, build, and operate the infr...

Location

Romania , Bucharest

Salary:

Not provided

myPOS

Expiration Date

Until further notice

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)
5+ years of Data or ML Engineering experience, with at least 3 years shipping ML systems to production
Strong Python skills (typed code, async, testing) and solid SQL fluency
Hands-on MLOps experience: model registries, experiment tracking (MLflow or Vertex Experiments), pipeline orchestration, and reproducible training runs
Strong DevOps fundamentals: CI/CD (GitHub Actions, Cloud Build, or similar), IaC (Terraform), containerization (Docker)
Familiarity with at least one major cloud provider (GCP, AWS, Azure) and deploying data solutions in the cloud
Experience building and maintaining data pipelines with orchestrators (Airflow/Composer, Dagster) and distributed engines (Spark, BigQuery)
Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments
Collaborative mindset and clear communication across engineering, analytics, and business stakeholders

Job Responsibility

Build and maintain MLOps automation end-to-end: CI/CD for models and pipelines, environment management, artifact versioning (models, data, prompts, code), and release governance
Implement and operate model serving infrastructure: deployment patterns (blue/green, canary, shadow), endpoint management, scaling, and latency/throughput optimisation
Build and maintain training and experimentation infrastructure: job orchestration, compute provisioning, experiment tracking, hyperparameter management, and reproducibility tooling
Implement observability for ML systems: data quality checks, feature drift detection, model performance monitoring, bias checks, alerting, and incident response workflows
Build and maintain data pipelines for ingestion, transformation, feature engineering, and export across multiple sources and destinations
Design and maintain a feature store or feature platform layer: serving consistency, point-in-time correctness, and reuse across teams
Expose well-governed datasets, features, and APIs that models, pipelines, and downstream consumers can rely on
Enforce secure data handling and compliance with relevant data protection standards, access controls, and audit requirements
Contribute to documentation, platform standards, and continuous improvement of ML engineering processes across teams

What we offer

Vibrant international team operating in hi-tech environment
Annual salary reviews, promotions and performance bonuses
myPOS Academy for upskilling and training
Unlimited access to courses on LinkedIn Learning
Annual individual training and development budget
Refer a friend bonus as we know that working with friends is fun
Teambuilding, social activities and networks on a multi-national level

Fulltime

Ml Platform Engineer

As an ML Platform / MLOps Engineer, you will design, build, and operate the infr...

Location

Poland , Warsaw

Salary:

Not provided

myPOS

Expiration Date

Until further notice

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)
5+ years of Data or ML Engineering experience, with at least 3 years shipping ML systems to production
Strong Python skills (typed code, async, testing) and solid SQL fluency
Hands-on MLOps experience: model registries, experiment tracking (MLflow or Vertex Experiments), pipeline orchestration, and reproducible training runs
Strong DevOps fundamentals: CI/CD (GitHub Actions, Cloud Build, or similar), IaC (Terraform), containerization (Docker)
Familiarity with at least one major cloud provider (GCP, AWS, Azure) and deploying data solutions in the cloud
Experience building and maintaining data pipelines with orchestrators (Airflow/Composer, Dagster) and distributed engines (Spark, BigQuery)
Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments
Collaborative mindset and clear communication across engineering, analytics, and business stakeholders

Job Responsibility

Build and maintain MLOps automation end-to-end: CI/CD for models and pipelines, environment management, artifact versioning (models, data, prompts, code), and release governance
Implement and operate model serving infrastructure: deployment patterns (blue/green, canary, shadow), endpoint management, scaling, and latency/throughput optimisation
Build and maintain training and experimentation infrastructure: job orchestration, compute provisioning, experiment tracking, hyperparameter management, and reproducibility tooling
Implement observability for ML systems: data quality checks, feature drift detection, model performance monitoring, bias checks, alerting, and incident response workflows
Build and maintain data pipelines for ingestion, transformation, feature engineering, and export across multiple sources and destinations
Design and maintain a feature store or feature platform layer: serving consistency, point-in-time correctness, and reuse across teams
Expose well-governed datasets, features, and APIs that models, pipelines, and downstream consumers can rely on
Enforce secure data handling and compliance with relevant data protection standards, access controls, and audit requirements
Contribute to documentation, platform standards, and continuous improvement of ML engineering processes across teams

What we offer

Vibrant international team operating in hi-tech environment
Annual salary reviews, promotions and performance bonuses
myPOS Academy for upskilling and training
Unlimited access to courses on LinkedIn Learning
Annual individual training and development budget
Refer a friend bonus as we know that working with friends is fun
Teambuilding, social activities and networks on a multi-national level

ML Platform Engineer

We are looking for a motivated and experienced Machine Learning Platform Enginee...

Location

Israel , Tel-Aviv

Salary:

Not provided

K Health

Expiration Date

Until further notice

Requirements

5+ years in software engineering with experience in backend/platform roles
5+ years of experience with Python
Proficiency in another language, such as C++, Rust, Java, or Go, is an advantage
2+ years of experience working with cloud platforms such as Google Cloud (preferred), Azure, or AWS, including familiarity with ML workflow frameworks like KFP or Vertex Pipelines
Solid experience in ML/AI development (a must)
Experience with inference optimization (vLLM) and fine-tuning (Axolotl/Huggingface)
Expertise with transformers, PyTorch, CUDA, and other low-level ML libraries
Familiarity with Docker and Kubernetes
Excellent problem-solving skills and a proactive attitude, with a strong focus on code quality and optimization
Collaborative mindset with the ability to work closely with cross-functional teams. Strong communication and teamwork skills are essential

Job Responsibility

Design, develop, and maintain our machine learning ecosystem libraries
Build and manage data science code, Docker images, and Kubeflow Pipelines (KFP)
Create and maintain CI scripts to ensure seamless integration and delivery
Conduct thorough code reviews to uphold high-quality standards
Collaborate closely with data scientists, understanding and addressing their evolving needs
Work alongside software developers to seamlessly integrate machine learning models into production systems
Stay current with the latest advancements in machine learning, leveraging innovative techniques to enhance the company’s products and services

What we offer

20 paid vacation days, 18 days sick leave
Hybrid work schedule with team meals and stocked fridges
Commuter Benefits
Community focused events
Pension Plan
Stipend per day for food
Stock options for every full-time employee
Vocational Studies Fund

Fulltime

ML Platform Engineer

We are seeking a Machine Learning Engineer to help build and scale our machine l...

Location

United States

Salary:

Not provided

Duetto

Expiration Date

Until further notice

Requirements

3+ years of experience in ML engineering or a similar role building and deploying machine learning models in production
Strong experience with AWS ML services (SageMaker, Lambda, EMR, ECR) for training, serving, and orchestrating model workflows
Hands-on experience with Kubernetes (e.g., EKS) for container orchestration and job execution at scale
Strong proficiency in Python, with exposure to ML/DL libraries such as TensorFlow, PyTorch, scikit-learn
Experience working with feature stores, data pipelines, and model versioning tools (e.g., SageMaker Feature Store, Feast, MLflow)
Familiarity with infrastructure-as-code and deployment tools such as Terraform, GitHub Actions, or similar CI/CD systems
Experience with logging and monitoring stacks such as Prometheus, Grafana, CloudWatch, or similar
Experience working in cross-functional teams with data scientists and DevOps engineers to bring models from research to production
Strong communication skills and ability to operate effectively in a fast-paced, ambiguous environment with shifting priorities

Job Responsibility

Develop, maintain, and scale machine learning pipelines for training, validation, and batch or real-time inference across thousands of hotel-specific models
Build reusable components to support model training, evaluation, deployment, and monitoring within a Kubernetes- and AWS-based environment
Partner with data scientists to translate notebooks and prototypes into production-grade, versioned training workflows
Implement and maintain feature engineering workflows, integrating with custom feature pipelines and supporting services
Collaborate with platform and DevOps teams to manage infrastructure-as-code (Terraform), automate deployment (CI/CD), and ensure reliability and security
Integrate model monitoring for performance metrics, drift detection, and alerting (using tools like Prometheus, CloudWatch, or Grafana)
Improve retraining, rollback, and model versioning strategies across different deployment contexts
Support experimentation infrastructure and A/B testing integrations for ML-based products

Principal ML Engineer, ML Platform Engineering

Xometry is seeking a Principal Machine Learning Engineer to join our core machin...

Location

United States , North Bethesda

Salary:

140000.00 - 182000.00 USD / Year

Cherry Ventures

Expiration Date

Until further notice

Requirements

At least 7 years of experience in machine learning engineering, software engineering, data science, or similar technical role
A bachelor’s degree is required, but an advanced degree (M.S. or PhD) in computer science, machine learning, AI, or a related field is preferred and may substitute for some years of experience
Demonstrated experience designing and deploying cloud infrastructure (AWS preferred) to support machine learning, and machine learning models, with considerations for scale, reliability and security
Deep understanding of the machine learning lifecycle and related infrastructure needs - feature stores, a/b testing, model registration, drift detection, automated retraining, etc
Strong technical expertise. You will need to either have or demonstrate the ability ability to quickly build technical expertise in the following: Software engineering principles, including parallel and distributed computing, version control, reproducibility, and continuous integration
Machine learning techniques and algorithms, with emphasis on their impact to infrastructure implementation Including large-scale language and vision models (Transformers, GPT, VLMs, LLMs), deep learning (PyTorch, Tensorflow)
Infrastructure as Code (IaC), especially Terraform
REST API design and implementation
Object oriented and functional programming in Python
Multimodal data processing (e.g., combining text, image, and 3D data)

Job Responsibility

Hands-On Technical Leadership: Adopt a 'lead by example' approach by actively coding and troubleshooting, as well as creating documentation and technical diagrams
Teaching & Mentorship: You will serve as a mentor and guide to engineers across the organization, teaching and mentoring them to grow their skills
Code Review: You will do code review and mentor others within the organization regarding best practices in ML Engineering
Operational Excellence: Guarantee the delivery of superior infrastructure and software that not only meets but exceeds customer expectations, while aligning with the strategic business timelines
Collaborative Strategy: Forge strong partnerships with product managers, data scientists, and company leadership to promote a culture of open communication and integrated team dynamics
Guide Innovation: Champion the adoption of cutting-edge technologies, methodologies, and practices to enhance problem-solving efficiency and effectiveness across the AI/ML organization.

What we offer

401(k) match
medical, dental and vision insurance
life and disability insurance
generous paid time off including vacation, sick leave, floating and fixed holidays, maternity and bonding leave
EAP, other wellbeing resources

Fulltime

Senior ML Platform Engineer, AI Platform

We are seeking a skilled and passionate ML Platform Engineer to join our team an...

Location

Singapore , Singapore

Salary:

Not provided

Airwallex

Expiration Date

Until further notice

Requirements

5+ years in backend software development
at least 2+ years focus on AI/ML Platform or MLOps infrastructure
deep expertise in MLOps practices, including automated deployment pipelines, model optimization, and production lifecycle management
proven experience designing and implementing low-latency model serving solutions
proficiency in Python
skill in writing high-quality, maintainable code
experience in design and development of large-scale distributed, high concurrency, low-latency inference, high availability systems
excellent communication and mentoring abilities
a relevant degree in Computer Science, Mathematics or related fields

Job Responsibility

Platform Development: Design, build, and maintain the end-to-end MLOps platform using Kubernetes and Cloud Services
Infrastructure as Code (IaC): Use Terraform or similar tools to manage, provision, and scale all ML-related infrastructure securely and efficiently
Pipeline Automation: Implement and optimize CI/CD/CT (Continuous Integration, Delivery, Training) pipelines to automate model training, testing, packaging, and deployment using tools like Argo and Kubeflow Pipelines
Serving Infrastructure: Build highly available, low-latency, and high-throughput model serving infrastructure
Observability: Implement robust monitoring, alerting, and logging solutions to track infrastructure health, model performance, and data/model drift
Tooling & Support: Evaluate, integrate, and support ML tools such as Feature Stores and distributed model training pipelines
Security & Compliance: Ensure platform security, implement RBAC (Role-Based Access Control), and manage secrets for sensitive data and production environments
Collaboration: Work closely with Data Scientists and ML Engineers to understand their needs and provide technical guidance on best practices for scaling their models

Fulltime

Full Stack Engineer, ML Platform

Prior Labs is building foundation models that understand tabular data, the backb...

Location

Germany , Berlin

Salary:

Not provided

Prior Labs

Expiration Date

Until further notice

Requirements

5+ years of software engineering experience building highly-available user-facing products
Experience working across the stack (frontend + backend) and comfort owning features end-to-end
Passion for user experience, data science and machine learning
Enthusiasm for working in a fast-moving, ambiguous environment where new ideas ship quickly and user impact matters most
Strong JavaScript/TypeScript
Strong Python

Job Responsibility

Own our user-facing frontend & backend applications end-to-end to build the next-generation of data science tools
Work with product and research to turn new model and agent capabilities into production features
Build high-trust, scalable systems
Optimize for speed, reliability, and security

Fulltime

Senior Software Engineer, ML Platform

We’re looking for a software engineer to join Parafin’s Infrastructure team and ...

Location

United States , San Francisco

Salary:

230000.00 - 265000.00 USD / Year

Parafin

Expiration Date

Until further notice

Requirements

5+ years of software engineering experience, including experience on ML platform/MLOps systems (training, deployment, and/or feature pipelines)
Strong Python
solid software design and testing fundamentals
Proficiency with SQL
hands-on Spark/PySpark experience
Knowledge of ML fundamentals—probability & statistics, supervised vs. unsupervised learning, bias/variance & regularization, feature engineering, model evaluation metrics, validation strategies, and production concerns like drift, stability, and monitoring
Expertise with modern data/ML stacks—AWS, Databricks (workflows, lakehouse, MLflow/registry, Model Serving), and Airflow (or equivalent orchestration)
Experience building real-time systems (service design, caching, rate limiting, backpressure) and batch pipelines at scale
Practical knowledge of feature-store concepts (offline/online stores, backfills, point-in-time correctness), model registries, experiment tracking, and evaluation frameworks
Strong problem-solving skills and a proactive attitude toward ownership and platform health

Job Responsibility

Turn notebooks into software
Decompose data scientist training/inference notebooks into reusable, tested components (libraries, pipelines, templates) with clear interfaces and documentation
Create developer-friendly ML abstractions
Build SDKs, CLIs, and templates that make it simple to define features, train/evaluate models, and deploy to batch or real-time targets with minimal boilerplate
Build our real-time ML inference platform
Stand up and scale low-latency model serving
Expand batch ML inference
Improve scheduling, parallelism, cost controls, observability, and failure/rollback for large-scale batch scoring and post-processing
Own and expand the feature store
Design offline/online feature definitions, high read/write throughput, and consistent offline/online semantics

What we offer

Equity grant
Medical, dental & vision insurance
Work from home flexibility
Unlimited PTO
Commuter benefits
Free lunches
Paid parental leave
401(k)
Employee assistance program

Fulltime

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