About the Role 

We’re looking for an ML/AI Ops Engineer with 7+ years of experience to own the end-to-end operationalization of our ML/AI solutions, ensuring models move smoothly from development to scalable, reliable production products. In this role, you’ll design and automate CI/CD pipelines, build and optimize model lifecycle workflows, monitor deployed models for performance, drift, and reliability, and integrate intelligence products into various digital tools such as Copilot, Salesforce, and Tableau. You’ll collaborate closely with Data Scientists, Data Engineers, and Data Architects to transform high-quality research models into robust, production-grade products. This is a key role in shaping a modern ML/AI lifecycle—from CI/CD to high governance. 

What You’ll Do 

• Own the end-to-end operationalization of ML and AI solutions—from development to scalable, reliable production systemsthat integrateseamlessly with other digital tools. 
  • Design, automate, and maintain CI/CD pipelines for model training, testing, deployment, and retraining (Azure DevOps, Databricks). 
  • Build, optimize, and version model lifecycle workflows, ensuring reproducibility, lineage, and governance across the ML/AI platform. 
  • Monitor production models for performance, drift, reliability, and resource usage; implement automated retraining workflows. 
  • Optimize compute, storage, and orchestration across the Databricks platform to ensure efficient, cost-effective operations. 
  • Collaborate closely with ML/AI Scientists, Data Engineers, and DWH team to transform research-grade models into production-ready services. 
  • Contribute to advancing our ML/AI platform, tooling, automation standards, and best practices. 

What You’ll Bring 

• Solid experience in operationalizing ML/AI models, including deployment, automation, monitoring, and lifecycle management.
• Strong programming skills in Python,PySpark, and SQL with clean, efficient, production-ready code.
• Experienced in feature engineering with a practical understanding of data engineering fundamentals - designing, validating, and optimizing feature pipelines, and ensuring feature consistency
• Experience in building Vector embeddings & RAG systems.
• Familiarity in ML and LLM models development and libraries used.
• Experience with MLflow (or similar tools) for model tracking, registry management, and lifecycle operations.
• Familiarity with CI/CD pipelines (Azure DevOps preferred)
• Strong grasp of data versioning, model versioning, reproducibility, and data lineage within governed ML/AI environments.
• Experience designing, consuming, or integrating REST APIs to expose ML/AI models as services and support real-time or near-real-time inference.
• Experience monitoring production models, identifying drift or performance issues, and implementing corrective workflows.
• A collaborative, systems-thinking mindset, working closely with ML/AI Scientists, Data Engineers, and Data Warehouse team. 

Bonus Skills 

• Understanding ofdata quality frameworks and how they integrate into ML pipelines.
• Comfort with infrastructure-as-code for provisioning and managing ML/AI platform components. 
• Working knowledge of Unix environments and general DevOps principles; exposure to Docker/Kubernetes is beneficial.
• Experience with real-time or near-real-time serving architectures, event-driven systems, or streaming-based inference.
• Experience with AI agent tools and MCP servers. 
• Strong interest in contributing to internal ML/AI platform evolution, including tooling, automation, standards, and best practices.

What You’ll Get 

• Two weeks of paid vacation, 12 statutory holidays, plus 4 extra global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
• Paid parental leave: 8 days for fathers, 122 days for birthing parents, 92 days for adoptive parents
• Medical, dental, and vision coverage fully funded through INS Premium for employees and dependents
• Mental health support, therapy sessions, and virtual care via our Employee Assistance Program
• Retirement and social security contributions through Costa Rica’s statutory programs
• Life insurance equal to 24x monthly salary, plus disability and funeral coverage
• Daily cafeteria subsidy
• Fertility, adoption, and surrogacy support, plus 24 paid volunteer hours through Veeam Cares
• Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning

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About the Role

We are looking for an Analytics Engineer to join our internal Data Management team and help power data-driven decision-making across the organization. In this role, you will design and maintain scalable data models, pipelines, and transformation processes that enable analytics, reporting, and data science initiatives. You will collaborate closely with analysts, data scientists, and engineering teams to ensure reliable, efficient, and well-structured data that supports business insights and strategic initiatives.

What You’ll Do

• Design scalable and reliable data marts and transformation processes that support analytics and reporting needs
• Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and translate them into efficient data solutions
• Develop optimized data models and schemas that enable efficient storage, retrieval, and analysis of large datasets
• Build ETL and ELT pipelines that transform raw data from multiple sources into structured datasets for analytics and reporting
• Partner with data, architecture, and DevOps teams to ensure the scalability, performance, and reliability of data systems
• Monitor data pipelines and systems to troubleshoot issues and identify opportunities for improvement
• Document data architectures, data flows, and processes to support transparency, collaboration, and maintainability

Technologies You’ll Work With

• SQL and relational databases
• Cloud platforms such as Azure, AWS, or GCP
• Programming languages such as Python, Java, or Scala
• Databricks and Spark
• Data warehouse technologies such as Postgres or Vertica
• ETL/ELT frameworks and data transformation tools

What You’ll Bring

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience
• Strong proficiency in SQL and experience working with databases and data warehouse systems
• Experience building data pipelines and data processes in a cloud environment such as Azure, AWS, or GCP
• Experience with at least one programming language such as Python, Java, or Scala
• Knowledge of ETL/ELT processes, data modeling, and data warehousing best practices
• Ability to collaborate with cross-functional teams including analysts, engineers, and business stakeholders
• Strong written and verbal English communication skills (B2 level or higher)

Bonus Skills

• Experience working with Databricks and Spark
• Experience with Azure cloud services
• Experience with Postgres or Vertica data warehouse technologies
• Experience working with Salesforce data
• Experience building datasets used in analytics or reporting tools

What You’ll Get

• Two weeks of paid vacation, 12 statutory holidays, plus 4 extra global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
• Paid parental leave: 8 days for fathers, 122 days for birthing parents, 92 days for adoptive parents
• Medical, dental, and vision coverage fully funded through INS Premium for employees and dependents
• Mental health support, therapy sessions, and virtual care via our Employee Assistance Program
• Retirement and social security contributions through Costa Rica’s statutory programs
• Life insurance equal to 24x monthly salary, plus disability and funeral coverage
• Daily cafeteria subsidy
• Fertility, adoption, and surrogacy support, plus 24 paid volunteer hours through Veeam Cares
• Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning

Please note: The position is based in San Jose, Costa Rica. If the applicant is permanently located outside of Costa Rica, Veeam reserves the right to decline the application. All applications must be submitted in English.

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#LI-REMOTE

What You’ll Do 

• Design, implement, and evaluate novel machine learning and deep learning algorithms, concentrating on Large Language Models (LLMs), automated speech recognition (transcription), and unified multimodal architectures. 
• Iterate on AI model development, starting from the data needed for training, architecture, input/output representations, evaluation, and deployment for complex sequence-to-sequence and multimodal tasks. 
• Collaborate with other research engineers to prototype and validate complex solutions from academic literature in natural language processing and speech. 
• Conduct experiments to benchmark new techniques and evaluate model behavior. 
• Develop tools and frameworks to support scalable and reproducible research and development. 
• Communicate research findings to leadership in a concise, and convincing manner. 
• Stay current with the latest developments in Generative AI and Speech/Language Models and identify relevant innovations. 
• Assist in transitioning research prototypes into production-ready systems. 

Minimum Education Requirement: Master’s degree in Artificial Intelligence, Computer Science, Computational Sciences, Machine Learning, Informatics.  

Minimum Experience Requirements: AI/ML Engineer, Software Engineer, Research Scientist, AI Research Engineer or related job title in which 3 years of experience with developing and supporting production machine learning systems, including model deployment, monitoring, versioning, and retraining, using modern observability and experimentation tools (e.g., Grafana, Prometheus, Statsig); building ML and deep learning models using Python and PyTorch, including NLP tools (e.g., Huggingface, NLTK, spaCy, transformer-based models); with largescale data pipelines using distributed processing frameworks (e.g., Spark, Databricks); and collaborating with cross-functional teams to translate business requirements into ML solutions PLUS 1 year of experience developing computer vision or multimodal ML systems (e.g., OpenCV, YOLO, vision transformers) were gained.  

Alternate Education Requirement: PhD in Artificial Intelligence, Computer Science, Computational Sciences, Machine Learning, Informatics. 

Alternate Experience Requirements: AI/ML Engineer, Software Engineer, Research Scientist, AI Research Engineer or related job title in which 2 years of experience with developing and supporting production machine learning systems, including model deployment, monitoring, versioning, and retraining, using modern observability and experimentation tools (e.g., Grafana, Prometheus, Statsig); building ML and deep learning models using Python and PyTorch, including NLP tools (e.g., Huggingface, NLTK, spaCy, transformer-based models); with largescale data pipelines using distributed processing frameworks (e.g., Spark, Databricks); and collaborating with cross-functional teams to translate business requirements into ML solutions PLUS 1 year of experience developing computer vision or multimodal ML systems (e.g., OpenCV, YOLO, vision transformers) were gained.

Please apply online at: https://www.archer.com/careers. Must put job code SJ2026YM on resume/CV and cover letter. 

At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. For this position we are offering $240,000 to $252,000 per year.

#LI-DNF

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is based in San Jose, CA and requires 5 days/week in-office collaboration.

We are seeking a motivated Mid-Level Full-Stack Engineer to own the end-to-end lifecycle of our robot fleet telemetry, data management, and visualization tools. This role bridges the gap between raw hardware signals and actionable human-centric insights. You will design and maintain the data pipelines that process high-volume fleet telemetry while building the front-end interfaces that power fleet health monitoring, regression detection, and executive reporting.

Responsibilities: 

• Data Pipeline Engineering: Design, implement, and maintain scalable data pipelines to process high-volume fleet telemetry and subassembly data into analytics-ready datasets.
• Full-Stack Development: Architect, design, and deploy tools for robot configuration, diagnosis, calibration, and user interaction.
• Data Visualization: Develop and maintain curated datasets and frameworks powering dashboards, regression monitoring tools, and fleet-level KPIs.
• API & Framework Design: Build scalable, well-documented APIs to support mission-critical operations and self-service analytics.
• Data Governance: Establish and enforce data quality standards, schema governance, and tagging conventions to ensure long-term maintainability of the telemetry layer.
• Cross-Functional Collaboration: Partner with Robotics, Middleware, and Operations teams to translate operational needs into structured data models and visualization requirements.
• System Analysis: Support raw-signal investigations and serve as a primary data owner during incident investigations to enable cross-subsystem root-cause analysis.

Requirements: 

• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Robotics, or a related field.
• Experience: 3+ years of industry experience in software development or designing telemetry/analytics platforms.
• Front-End Expertise: Proficiency in JavaScript/TypeScript, HTML5/CSS, React, and Next.js.
• Back-End & Data: Strong proficiency in Python and SQL; experience building cloud-based data pipelines (e.g., AWS S3, Glue, Redshift).
• Infrastructure: Experience with Docker for containerization and deployment.
• Systems Interest: A strong interest in working on software systems that interact with real hardware.
• Communication: Exceptional verbal and written communication skills with the ability to partner with both engineering and business stakeholders.

Bonus Qualifications: 

• Platform Experience: Direct experience using Databricks for data engineering, Spark processing, or analytics.
• Robotics: Prior experience with robotics, autonomous systems, or hardware-in-the-loop telemetry.
• Advanced Analytics: Experience supporting anomaly detection or ML-based telemetry analysis pipelines.
• Distributed Systems: Experience with distributed data processing tools such as PySpark.
• Custom Systems: Prior experience working with custom hardware/software integrations.

The US base salary range for this full-time position is between $150,000 - $240,000 annually.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended. 

Data scientists at TTD work closely with engineering throughout the lifecycle of the product, from ideation to productionization and monitoring. Our data scientists are end-to-end owners. You will participate actively in all aspects of designing, researching, building, and delivering data-focused products for our clients and traders.

We are looking for a Senior Data Scientist to design, build, and scale data driven solutions powering our forecasting, pacing, and recommendation systems for our emerging channels. You will develop advanced machine learning or deep learning models that directly influence planning, allocation and performance on our platform.

The main job directions include:

• Develop forecasting models using statistical, ML and deep learning approaches.
• Build and optimize pacing algorithms that balance short-term performance with long-term objectives and constraints.
• Build custom solutions tailored to our unique environment, where off-the-shelf models often don’t scale effectively.
• Collaborate with product teams to understand business goals and translate them into actionable data science solution.
• Work closely with engineering teams to deploy models from prototype to production, ensuring smooth integration into products.
• Define success metrics, conduct offline evaluation and online experiments (A/B testing), and measure business impact.
• Communicate technical results and tradeoffs clearly to technical and non-technical audiences.

WHO WE ARE LOOKING FOR

- Proficient in open-source languages, you have a strong passion for enhancing and expanding your technical skills. Your expertise includes hands-on development of predictive models and solutions utilizing open-source tools and cloud computing platforms. Has a deep understanding of the foundations of statistics and machine learning

- Hands-on experience building data science solutions at scale. A track record of owning a project end-to-end (from research to production), and partnership with a cross-functional team of data scientists, engineers, and product managers to deliver the data science solution and models

WHAT YOU BRING TO THE TABLE

We do not expect you to know every technology we use when you start at TTD. What we care most about is that you can learn quickly and solve complex problems using the best tools for the job. However, we find that the most successful candidates typically come in with something like the following experience:

- BS/MS with 5+ years or a PhD with 3+ years of experience working in a DS and ML role that involves bringing products from ideation to production.

- Experience in building ML model in always-on production system and working with diverse technologies and data sources

- Strong experience in one or more of: forecasting, pacing systems, or recommendation systems.

- Proficient in Python

- Hands-on experience with deep learning frameworks (PyTorch, TensorFlow).

- Experience running heavy workloads on a distributed computing cluster (especially EMR or Databricks), leveraging technologies like Spark to work with large datasets.

- Experience in programmatic advertising and/or real-time auctions is preferred