As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.
Locations (3)
Wrocław, Poland
Gdańsk, Pomerania, Poland
Poland
Responsibilities
Own the ML lifecycle: take models from problem framing through training, deployment and monitoring
Frame problems: turn business goals into ML tasks and metrics. Use plain code where a model isn’t needed
Build models: classical ML, time series and NLP, plus LLM and RAG features where they fit
Evaluate rigorously: sound validation, the right metrics, and evals that gate every release
Ship and run models: deploy with Docker and CI/CD on cloud ML platforms, and monitor drift and cost
Lead Data Engineer role focused on driving the modernization of a large-scale reconciliation data platform from legacy Hadoop to Databricks on AWS using Scala Spark and cloud-native patterns.
Responsibilities
Build and optimise large-scale Scala Spark data pipelines on Databricks, focusing on the reconciliation domain — writing clean, modular, and production-ready code
Drive modernisation efforts by refactoring legacy Hadoop-based pipelines into cloud-native Databricks/AWS architectures using Delta Lake and Databricks Workflows
Tune and simplify complex or over-engineered Spark jobs — analysing performance bottlenecks and transforming them into efficient, standardised patterns
Translate designs into reality — take high-level architectural decisions and convert them into detailed technical implementations, data models, and reusable pipeline components
Collaborate daily with architects, platform, and DevOps teams on design discussions, technical decisions, and production releases
Lead a team of data analysts in AML modeling and advanced data insights, driving strategy, building models, and delivering business value.
Locations (4)
Plano, Texas, United States
McLean, Virginia, United States
Richmond, Virginia, United States
Chicago, Illinois, United States
Compensation
Plano, TX: $149,800 - $171,000 for Data Analysis Manager
McLean, VA: $164,800 - $188,100 for Data Analysis Manager
Richmond, VA: $149,800 - $171,000 for Data Analysis Manager
Chicago, IL: $149,800 - $171,000 for Data Analysis Manager
Responsibilities
Drive current and future strategy by leveraging analytical skills
Lead and scale the Mobile Integrations team, driving holistic mobile architecture, technological direction, and platform scalability for SDKs.
Responsibilities
Take ownership of the Mobile Integrations team from day one, setting the benchmark for engineering excellence across our mobile SDKs
Scale scope to lead up to 3 cross-functional mobile engineering teams, guiding line managers and tech leads
Drive the holistic mobile architecture, technological direction, and platform scalability
Collaborate closely with the VP of Engineering, Product leadership, and executive stakeholders to translate high-level business goals into a concrete, high-impact mobile platform roadmap
Champion engineering best practices, sustainable code reviews, and architectural alignment across teams
Lead, support, and mentor a team of junior data engineers within an agile framework.
Write defensive, fault tolerant and efficient code for production level data processing systems.
Configure and deploy Quantexa’s software using tools such as Spark, Hadoop, Scala, Elasticsearch, with our platform being hosted on both private and public virtual clouds, such as Google cloud, Microsoft Azure and Amazon.
Act as a trusted source of knowledge to clients, articulating technical concepts to non-technical audiences.
Collaborate with solution architects and R&D engineers to champion solutions and standards for complex big data challenges.
Build and maintain controls data models, pipelines, dashboards, forecasting and AI tools to support project controls across Blue Origin facilities.
Compensation
Base Pay Range for: WA applicants is $130,706.00 - $182,987.70
Responsibilities
Build and maintain data pipelines that join schedule, cost, labor actuals, procurement, change requests, engineering configuration and risk into a governed controls dataset
Define the controls data model and data dictionary
Build and run Monte Carlo schedule and cost risk models
Build and run portfolio and project dashboards
Design, build and deploy AI agents and LLM-based tools for controls work
Requirements
Bachelor's degree in a quantitative or technical field such as data science, statistics, information systems, computer science, mathematics, economics, finance, business analytics, construction management or engineering
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