Bring your analytical curiosity to meaningful work at Osaic, where innovation helps financial professionals better serve their clients. Help advance our north star of becoming the #1 firm in advisor satisfaction.
Compensation
Salary: $70,500 - $87,000 per year + annual performance-based bonus
Responsibilities
Develop clear, scalable views of performance that explain what changed, where it changed, and the factors driving the result
Continuously improve reporting workflows, data latency, analytical depth, and stakeholder deliverables by applying automation and the latest AI-enabled tools
Investigate performance drivers and business initiatives across advisor and practice economics, including recruiting, retention and cost to serve, to inform investment, pricing and service decisions.
Synthesize structured and unstructured analysis into concise explanations of performance drivers, implications, and recommended next steps
Partner with business, finance, product, marketing, and data teams to align definitions, validate results, and connect performance trends to business activity
Requirements
Master’s Degree in a quantitative discipline preferred
Bachelor’s degree from accredited university in a quantitative discipline required
Senior MLOps & Cloud Infrastructure Engineer responsible for building, operating, and evolving the cloud infrastructure and engineering platforms behind production AI systems.
Responsibilities
Design, provision, and operate scalable infrastructure on GCP
Manage containerised applications and ML workloads with Kubernetes
Build reproducible infrastructure using Terraform
Design and improve CI/CD pipelines for backend services and data pipelines
Improve service availability, resilience, and incident response
Scientist or engineer focused on expansion microscopy development and protocol optimization within a sample production pipeline.
Responsibilities
Independently design, coordinate, and execute bench level experiments focused on expansion microscopy.
Operate within our sample production pipeline to ensure a continuous flow of high-quality gels.
Iteratively optimize expansion microscopy workflows by designing, sourcing and adapting techniques and methodologies from the literature and scientific community.
Pilot variations of expansion microscopy protocols focused on optimizing signal retention for multi-round cycles of immunostaining, imaging, and signal extinction.
Pilot methods for increasing diffusion within expanded hydrogel/tissue hybrids.
Requirements
Ph.D. in biology, chemistry, bioengineering, materials science, mechanical engineering, or equivalent research or industry experience
Nice to have: advanced optical microscope modalities (spinning-disc confocal or light-sheet microscopy)
Nice to have: quantitative scoring systems for expansion microscopy or imaging data
Nice to have: scaling expansion microscopy throughput in routine production environments
Nice to have: automating lab workflows (liquid handlers, microcontrollers like Arduino or Raspberry Pi, scripting in Python)
Lead the industrialization of end-of-line manufacturing test capability for complex precision electromechanical assemblies.
Compensation
Pay range / Rango salarial / Intervalo salarial /Fascia retributiva / Salaribereik: Ireland: 81,120.00 EUR - 121,680.00 EUR
Responsibilities
Own end-to-end industrialization of the end-of-line manufacturing test system
Define test-system requirements, measurement architecture, test methods, equipment specifications, interfaces and objective acceptance criteria
Lead integration of fixtures, load systems, sensors, instrumentation, electrical hardware, motion control, data acquisition and test software across the lab, pilot line and final production environment
Drive system development, debugging, characterization and qualification, including calibration, measurement uncertainty, MSA/Gauge R&R, correlation and capability
Engineer the solution to meet cycle-time, throughput, availability, maintainability, robustness, traceability, replication and capacity requirements
Requirements
Bachelor’s or advanced degree in Mechanical, Electrical, Mechatronics, Test, Controls or a related engineering discipline
Support prototype builds, engineering samples, and new product introductions by configuring existing measurement and inspection systems for new products.
Compensation
The range for this position is $81,701.00 - $112,339.00 assuming full time status.
Responsibilities
Support prototype builds, engineering samples, and new product introductions by configuring existing measurement and inspection systems for new products.
Work with global engineering, manufacturing, quality, and measurement experts to translate requirements and testing needs into equipment and product configurations.
Plan and run measurement system qualification, including measurement system analysis, gauge repeatability and reproducibility studies, capability studies, and method correlation.
Evaluate commercially available measurement and inspection technologies, and troubleshoot configuration issues during prototype and early manufacturing builds.
Create and maintain product configurations, test parameters, and technical documentation, and train engineering and operations teams on measurement systems and methods.
Requirements
Bachelor's degree in Engineering, Physics, Optics, Mechanics, or a related field
Own the product capability that lets PhysicsX and its customers improve LLMs for their specific work, turning post‑training methods into usable solutions.
Responsibilities
Own the path from a base model and customer workflow to a measured, deployable model improvement
Build reliable post‑training harnesses covering data preparation, training, evaluation, model artifacts and hand‑off to serving
Improve open models against PhysicsX's agent and engineering benchmarks
Develop and operate methods spanning SFT, preference optimisation, RLHF/RLAIF, RL with verifiable rewards, distillation, rejection sampling and related techniques
Build reproducible synthetic‑data and expert‑data pipelines
Requirements
Nice to have: post‑training open‑weight models for tool use, reasoning or long‑horizon agent workflows
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