ZipRecruiter

Log In

1

Geometric Modeling Jobs in Massachusetts (NOW HIRING)

Amazon

Sr. Robotics Mech Engineer, Velocity

Westborough, MA · On-site

$107K - $142K/yr

... models, engineering drawings, and specifications for mechanical components and assemblies - Conduct engineering analyses including tolerance stack-ups, Geometric Dimensioning and Tolerancing (GD&T ...

Amazon

Sr. Robotics Mech Engineer, Velocity

Westborough, MA · On-site

$107K - $142K/yr

... models, engineering drawings, and specifications for mechanical components and assemblies - Conduct engineering analyses including tolerance stack-ups, Geometric Dimensioning and Tolerancing (GD&T ...

Intuvie

Mechanical Engineers

Natick, MA · On-site

$104K - $108K/yr

Design, analyze, and interpret complex mechanical assemblies using AutoCAD, SolidWorks 3D/2D modeling, GD&T (Geometric dimensioning and tolerancing) practices; Coordinate product releases in ...

Orion Castings

CMM Programmer

Billerica, MA · On-site

$36 - $44/hr

Create, optimize, and troubleshoot complex CMM programs using 3D CAD models, part drawings, and geometric specifications. * Quality Documentation: Conduct detailed First Article Inspections (FAI) and ...

Orion Castings

CMM Programmer

Billerica, MA

$36 - $44/hr

Create, optimize, and troubleshoot complex CMM programs using 3D CAD models, part drawings, and geometric specifications. * Quality Documentation: Conduct detailed First Article Inspections (FAI) and ...

previous page

Showing results 41-60

All JobsGeometric Modeling JobsGeometric Modeling Jobs in Massachusetts

Geometric Modeling information

What is geometric modeling?

Geometric modeling is the mathematical and computational process of representing the shape and structure of objects in two or three dimensions. This field is widely used in computer-aided design (CAD), computer graphics, animation, engineering, and architecture. Geometric modeling involves creating and manipulating digital models using points, lines, curves, surfaces, and solids, allowing for visualization, simulation, and fabrication of real-world objects. Professionals in this area use specialized software to design complex structures, analyze their properties, and prepare them for manufacturing or virtual rendering.

What are the key skills and qualifications needed to thrive as a geometric modeling specialist?

To thrive as a Geometric Modeling Specialist, you need strong mathematical proficiency, spatial reasoning, and experience with 3D modeling, often supported by a degree in computer science, engineering, or a related field. Familiarity with CAD software, 3D modeling tools (such as Blender, Maya, or SolidWorks), and programming languages like Python or C++ is typically required. Attention to detail, creativity, and effective problem-solving skills are crucial soft skills for this role. These abilities ensure the accurate creation and manipulation of complex models essential for product design, animation, simulation, and engineering applications.

What are some typical challenges faced by geometric modeling professionals in a collaborative project environment?

Geometric modeling professionals often work as part of multidisciplinary teams, collaborating closely with engineers, designers, and software developers. A common challenge is ensuring that complex models meet both aesthetic and functional requirements, while staying compatible with different software platforms used by the team. Clear communication and iterative feedback are essential to resolve discrepancies and maintain model integrity throughout the project lifecycle. Balancing technical accuracy with project deadlines is also a frequent aspect of the role.

What is the difference between Geometric Modeling vs CAD Designer?

AspectGeometric ModelingCAD Designer
CredentialsKnowledge of geometry, CAD software, and 3D modelingSimilar, often requires CAD software proficiency and technical drawing skills
Work EnvironmentDesign studios, engineering firms, manufacturingArchitectural firms, engineering companies, manufacturing
Industry UsageUsed in product design, animation, simulationUsed in drafting, technical drawings, project planning
Search & Comparison IntentFocus on 3D modeling techniques and softwareFocus on technical drafting and design documentation

While both roles involve design and technical skills, Geometric Modeling primarily focuses on creating 3D models and shapes, often for engineering or animation purposes. CAD Designers concentrate on technical drawings and detailed plans used for manufacturing or construction. Understanding these differences helps in choosing the right career path or job search focus.

What are popular job titles related to Geometric Modeling jobs in Massachusetts?

For Geometric Modeling jobs in Massachusetts, the most frequently searched job titles are:

What job categories do people searching Geometric Modeling jobs in Massachusetts look for?

The top searched job categories for Geometric Modeling jobs in Massachusetts are:

Geometric Modeling jobs near you
Infographic showing various Geometric Modeling job openings in Massachusetts as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Associate Principal Scientist, Biologics AI

AstraZeneca

Cambridge, MA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 16 days ago

AstraZeneca rating

8.4

Company rating: 8.4 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

22nd of 86 rated pharmaceutical

Job description

We are seeking an experienced and visionary Associate Principal Scientist to lead Biologics AI innovation at AstraZeneca's US R&D centers in Waltham, MA or Gaithersburg, MD. This is a high-impact scientific leadership role accountable for defining and executing the AI strategy that integrates state-of-the-art machine learning with wet-lab discovery to accelerate biologics engineering and enable next-generation biotherapeutics. You will set technical direction, own delivery across multiple programs, and shape data generation at scale-working across computational and experimental functions and with global partners to translate AI into robust, reproducible advances in discovery.
Key Responsibilities
  • Strategic leadership and vision: Define and drive the AI strategy for biologics discovery and engineering, setting priorities and roadmaps that integrate AI and wet-lab capabilities and deliver measurable impact on pipeline goals.
  • Program ownership: Lead multiple cross-functional discovery initiatives from problem framing through deployment, ensuring rapid translation of computational insights into experimental design and decision-making.
  • Advanced ML innovation: Architect, develop, and guide application of cutting-edge models-protein language models, structure-informed and geometric methods, de novo/protein design, and multi-modal learning that fuses sequence, structure, and biological activity data-to solve high-value scientific problems.
  • AI-wet-lab integration at scale: Establish closed-loop design-build-test-learn workflows with experimental teams, formalizing feedback cycles, uncertainty quantification, and active learning to improve model reliability and throughput.
  • Data strategy and governance: Set standards for high-quality data generation, curation, and metadata; partner with wet-lab leaders to design assays and campaigns that maximize ML utility and reproducibility; influence data platform evolution in collaboration with informatics and engineering.
  • End-to-end ML lifecycle leadership: Oversee and improve processes across data pipelines, model development, validation, deployment, monitoring, and continuous improvement, including best practices for reproducibility, documentation, and scientific rigor.
  • Technical mentorship and team development: Mentor and upskill scientists across AI/ML and experimental domains; provide day-to-day technical guidance and contribute to recruitment and development of a high-performing team.
  • Stakeholder influence and communication: Communicate strategy, progress, risk, and scientific insights to senior stakeholders; influence portfolio decisions and advocate for AI-enabled approaches internally and with external partners.
  • External scientific leadership: Drive publications, patents, and external visibility; represent AstraZeneca in collaborations and at scientific venues; evaluate and integrate emerging methods and tools.

Required Qualifications
  • Education and experience: PhD in computer science, machine learning, bioinformatics, computational biology, physics, chemistry, mathematics, engineering, or a related quantitative field, with typically 8+ years of relevant post-degree experience in academia and/or industry; or a Master's with 12+ years of relevant experience.
  • Domain impact in biologics AI: Demonstrated track record applying AI/ML to proteins, antibodies, or related biologics, with clear examples of methods translated into experimental outcomes, platform capabilities, or pipeline decisions.
  • Deep technical expertise: Hands-on leadership in developing and deploying advanced ML (deep learning, generative models, structure-aware and geometric methods, sequence/structure multi-modal models) for protein sequence modeling, structure-informed prediction, de novo design, or biologics optimization.
  • Closed-loop integration: Proven success establishing iterative computational-experimental cycles (e.g., active learning, design-build-test-learn), including designing experiments to interrogate model predictions and improve data/model quality.
  • Lifecycle and systems: Experience leading the full ML lifecycle at scale-data design and preprocessing, model architecture, training/evaluation, deployment, monitoring, and maintenance-using modern ML frameworks (e.g., PyTorch, TensorFlow) and software engineering best practices.
  • Data and platforms: Experience with cloud-based ML environments and scalable data workflows; ability to specify requirements and partner with data engineering/IT to evolve production ML systems that support discovery at scale.
  • Cross-functional leadership: Strong record of influencing and delivering in matrixed, multidisciplinary environments, bridging AI scientists, computational biologists, protein engineers, and wet-lab teams across sites.
  • Scientific communication: Excellent communication skills with the ability to synthesize complex technical concepts for diverse audiences and to shape scientific and portfolio decisions.
  • Innovation and delivery: Evidence of scientific innovation and impact through publications, patents, platform creation, or deployment of AI methods that materially improved experimental or business outcomes.

Preferred Qualifications
  • Protein and antibody engineering: Experience with antibody/nanobody/protein engineering, including de novo design and multi-objective optimization for developability, stability, and functional performance.
  • Advanced methodologies: Expertise with generative models (e.g., diffusion, autoregressive LMs), geometric deep learning/graph neural networks, Bayesian optimization, uncertainty quantification, and active learning for guided experimentation.
  • Multi-modal learning: Experience integrating heterogeneous data types (sequence, structure, biophysics/biochemistry assays, high-throughput binding/functional data, bioprocess/developability metrics) into unified models.
  • Productionization and MLOps: Experience leading deployment of scientific software/ML models into production discovery workflows, including model monitoring, versioning, and compliance with governance standards.
  • Data generation strategy: Demonstrated ability to design or refine assay strategies and experimental campaigns to maximize downstream ML performance and data reuse, including metadata standards and FAIR principles.
  • People and project leadership: Prior experience leading scientists and managing complex projects or collaborations; ability to set goals, delegate effectively, and deliver against timelines.
  • External profile: Strong external scientific presence (peer-reviewed publications, patents, invited talks, open-source contributions, or community standards).

Why Join Us?
As part of AstraZeneca's dynamic US biologics R&D community, you will play a critical role in shaping the future of AI-driven biologics discovery and engineering. Collaborating across cutting-edge computational and experimental teams, you'll drive innovation that brings transformative medicines to patients around the world. You will be supported by a collaborative, inclusive, and empowering environment, with unparalleled opportunities for scientific impact and personal growth.
The annual base pay for this position ranges from $144,648.80 - $216,973.20. Our positions offer eligibility for various incentives-an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.
Date Posted
13-Aug-2026
Closing Date
28-Aug-2026
Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.

What AstraZeneca employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

AstraZeneca logo

About AstraZeneca

Sourced by ZipRecruiter

AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development and commercialization of prescription medicines for some of the world's most serious diseases. But we're more than one of the world's leading pharmaceutical companies. A place built on courage, curiosity and collaboration - we make bold decisions driven by patient outcomes. Empowered to lead at every level, free to ask questions and take smart risks that write the next chapter for our pipeline and Oncology team. Make a meaningful impact that brings real benefits to society. By applying your knowledge of data, you will help to redefine our industry and ultimately save lives. Work with experts who share a common goal: to accelerate the potential of medicines and the science of tomorrow.

Industry

Pharmaceutical product wholesalers and pharmaceutical and medicine manufacturing

Company size

10,000+ Employees

Headquarters location

Cambridge, Cambridgeshire, GB

View All AstraZeneca Jobs

Apply