Senior Hardware Modelling Engineer

RTX Technologies
Harlow
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Electronics Engineer

Senior Electronics Engineer

Senior MLOps Engineer

Software Engineer

Machine Learning Performance Engineer, London

Machine Learning & Computer Vision Engineer

Date Posted:

2024-09-05

Country:

United Kingdom

Location:

GBR01:Harlow, Essex, Kao One, Kao Park, Harlow, CM17 9NA

Role: Senior Hardware Modelling Engineer

Raytheon UK is looking for an experienced and high performing Engineer to join a successful Assured Position, Navigation and Timing (APNT) team within the Global Sensors Mission Area, creating Digital GPS Anti-Jam Systems for the military market. The role will be based at Raytheon’s Harlow facility and the successful candidate will report directly to the APNT Digital Hardware Engineering Lead.

The Senior Hardware Modelling Engineer will be able to make their mark on a broad and varied range of product designs and will be expected to assist in the integration of these designs into products, being the focal point for the complete system.

Main Duties:

The successful applicant will support the APNT Team across various programmes (both internally and Customer funded) and will involve the following main duties:

  • Develop requirements to ensure compliance to the relevant standards.
  • Work alongside our system engineers/modellers on architectures of electronic subsystems.
  • Create hardware models and run the appropriate simulation.
  • Able to read/create Schematic creation, support PCB design, component selection, evaluation and verification.
  • Work alongside our embedded software engineers and mechanical engineers.
  • Generate supporting design documentation and perform regular technical reviews.

Candidate Requirements:Essential:

  • Experience with embedded digital electronics and microprocessor systems.
  • Experience of working with ADC/DAC.
  • Experience of working with memory devices such as SRAM, Flash.
  • Experience working with FPGAs and SoC technologies.
  • Model based design creation and simulation.
  • Understanding of IBIS, SPICE, etc models.
  • Experience with Matlab/Simulink, SPICE toolsets.
  • Experience of creating prototypes and turning them into a production hardware.
  • Experience of supporting PCB designs in conformance with defence engineering standards/airborne standards.
  • Experience with High-Speed Interfaces and signal technologies such as LVDS, JESD.
  • Experience in designing for manufacture.
  • Able to present technical information clearly and concisely to others.
  • Demonstrable analytical and problem-solving skills.
  • An openness to learn and try new techniques.
  • Ability to adapt to a dynamic work environment - working independently or with others to overcome obstacles.
  • Capable of analysing and reporting on technical data.
  • Eligible or holder of current SC security clearance.

Desirable:

  • Experience of working with the Xilinx chip family.
  • Familiarity with the application of a systems engineering approach across the design and development life cycle.
  • Experience using the Mentor Xpedition PCB design tool and SPICE.
  • Experience creating DO254 compliant electronic systems.
  • Understanding of VITA interface.
  • APNT systems domain knowledge and/or experience in a similar field.
  • Understanding of environmental requirements (shock, vibration, EMC, etc.), experience applying these requirements to a design and experience testing for these requirements.

About Raytheon Technologies:

Raytheon Technologies Corporation is an aerospace and defence company that provides advanced systems and services for commercial, military and government customers worldwide. It comprises four industry-leading businesses – Collins Aerospace Systems

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.