Any SE folk ever transition out of the field into a different career leverage their SE experience into it?

Hi All,

After having spent ~10Y working on SmarTeam, ENOVIA and 3DExperience Platform. And recently using Azure Devops and Salesforce. Seeing all the complexity, engineering that went into it, I was itching to create something similar of my own. Now that we have LLM assisted coding, big projects should be doable by a single person, right? This is my attempt (v0) doing the same.

1.9B Tokens and 28 Dollares Later, I have a working demo.

Since this was a big endeavour, I wanted to have structured knowledge base. Discussion with various LLMs suggested (OpenSpec)[https://openspec.dev/] is a good way to store this info and make changes to it. LLMs also suggested that I track workitems using (beads)[https://github.com/gastownhall/beads]

I initially modeled the RFLP (Requirements, Functional, Logical, Physical) framework from my early days of working on 3Dx. But I didn't have much sample data to seed. 3 days ago I had an Idea, what if I modeled the beads, openspec and git commits into it? it worked. I have seeded the demo with its own development history.

Looking to hear your thoughts and questions. Cheers!!

We're planning to outsource some CAD work, but a lot of it involves proprietary designs and customer-specific parts. Before we send anything over, I'd like to make sure we're choosing the right company.

Besides signing an NDA, what do you usually look for? Certifications? Past projects? Security policies? Curious what your checklist looks like before trusting someone with sensitive engineering data.

Been in SE about 10 years, defense, medical devices, enterprise tech. TS clearance, MBSE, CAMEO, IBM DOORS, the whole stack. Also have PM and product experience layered on top which is either a flex or the reason I feel like I don’t fully belong anywhere lol.

Here’s my situation. I’m a grey badge contractor which means I have zero org chart authority over literally anyone. Everything I get done runs through relationships, trust, and somehow convincing rooms full of people who don’t want to agree on anything to actually move in the same direction. Chaired 20+ TRBs across military, federal civilians, and subcontractors in classified environments. Got a corporate award at one gig and four consecutive innovation awards in 12 months at my current one. None of it came from authority, just from being a people person in a field that is not exactly known for those. I got into engineering because I wanted to actually solve hard problems, not facilitate other people solving them while I manage the paperwork trail. The pay keeps me here but it’s not enough anymore.

Also noticing the SE and SWE lines are blurring hard. Digital engineering, data pipelines, cloud stuff showing up in every SE job description now. I’ve been building AI tools on the side which scratches the itch more than my actual job does if I’m being honest.

What skills outside of MBSE are actually becoming non-negotiable right now? Cloud, digital twins, simulation environments, data pipelines? Where should I be putting time?

Has anyone gotten out of SE entirely and used those same skills somewhere that actually felt stimulating? Not just a different SE job, actually out. What did that look like?

Is defense SE uniquely soul crushing or is this everywhere? Medical device SE was at least tangible even if I spent half my time hand holding consultants who should have known better. Defense governance work just feels like requirements shuffling with extra classification markings.

I am just trying to figure out if there’s a version of this career that doesn’t feel like I’m slowly dying inside.

Any thoughts on doing MIT SDM in 1 year if anyone’s done it?
The program I’m in requires me to complete my masters in 1 year hence the compressed schedule. I understand it’s usually 1.5 years to complete.
My undergrad was in engineering.

I'm part of a team developing a renewable energy site screening tool. I decided to run it as a proper MBSE project, but without Cameo or Rhapsody or any SysML tooling. Everything is YAML and markdown in git, plus a validator script that checks the model for consistency. It's worked better than I expected so I'm writing up the structure.

What the system does

UK renewable site screening. A land agent or developer normally has to manually cross-reference DNO substation capacity, planning constraint layers, resource data and market context to judge whether a site is viable. This tool takes a site (coordinate or polygon), a technology type and an area, and produces a PDF desktop-study report plus an interactive map in about 100 seconds: grid connection viability, planning constraints, EOS P50/P90 yield data, and a scored risk verdict.

It's 25 Python modules covering spatial queries (substations, planning constraints, cable routing), resource and yield modelling (ERA5 wind, solar irradiance, P50/P90), risk scoring and decision logic, report and map generation, and orchestration. So a real system with real complexity, not a toy example.

Current state of the model: 170 requirements, 162 test cases, 139 decision records, 354 files total.

Structure

Numbered layers, one concern per layer. One artifact per file: each requirement is its own YAML file, each test case is its own YAML file. This turned out to be the most important decision. Diffs are readable, merges rarely conflict, and I can grep the whole model. I've watched traceability rot inside monolithic requirement documents before and didn't want to repeat that.

Every file has a typed ID and IDs are how everything links: RSI-SYS-402 (requirement), RSI-TC-165 (test case), D-R140 (decision), RSI-R008 (risk), RSI-ICD-004 (interface).

Links go both ways. Requirements have verified_by: [RSI-TC-165], test cases have verifies: [RSI-SYS-1652]. Yes it's redundant. The redundancy is the point: a machine can only detect a broken link if both ends are supposed to agree.

Code comments cite requirement IDs. Requirements cite the decision records that justify them.

Two verification layers

The 162 MBSE test cases are the specification of what must be true: preconditions, procedure, pass criteria. Technology agnostic, reviewable by a human.

Separately there are 625 pytest tests across 50 files, runs in about a minute. That's the executable side.

I keep them separate on purpose. The spec layer defines what verification means, so it survives refactors of the test code.

The validator

validate.py, 401 lines, only dependency is PyYAML. Exits 0 or 1 so CI gates on it. It checks:

• Schema: required fields, enum values, ID format matches filename, date formats, duplicate IDs
• Reciprocity: if RSI-SYS-901 lists RSI-TC-001 under verified_by, RSI-TC-001 must list it back under verifies
• Rollups: every hand maintained summary number gets recomputed from the files and compared
• Citation integrity: every D-Rxxx cited anywhere must resolve to a real decision entry

LLMs take to this like fish to water

This wasn't the original motivation but for me it is highly effective, The whole model is plain text with typed IDs and explicit links, which seems to be a shape an LLM works well with. No binary model files, no proprietary export step, no screenshots of diagrams. An agent can grep for RSI-SYS-402, read the requirement, follow verified_by to the test case, follow the decision citation, and have full context in a few file reads.

I've been able to hot swap between Codex and Claude sessions in the same project with very little context lost. Neither needs project-specific onboarding beyond pointing it at the repo, because the structure is self-describing. Both can also run validate.py after making changes. If an agent hallucinates a decision citation or breaks a reciprocal link, the issue gets flagged when the validator runs.

I went to a community college 10 years ago, got an associate degree, then transferred to a 4yr and flunked out. About 4 years ago, I went back to another CC, got another associate to reboot. First was in math, second was in CS. I had planned on becoming a software developer, but it became pretty apparent that entry level software engineering is probably screwed at least for the foreseeable future.

I got into another 4yr and had planned on pivoting into computer engineering, as I do have an interest in computer hardware and I didn't think I would be able to get as many of the prereqs needed for EE. Then I planned on pursuing s systems engineering masters, as I've done some research, and systems aligns with stuff I'm interested in.

However, turns out that's probably not happening either. I'm dependent on financial aid, and federal regulations require that in order to still be eligible for aid, I must complete my degree within 180 credits. My total number of credits transferring in is 104 from all the courses I took over the years, and that only leaves me 76 credits until my aid would run out. Which ultimately means I cannot take an engineering undergrad, which I gather is the traditional path into an SE masters.

As it stands currently, I will have to either do a math/cs double major, or major in one and minor in the other. Unfortunately, my credits are too invested in those subjects at this point to really pivot to anything else. I can hopefully take on some electives in SE, but that's about it. Then I could pursue a masters in systems engineering.

What I wanted to know is what will my potential job prospects be? If I have a math/cs -> systems engineering pathway and then try to enter the job market without having been an engineer beforehand, will it be incredibly hard? I find the idea of systems engineering pretty interesting (I think it was kind of was what I was looking for the whole time maybe), but ultimately if I sink the next 3-4 years of my life into this, I do want it to be worth something. For context as well, I just turned 40. So at this point I maybe have 25-30 years before it's time to retire.

Realistically, what are my chances/prospects of working in the systems engineering field with all this in mind?

Do engineering teams actually struggle with keeping requirements, design docs, spreadsheets, CAD/test info, decisions, and changes synchronised across a project? What do you think if there were a tool that connects all these sources into a live engineering graph/ digital thread so that when something changes teams can see the affected requirements, components, parameters, interfaces etc. Does that sound genuinely useful in real work flows or more like a nice to have? Just trying to understand if this is a real pain and the idea of this tool would solve anything worthwhile.

Thanks!

I have an over 15 year career mainly in the DoD world with a business degree. In the last 6 ish years I’ve drifted more into the SE world and nearly a year ago it was made official and my title was changed. I’ve gotten more into requirement development, analysis, decomposition, and I’ve been tasked as the lead architect for our team using Cameo.

I’ve (mostly) gotten over the imposter syndrome and have proven to myself that I can hold my own in meetings/discussions with other “real” engineers. The company I work for is enormous and if I want to take other SE roles in the company I feel I really need to beef up my skills, especially modeling/Cameo (open to UML as well). The company offers training which has been hit or miss in terms of quality.

Forgive me for such a broad question but is there a good starting point to get going? Advice? Youtube channel? I saw MagicGrid mentioned here before so I’ll look into that.

I also wouldn’t mind ultimately getting out of this industry so anything that would be more generally applied than just the defense/aerospace industry would be great.

Edit: Added an image

This is one of those problems I've been annoyed at for years and kept building hacky workarounds for. Klaiv is me trying to fix it properly instead.

The pain, if you've felt it:

• The architecture tool doesn't play with git. The model is a binary or XMI blob you can't really diff, review, or merge.
• So the software people never touch it. They draw their own boxes somewhere else, and now there are two sources of truth drifting apart.
• By audit time the architecture no longer matches the system, and someone burns a week rebuilding it to pass.

Klaiv keeps the SysML/UML model as plain YAML in the repo. The model gets diffed and reviewed in normal pull requests, and a GitHub action renders before/after diagram images right on the PR. Diagram layout is split into its own files, so moving boxes around doesn't pollute the diff that matters. That layout is still committed and reviewed when you need it, just kept out of the way. It does both UML and SysML, and you can add your own element and diagram types.

It's early. Pre-1.0, a VS Code extension for now, and a few things (a CI validation gate, architecture-to-code traceability) are still in progress.

If you want to look without installing anything: I modeled a good chunk of Autoware (the open-source AV stack) as a real, non-toy example, and every diagram was built in its own PR, so you can click through the pull requests and see the rendered diagrams.

• Autoware model, browse the diagrams straight from the PRs: https://github.com/klaiv-dev/autoware
• Install from the VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=Klaiv.klaiv-vscode
• More detail: https://github.com/klaiv-dev/klaiv-docs

Genuinely after feedback, not upvotes. Does this resonate, or am I solving a problem you don't have? And if you've made "MBSE in git" work some other way, I'd like to hear how. Thanks

Hi r/systems_engineering,

We are back with Lesson 11 of our technical deep dive into the new standard.

In our previous lesson, we analyzed how interface def acts as a connection blueprint that can natively hold its own physical properties. Today, we are shifting our focus to how we actually use those blueprints to wire up a concrete system topology.

1. Moving Beyond Visual-Only Topologies

When analyzing a system architecture, managing and reviewing its connectivity purely through tracing lines on an Internal Block Diagram can make topology difficult to search, version-control, or programmatically validate.

SysML v2 addresses this by making system topology explicit within the model text itself. A connection can be declared natively, allowing the architecture to be read, searched, and inspected directly without depending solely on a graphical diagram.

2. Navigating Nested Endpoints: Feature Chains

To establish a connection textually when ports are nested deep inside part definitions, SysML v2 utilizes dot notation—formally known as a feature chain.

For example, if you have a Charger part usage containing a plug port, you reference that specific endpoint using: Charger.plug

This allows you to explicitly state exactly which internal interfaces are hooking up to one another from a local system context block.

3. Connect Usages vs. Interface Usages

Depending on how detailed your system model needs to be, you have two primary ways to declare connectivity in the text:

• The Simple connect Shorthand: Useful when all you need to assert is basic structural connectivity between parts or items.
• Typed Interface Usages: When a connection strictly links ports together and needs to carry engineering data, you use an interface usage typed by an interface def.

Example:

Code snippet

interface usage charging_session: ChargingCable {
    end charger_side = Charger.plug;
    end vehicle_side = MyCar.socket;
}

(Note: There is also a compact, one-line shorthand form available when the end mappings are obvious and don't need explicit block declarations).

Because this interface usage points directly to your reusable connection definition, it inherits all specified semantic constraints and attributes (such as cable resistance or data latency) directly within that local context topology.

4. Semantic Validation Benefits

Because connections are explicitly declared and typed by an interface definition in the text, compliant tools have the semantic information necessary to automatically validate your model. The tool can check if the referencing ends actually conform to the expected port types, natively flagging errors if someone accidentally tries to connect incompatible ports in a design loop.

This paper introduces a framework which works on constraining the AI slop problem, it works by creating a requirement to provide a piece of Material trails as provenance, which is naturally collected by any content creator with or without assistance of AI.

Hello all, this is part 2 of my original post 2 days ago. I want to thank this community for all of the feedback you all original gave me. I think this resume is much better based on everything that was said and I am wondering, because i am changing fields from a Higher Ed professional, specifically in housing. I am currently an Assistant Director in charge of operations and personnel in my department but I am currently getting my masters in Systems, would you hire me with this resume?

Side note, I keep trying to post this in eng resume but the bot keeps pulling the post because of formatting/flair issues but when I figure that out I'll only bring system's engineering questions here.

Hi everyone,

I'm a junior software engineer looking to transition into a Forward Deployed Engineer (FDE) role. I'm not looking for interview prep, but for detailed blogs, case studies, or write-ups that explain how FDEs discover customer problems, design solutions, and deploy them in real client environments.

If you know of any personal blogs, engineering articles, newsletters, or books that cover real-world FDE work, I'd really appreciate your recommendations.

Thanks!

Hi,

I work as a Systems Engineer in the automotive domain, where my role is focused on requirements engineering. The MBSE team consumes our requirements and performs the modeling. I'm familiar with their process and have been part of those discussions, but I haven't done the hands-on modeling myself.

I'd like to gain hands on MBSE experience (sequence diagrams, state machine diagrams, etc...) to expand my skill set. Unfortunately, my company doesn't provide tool licenses for me to practice with.

I'm looking for suggestions to self-learn these skills, and open source tools where I can practice and verify the diagrams on my own.

Hello all, I am changing fields from a Higher Ed professional, specifically in housing. I am currently an Assistant Director in charge of operations and personnel in my department but I am currently getting my masters in Systems Engineering from Cornell. I have been applying for Systems engineering roles now but I wanted to see your opinions based on you all experience. Basically, is this resume a good showcase of what I have learned and would you hire me?

It's a call for help. My company is a product based company, making products for stakeholders ranging from aerospace to automobile to even some medical sectors. Providing engineering solutions.Now , as the business is expending we are exploring MBSE way for working and working with so many standards and certifications needed, we believe MBSE can solve our problem. So far we were able to produce physical architecture using Capella (open source) however, we are struggling to find a way to perform Safety analysis (FHA, PSSA, FTA, CCA) as well with the model.Capella offers some extensions but they are commercial and we aren't sure if that will be worth investing or not? Do any of you within the community have an answer to my problem? We have gone via Python4Capella scripts as well, but then it's becoming difficult to handle a large number of data. Suggest us a tool that can solve our problem. Soon we will be looking for the PLE aspect of MBSE as well. Please help.

Thanks

Currently working in a product/service based company for various aerospace projects. We are now planning to adopt MBSE that can streamline our process to certification. Till now we were using a document centric approach but now, as the business is expanding MBSE is the right solution. The main goal is to integrate the Safety Assessment Process within the models. So far we've been using Capella for physical architecture and sheets for requirement management and have also been able to extract models using python4capella plug in, but the process is tedious. As we were experimenting with capella we got to know the limitation it comes with. The extensions needed for our task are commercial and we aren't sure if they'll help us or not? We are also exploring product line engineering, but how to find the right tool to do all of the work ? Maybe in the future the application will change, we would like to have a solid foundation so that any new product or new project can be solved with this framework.

Help us with the right strategy for these, we are new to this domain.

Thanks!

I've been reviewing a lot of technical interview prep material lately alongside some deep profiling on our production backend services. It made me realize how massive the gap has become between standard academic data structures and what it actually takes to scale a modern application. Spending months memorizing how to balance a red-black tree or traverse a graph using depth-first search is fine for passing an interview screen, but it rarely translates to solving real-world infrastructure bottlenecks.

In production engineering, standard DSA is no longer sufficient. The industry has quietly shifted toward a completely different set of non-standard, hardware-conscious data structures that you rarely see on LeetCode.

When you look under the hood of databases like RocksDB or messaging queues like Kafka, they aren't using traditional arrays and binary trees to handle high throughput. Instead, they rely on non-standard structures like Log-Structured Merge-trees for fast disk writes, Ring Buffers for lock-free memory sharing between concurrent threads, and Bloom Filters to prevent expensive, unnecessary database reads entirely.

Even simple concepts like utilizing Bitmaps for ultra-fast, in-memory flag checks are incredibly high-value in modern architectures but are completely ignored in standard algorithmic training.

The reality is that modern engineering is bottlenecked by physical hardware, CPU caches, and network I/O, not abstract Big-O notation. If we want to build highly optimized backend systems, we need to stop treating DSA like an interview game and start studying the non-standard structures that production-grade infrastructure actually relies on to survive under load. It would be great to see technical evaluations pivot away from academic puzzles and move closer to these practical systems concepts.

First year electronics and systems student i have one project from one internship already but i wanna make surevim on the right path