Software Engineer jobs, by country

Where software engineer hiring actually concentrates across five markets, which boards are worth your time in each, and the thing that decides more applications than any of it — whether you can legally take the job.

Jump to: India · the United States · the UK · Canada · Australia

Check work authorisation before you count vacancies

This is the most expensive mistake in an international search. A market with 400 roles you can legally take is better than one with 4,000 that need sponsorship you do not have, and no amount of tailoring changes it — visa status is a knockout question, answered before a human reads anything. Filter on it first, then look at volume. Paste any posting into TrueFit and it surfaces the knockouts (authorisation, years, degree, location) rather than a keyword score, so you find out in seconds instead of after a fortnight of silence.

Apply where the job actually lives

Aggregators are for discovery, not for applying. Postings on them go stale, get reposted by agencies, and route you through an extra layer that loses your application. When you find something, go to the company's own careers page and apply there — AutoApply fills the company's form and stops at submit, so this costs you nothing in speed. The boards listed under each country below are the ones genuinely worth checking in that market, alongside LinkedIn everywhere.

The five markets

The part that travels

Whichever market you are in, the sequence is the same and it is not "apply to more". Score the posting before spending time on it, reorder your resume so the relevant work sits where it gets read — see the software engineer resume examples — apply on the company's own page, then rehearse the interview out loud rather than in your head. Twenty applications done that way beat several hundred sprayed, and take less time.

JobStraight aggregates public listings and links to local boards; it is not the hiring employer. Every tool is free with no signup.

Coding interviews exist in most markets; what changes is what they actually test

In US big tech, a coding interview is a LeetCode-style problem — you solve an algorithmic challenge on a whiteboard or shared doc in 45 minutes, discussing your approach with an engineer. The problem is usually medium-hard: two-pointer traversals, dynamic programming, graph traversals, binary search variations. Big tech screens for algorithm fluency because it correlates with the type of thinking they value, not necessarily because you will solve these problems on the job. The interview is consistent across Meta, Google, Amazon, Microsoft — preparation is standardized, which makes preparation itself a commodity. Canadian and Australian big tech follows the same model. UK hiring is slightly different in emphasis: coding interviews still exist but are often paired with a system design component much earlier in the interview cycle, even for mid-level engineers. European hiring (German, French) still involves coding but tends toward more open-ended problems or problems grounded in realistic scenarios. A German tech company might ask you to solve a data transformation problem relevant to their stack rather than an abstract algorithmic puzzle.

Indian IT services companies (TCS, Infosys, HCL) also conduct coding interviews but the level is consistently easier — LeetCode Easy to Medium-Hard, not Medium to Hard. The interview focuses on coding style, ability to explain your work, and comfort with the language. Algorithms matter but are secondary to demonstrating you can write clean, maintainable code. Indian product companies (Razorpay, Swiggy, Myntra) use harder algorithmic interviews closer to US big tech, but the interview cycle is often shorter — you might face only one coding round plus system design and behavioural rounds, rather than the three-plus technical rounds common at US FAANG. Startups globally tend to focus more on problem-solving and less on memorized algorithms — the interviewer wants to see how you think through a messy problem, not how quickly you recognize a two-pointer pattern.

The gap between what coding interviews test and what real work demands is wider in some markets than others. In a US startup, you will probably not solve LeetCode problems after day one. In a European fintech or automotive company, you might solve data-heavy algorithmic problems regularly. In Indian services, your daily work is much closer to the interview — transformation code, API building, debugging, with less emphasis on algorithms. Knowing this distinction helps you prepare more effectively. If you are interviewing for a US startup, the coding interview is a screening gate; study algorithms hard for the interview, then forget them after you start. If you are interviewing for a financial services company, invest in understanding algorithmic thinking because it will shape your work.

A software engineer CV needs different depth depending on where you are applying

In the US, engineering CVs are concise — one page for junior, two pages for senior. Employers scan for: title progression, company size/reputation, and a bulleted list of shipped projects with technologies used. A US hiring manager does not care about your degree in detail (unless you are a bootcamp grad); they care about proven shipping. You list languages and frameworks you know, shipping velocity (how many projects you have shipped), and team size you have worked in. The CV is formatted to be scannable — bold the companies you worked for, list technologies inline, use short bulleted descriptions. UK hiring follows the same format, though slightly more text per bullet is accepted. Canadian and Australian hiring is identical to the US model.

European CVs are longer — two to three pages — and emphasize your educational background and certifications much more heavily. A German hiring manager reviewing an engineering CV looks for: degree type and university ranking, certifications (AWS, Kubernetes, specific cloud platforms), years of experience in specific technologies, and detailed descriptions of project scope and methodologies used. A CV claiming 'built REST API in Python' reads as thin; a European CV would say 'designed and implemented a scalable REST API using Python 3.9, FastAPI, PostgreSQL, and deployed to AWS ECS using Terraform, resulting in 40% improvement in query latency and supporting 50k daily active users.' The same accomplishment, but described with technical depth to demonstrate rigorous thinking.

Indian CVs vary by company type. Services company CVs read like extended European CVs — very detailed, heavy on certifications and educational credentials, with precise technology names. Product company CVs trend toward the US model — concise, focused on shipped products and business impact. However, even Indian product companies often weight educational credentials (degree type, university, graduation year) more heavily than US companies. An Indian CV without detailed education looks incomplete; a US CV that leads with education looks dated. Your CV strategy depends on where you are applying: if targeting India or Europe, expand the depth and formalize the educational section. If targeting the US, tighten to one to two pages and lead with shipped work. LinkedIn provides one voice; your downloadable CV should adapt.

The first role you land shapes what opportunities come after

If you land a junior backend role at a US startup, the next role you are hired for is probably another backend role at a better-known startup or a big tech company. Your CV signals 'startup engineer', which opens startup and fast-growth hiring channels to you. If you land a junior developer role at TCS, the next two or three roles you are offered will likely also be at services companies — your CV signals 'services engineer', and product company hiring managers will perceive you as inexperienced with product thinking, even if you are technically skilled. This is unfair but consistent. Services experience is harder to translate to product company hiring; product experience is easier to translate to services (but why would you?). Understanding this market structure before you take your first role is critical. If you eventually want to work for a US startup or FAANG, your first role should be at a startup, a series-funded company, or a product company. If you take your first role in services, your resume carries that signal for years.

The technologies you work with also lock in future opportunities. If your first role uses Python and JavaScript, those are your signal for the next five years. Hiring managers believe resume signals — if you have shipped Python, you are now a Python engineer. Switching from Python to Go or Rust is harder than staying on the language you started with; you have to overcome both the signal mismatch and the perception that you lack depth. This is why the first role's tech stack matters. Working with modern, widely-adopted technologies (Python, Go, TypeScript, Kotlin, Rust) leaves more doors open than working with niche or aging languages. This is not entirely fair — a brilliant engineer writes good code in any language — but hiring signals and resume patterns drive opportunity, not just capability.

Geographic signals also matter. A resume showing only India-based companies will trigger faster callbacks in India and slower ones in the US or Europe. A resume showing only US companies will be seen as expensive to hire in India or as culturally mismatched in Europe. If you plan to work internationally, your strategy should include building a multi-geographic work history early — two to three years in one market, then a move to another — so your CV signals 'cross-market engineer' rather than being locked into one region's hiring patterns. If you plan to stay in one region, that lock-in is less of a problem. Understanding this before your first role — or before your second move — shapes your entire career trajectory.

Frequently asked questions