N

Nokia

Makes network gear for carriers and data centers

Hardware Developer Eng Co-op/Intern

Winter 2027
CA$19.80 - CA$43.84/hr
Internship
Bachelor's
Ottawa, ON, Canada
In PersonOn-site office attendance is required.
Hardware EngineeringHardware Validation & Testing

About the job

Requirements
  • Currently pursuing a bachelor's degree, diploma, or Electronic Engineering Technology program at an accredited Canadian post-secondary institution, typically in the second or third year or equivalent, with relevant academic or co-op experience.
  • Have a solid foundation in hardware electronics concepts.
  • Have familiarity with electronic test equipment such as oscilloscopes and multimeters.
  • Have completed at least one co-op work term, a four-month paid internship, or have equivalent paid work experience.
Responsibilities
  • Work with an experienced team to develop hardware solutions for Nokia routing platforms.
  • Verify and debug circuit designs using oscilloscopes and other test equipment.
  • Test hardware under thermal and environmental conditions.
  • Complete the final assembly of new prototype units.
  • Qualify new circuit components.
  • Develop custom automation scripts in Perl, Python, and TCL to control test equipment.
Desired Qualifications
  • Have excellent written and oral communication skills.
  • Have good problem-solving skills.
  • Be able to manage tasks with minimal supervision.

About the company

N

Nokia

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Website

Nokia makes the equipment and software that mobile carriers, cloud companies, and businesses use to run networks, from cell-tower radios to fiber links between AI data centers. From 1998 to 2011 it was the world's largest mobile phone maker; it sold that business to Microsoft in 2014, and HMD Global, a separate company, makes Nokia-branded phones under license. Its network unit sells optical systems that send data over fiber, internet routers, and broadband gear, and its mobile unit sells radios and the core software that runs 5G networks. Nokia earns money from equipment and service contracts and from licensing its patents to phone makers and carmakers. It was founded in 1865 as a wood pulp mill and is based in Espoo, Finland.

Company Size

10,001+

Company Stage

IPO

Headquarters

Espoo, Finland

Founded

1865

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Simplify Jobs

Simplify's Take

What believers are saying

  • AI builders are a growth engine: AI and cloud customers ordered €2.8 billion from Nokia in the June 2026 quarter.
  • Equity has gained value: Nokia's US shares rose 60% from January to October 2026, while Swedish rival Ericsson's fell 1%.
  • The US is Nokia's top hiring hub: 211 of its 622 open roles in October 2026, versus 19 in Finland.

What critics are saying

  • Cuts are widening: in July 2026, Nokia raised restructuring charges to €800 million from €250 million, targeting Europe and China.
  • Expect office time: only 5 of Nokia's 622 open roles in October 2026 were remote, and 248 were fully on-site.
  • AI is still a small slice: AI and cloud customers made up 9% of Nokia's June 2026 quarter sales.

What makes Nokia unique

  • Nvidia owns 2.9% of Nokia: the companies jointly develop mobile-network software that runs on Nvidia computing hardware.
  • It owns a historic US research lab: Bell Labs, birthplace of the transistor and Unix, lists 10 Nobel Prizes.
  • It manufactures optical chips itself: Nokia owns the San Jose semiconductor plant acquired with US networking firm Infinera.

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Benefits

  • Hybrid Work Options
  • Professional Development Budget

Growth & Insights and Company News

Headcount

6 month growth

3%

1 year growth

5%

2 year growth

5%
PR Newswire
Oct 21st, 2026
Supermicro and Nokia bring NVIDIA AI-RAN computing to the network edge

Supermicro is working with Nokia to deploy AI radio access networks (AI-RAN), from distributed cell sites to centralised network hubs. The system pairs Nokia's RAN technology with the NVIDIA Aerial RAN Computer (ARC) platform and Supermicro's AI-optimised infrastructure. Placing accelerated compute closer to the access network lets operators run radio and AI workloads on shared infrastructure. It supports low-latency uses such as voice AI, vision AI, physical AI, and autonomous network operations, and offers a path to 6G. Supermicro's servers for NVIDIA ARC are available now. The ARC MGX 2U with dual Vera processor will follow later this year. The 2U ARC-Pro goes to customer testing later this year, with general availability in the first half of 2027.

Stork
Oct 16th, 2026
Nokia's free Jev rival has a catch.

Nokia's free Jev rival has a catch. A phone-maker's research team has entered the race to make AI decisions faster, cheaper, and more private. But the headline-grabbing promise hides a trade-off that showed up clearly when the models faced real news and satire. Nokia's answer to the AI decision boom. Generative Large Language Models (LLMs) produce text token by token, a creative but often unpredictable process. Decision models, in contrast, return structured outputs: probabilities for typed choices, yes/no answers, or numerical scores. This distinction is critical for applications demanding deterministic, parse-free judgments over freeform text generation. Nokia Applied Research addresses this need with AnyJev, an Apache 2.0 Python library. AnyJev adds a decision layer to open-weight models without requiring fine-tuning or labeled training data. It transforms a generative LLM into a "System One" decision engine, enabling rapid, structured evaluations. AnyJev directly competes with TypeSafe AI's Jev, a hosted, closed-source API. Jev processes approximately 310 tokens per call, costing $0.042 per million input tokens. AnyJev, however, runs locally on your infrastructure with zero per-call cost, leveraging your existing compute. AnyJev supports a range of popular local inference backends: * Hugging Face Transformers * vLLM * SGLang * llama.cpp This flexibility allows developers to integrate robust decision-making capabilities directly into their self-hosted model deployments, bypassing external API dependencies and their associated costs and latencies. The clever trick: read the next token, not a reply. AnyJev's core innovation lies in prompt engineering, not model training. It presents a state (the input text) and a multiple-choice question to an existing LLM, formatting options with distinct labels like "A" or "B." Instead of allowing the model to generate a natural-language answer, AnyJev directly queries the model's next-token probabilities for these specific option labels. This directly retrieves a probability distribution across the choices. This method completely bypasses the risks of malformed text or JSON parsing failures inherent in generative LLM outputs. A model cannot hallucinate or misformat a single character probability. However, a cleanly formatted probability is not inherently correct or trustworthy; it simply reflects the model's raw, uncorrected judgment. To address these inherent biases, AnyJev introduces L0 calibration. L0 tackles position bias by testing every possible ordering of options and averaging the results, ensuring no choice gains an unfair advantage from its placement. It also corrects for label bias by estimating and adjusting the model's prior preference for certain labels, regardless of content. L0 offers two prior estimation methods: batch prior, which analyzes answers across a batch of inputs, and content-free prior, which estimates bias from an input devoid of actual content. This second method is crucial for single-headline judgments, providing a more robust bias correction when batch data is unavailable. L0 significantly improves decision reliability without requiring any labeled training data. 500 headlines later, Jev still wins the big test. Nokia's AnyJev library faced a direct challenge against Jev 1.13 and a raw Qwen 8B model. The benchmark comprised 500 headlines, evenly split between satirical content from The Onion and real news, all processed on an M2 Max MacBook Pro. This test rigorously evaluated accuracy, speed, and confidence calibration. Jev achieved an 80% accuracy rate. Raw Qwen trailed significantly at 68.4%, with AnyJev L0 performing only marginally better at 68.6%. Jev also dominated in speed, completing all 500 evaluations in 51 seconds, while the local Qwen and AnyJev L0 combined took 4 minutes and 36 seconds, even with shared GPU resources. AnyJev L0's core strength emerged in bias correction, eliminating all answer flips when option order changed. Raw Qwen, in contrast, flipped answers on 20 headlines (4%), while Jev saw 18 minor shifts. For further technical details, explore the nokia-applied-research/AnyJev: Turn any LLM into a Jev-style decision model GitHub repository. Confidence metrics revealed a critical flaw in raw Qwen. While 87% of its answers boasted >=99% confidence, it was incorrect on 30% of those highly confident predictions. AnyJev L0 mitigated positional and label biases but did not improve calibration in this specific comparison, leaving Jev as the superior choice for calibrated confidence. Enjoying this? Get one like it in your inbox each morning. one email a day · unsubscribe in two clicks · no third-party tracking Bias correction can fix one test - and break another. Headline trials revealed AnyJev's catch. Qwen, with its strong bias toward satire, often misclassified real news that sounded like The Onion. AnyJev's default L0 calibration, using batch prior, barely corrected Qwen's extreme 0% or 100% probabilities. Applying content-free prior to L0, however, showed a different outcome. This stronger correction helped Qwen recognize real stories that sounded satirical, reducing its lean from 69% satire calls (on a 50/50 split) to a more balanced 67% satire and 66% real news. But this also misclassified some genuine Onion headlines. Correcting a model's label lean helps when that lean is harmful, but can overshoot when the mix of examples changes. The method does not make the underlying model smarter; it merely adjusts its output probabilities. AnyJev is compelling for private, self-hosted decisions and low API costs due to its Apache 2.0 license and local execution. Yet, benchmark AnyJev on your own representative labeled examples before production. The video's results - 80% accuracy for Jev versus 68.6% for AnyJev's L0 - are specific to one model, task, and test setup, not a universal verdict. Frequently asked questions. What is AnyJev? AnyJev is an open-source Python library from Nokia Applied Research that adds decision-style outputs to open-weight language models. How does AnyJev make a decision? It checks the model's next-token probabilities for answer labels such as A or B, then returns probabilities instead of generated text. Did AnyJev outperform Jev in the video's benchmark? No. On 500 headlines, Jev scored 80% accuracy, compared with 68.6% for Qwen with AnyJev's default L0 correction. Is AnyJev free to use? The library is free under the Apache 2.0 license. Running it locally still requires compute resources and a compatible open-weight model. Found this useful? Share it. For builders Want Stork to write one of these about your product? Send Stork a URL. Stork use the product, form a view, and publish what Stork actually think - in 8 languages, labeled Sponsored, with no copy approval on your side. That last part is what makes it worth quoting.

IT Business Today
Oct 16th, 2026
ITBusinessToday weekly News roundup: NVIDIA, panasonic EW, nokia, meltwater & more.

ITBusinessToday weekly News roundup: NVIDIA, panasonic EW, nokia, meltwater & more. Last updated: October 16, 2026 12:41 pm In Tech news this week... NVIDIA has announced a five-year commitment to AI safety in the United Kingdom, highlighting the growing importance of responsible AI development as advanced models and computing infrastructure expand. The commitment reflects ongoing efforts to support AI safety research and strengthen collaboration around the development and deployment of AI technologies. In industrial tech news this week... Panasonic Electric Works and LYNAF are working together to expand smart-lock adoption. The collaboration highlights the growing role of connected access technologies in buildings and facilities, where digital security and convenient access management are becoming increasingly important. In FinTech news this week... Nokia plans to sell its enterprise campus edge business to Aurelius, marking a strategic change in its enterprise connectivity portfolio. The transaction reflects continued restructuring in the technology sector as companies reassess business priorities and ownership of specialized network assets. In MarTech news this week... Meltwater has partnered with Hitachi Solutions East Japan to strengthen market intelligence and corporate communications capabilities. The collaboration brings together media intelligence and technology expertise, reflecting businesses' growing need to monitor public conversations, understand market trends, and make informed communication decisions. In health tech news this week... Zealand Pharma and Generate have partnered to develop AI-designed treatments for metabolic diseases. The collaboration highlights the growing role of generative AI and computational biology in pharmaceutical research, where advanced models are being used to explore potential therapeutic candidates. In GovTech news this week... The BtoB platform INVOICE has partnered with AMAS, highlighting efforts to strengthen digital business processes and transaction management. The collaboration reflects the broader shift toward digital platforms that help organizations streamline administrative workflows and improve business-to-business operations. In HR Tech news this week... KITZ has launched a globally unified human resources platform using SAP SuccessFactors. The initiative is designed to standardize HR processes and employee information management across international operations, supporting a more consistent approach to workforce administration. In IoT news this week... LINE WORKS has begun integration with the BizStack IoT platform, connecting workplace communication with industrial IoT capabilities. The integration highlights the potential to bring operational data and communication tools closer together, helping businesses improve information sharing across field and office teams. Article of the week. AI is becoming an increasingly important part of technology development, from safety research and drug discovery to connected infrastructure and enterprise operations. NVIDIA's five-year UK AI safety commitment reflects the growing focus on responsible AI development, while developments across smart locks, digital communications, and global HR platforms show how organizations are adapting their technologies to changing business needs. Stay tuned for more such updates! ITBT Staffwriter is an in-house contributor at IT Business Today, responsible for delivering timely News and QuickBytes. The author covers the latest developments across IT, tech, and enterprise solutions to keep readers informed with concise and impactful updates. October 16, 2026

iTWire
Oct 16th, 2026
Nokia launches flexible 50G/25G/10GPON solution to accelerate AI-ready fiber broadband.

Nokia launches flexible 50G/25G/10GPON solution to accelerate AI-ready fiber broadband. * Nokia is the first vendor with a 16-port line card solution that supports GPON, 10G, 25G and 50G PON on every port, enabling operators to evolve their networks with greater flexibility and lower risk. * Built on Nokia's Quillion chipset and multi-PON optical modules, the high-density line card unlocks mass-market deployment of fiber access speeds up to 50Gbps. * Nokia's industry-first multi-PON solution enables instant, high-performance, quantum-safe connectivity to meet the demands of the AI era, with general availability planned for 2027. Nokia recently announced a new 50G/25G/10G/GPON line card that gives operators the flexibility to evolve their fiber network at their own pace and be ready for the demands of the AI era. As AI transforms how consumers, businesses and industries use networks, richer content, greater cloud interaction and growing upstream traffic are increasing requirements for capacity, low latency, security and reliability. The solution enables operators to adapt their existing fiber networks as these demands grow. Earlier line cards could limit operators to a narrower set of PON technologies, requiring them to commit capacity in advance or add hardware as market needs changed. Nokia's new multi-PON line card supports multiple generations on every port, allowing operators to introduce higher speeds when and where demand develops. Built on Nokia's in-house Quillion chip and optical PON modules, the new solution supports GPON, 10G, 25G and 50G PON on every port, together with a variety of multi-PON optical modules. This now allows operators to deploy the technology - or combination of technologies - in a flexible way that's best suited to each market, service or business case. Operators can add or change PON generations on the same port and fiber as demand evolves. "The future of broadband is becoming harder to predict. AI is reshaping traffic patterns and creating new expectations for bandwidth, latency, security and reliability. At the same time, operators must make investments in next generation PON that will serve them for years to come. With our new line card, operators do not have to bet everything on a single path. They can deploy the PON technology their market needs now; add additional technologies on the same port and same fiber as the need arises; and easily mix and match PON generations to capture multiple revenue streams," said Maryam Chaudry, SVP & GM, Fixed Networks, Nokia. "As operators prepare their networks for AI-driven applications and higher-speed broadband services, flexibility is becoming just as important as performance. Nokia's ability to support multiple PON generations on every port gives operators a practical, lower-risk path to evolve their fiber networks as demand develops," said Jeff Heynen, vice president, Broadband Access and Home Networking at Dell'Oro Group Powered by Nokia's Quillion chipset and multi-PON optical modules, the line card provides a flexible evolution path from GPON/10G to 25G and 50G PON. For example, operators can deploy 10G + 25G PON today and add 50G PON when demand accelerates. Alternatively, those planning for 10G + 50G PON can easily add 25G PON if market needs develop differently. The solution also enhances network performance, security and resilience: * Up to 4x lower upstream latency for more responsive, data-intensive applications. * Quantum-safe encryption and authentication to safeguard network and user data against emerging threats. * End-to-end resilience without a single point of failure for dependable delivery of mission-critical connectivity services.

APAC News Network
Oct 15th, 2026
Nokia elevates Girish Mehta to Director and Head of NPO.

Nokia elevates Girish Mehta to Director and Head of NPO. Summarize with: New Delhi, Oct 15(APAC Media): Nokia has elevated Girish Mehta to Director and Head of Network Planning and Optimisation (NPO). Mehta has previously worked with Bharti Airtel Foundation and Ericsson India. His experience spans telecommunications and network technology, with his new role focused on network planning and optimisation. India parcel delivery In his new position, Mehta will focus on improving network planning, optimisation and operational efficiency. These functions help telecom operators manage network capacity, improve coverage and maintain service performance as demand for mobile connectivity grows. Network planning helps operators identify capacity requirements and expand coverage, while optimisation focuses on improving the performance of existing networks. Both areas are important for telecom companies seeking to deliver reliable connectivity and manage network resources efficiently. Mehta's experience at Ericsson India adds to his background in telecommunications and network technology. He has also worked with Bharti Airtel Foundation before taking up the leadership position at Nokia. Summarize with: