M

Mistral AI

Open-weight LLMs and enterprise AI deployment

Backend Software Engineer - Backend

Full-Time
No salary listed
Mid, Senior
Bachelor's
New York, NY, USA
Hybrid
Software EngineeringBackend Engineering

About the job

Requirements
  • A degree in Computer Science, Software Engineering, or equivalent practical experience.
  • Proficiency in Python or another backend language such as Golang, Kotlin, or C#; engineers are assessed in their preferred language during a live coding session.
  • Strong understanding of backend fundamentals, including APIs, databases, caching, messaging systems, and distributed architectures.
  • Strong problem-solving abilities and attention to detail.
  • Ability to ship end-to-end features with minimal oversight.
  • Excellent communication skills and a collaborative attitude.
  • A team-oriented, low-ego mindset and curiosity for learning.
Responsibilities
  • Design, develop, and maintain scalable, robust backend features and APIs using modern frameworks.
  • Ensure high performance and reliability across distributed systems.
  • Contribute to systems powering inference, billing, AI tooling, observability, and developer experience.
  • Design and implement efficient, secure, and scalable architectures for fast-growing products.
  • Collaborate with infrastructure teams on deployment, monitoring, and performance optimization.
  • Write clean, maintainable, and well-documented code.
  • Participate in code reviews and contribute to technical standards and best practices.
  • Collaborate with product managers, platform engineers, and AI/ML teams to design, build, and deliver scalable model-as-a-service solutions for developers and enterprise users.
  • Partner with teams across AI Studio, Le Chat, and Mistral Code to ensure a consistent platform-wide experience.
  • Address complex engineering challenges involving distributed systems and AI product integration.
  • Stay current with new technologies, including AI/LLM integration, observability, and backend frameworks, and bring relevant technologies into production.
  • Complete the hiring process, including an introduction call, live-coding interview, system design interview, hiring manager interview, culture-fit discussion, and reference checks.
Desired Qualifications
  • Full-stack exposure.
  • Experience working closely with an API and developer ecosystem.
  • Experience deploying software to diverse environments, including cloud and on-premises environments.
  • Infrastructure management experience with Docker, CI/CD, Kubernetes, and Helm or Terraform.
  • AI/ML engineering experience.
  • Experience with observability and monitoring tools such as Prometheus, Grafana, and Datadog.
  • A UX and product-centric mindset.

About the company

Mistral AI develops open-weight large language models, along with inference infrastructure and enterprise tools that let organizations run AI with control over data residency, customization, and the underlying software stack. Its platform supports on-premises or cloud deployment of models, with governance and integration capabilities that avoid vendor lock-in to a single hyperscaler. By offering open model architectures and a modular stack, Mistral differentiates itself from competitors tied to proprietary ecosystems and data‑center limitations. The goal is to enable reliable, scalable AI deployments for public-sector and enterprise customers while expanding research, capacity, and international reach.

Company Size

1,001-5,000

Company Stage

Series D

Total Funding

$7.2B

Headquarters

Paris, France

Founded

2023

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Simplify's Take

What believers are saying

  • Samsung-led funding on September 8, 2026 gives Mistral capital for frontier research and datacenters.
  • Morocco's October 1, 2026 Darija launch proves governments buy Mistral for language sovereignty.
  • Canada expansion, Quebec government work, and TotalEnergies contracts deepen enterprise and public-sector demand.

What critics are saying

  • OpenAI and Anthropic keep widening enterprise share; Mistral still holds 2% in U.S. surveys.
  • Mistral's €3 billion September 8, 2026 raise demands massive compute spending before revenues justify it.
  • If inference economics worsen, sovereign-AI customers defect to cheaper Chinese open models by 2027.

What makes Mistral AI unique

  • Open-weight models plus on-prem deployment give European customers data residency and control.
  • Arthur Mensch opened Munich on September 28, 2026, targeting industrial AI with SAP, BMW, Siemens Energy.
  • Mistral integrates models, Vibe, and compute infrastructure, unlike API-only rivals OpenAI and Anthropic.

Help us improve and share your feedback! Did you find this helpful?

Benefits

  • Health Insurance
  • Company Equity
  • Parental Leave
  • 401(k) Retirement Plan
  • Paid Vacation

Growth & Insights and Company News

Headcount

6 month growth

-8%

1 year growth

-8%

2 year growth

-11%
TokenPost
Oct 15th, 2026
CERN and Mistral plan AI model for particle physics research.

CERN and Mistral plan AI model for particle physics research. The Large Physics Model would train on billions of simulated LHC collisions and support reconstruction, particle identification, signal detection and detector design. Published Oct 15, 2026, 8:46 AM EDT CERN and Mistral AI signed a statement of intent Thursday, Oct. 15, to develop a particle physics model aimed at handling analysis needs as collider data volumes rise. The initiative, called the Large Physics Model, is the first project in a long-term scientific AI partnership. It would be trained on billions of proton-proton collision events generated by simulation at the Large Hadron Collider (LHC). Planned uses include rebuilding collision events, identifying particles, flagging unexpected signals and helping researchers evaluate possible detector designs. The proposal covers several parts of the research workflow rather than a single narrowly defined task. Today, collision reconstruction uses specialized algorithms to turn detector readings into particles and other objects used in research. Machine learning already supports some focused tasks. The new model is intended to cover more of that analysis chain in one system. No performance results have been presented to show how well it can handle the planned work. The training material and stored archive are different: planned training would use simulated events, while the experimental-data tally reflects records already collected. CERN has stored about one exabyte of LHC experimental data, roughly 1 million terabytes. No additional details on the planned training set have been announced. The High-Luminosity Large Hadron Collider is expected to begin operating around 2030. The upgraded machine is designed to produce roughly 10 times as many collisions and as much data as the original LHC design. That projected growth would increase the volume of material researchers need to analyze. "High-energy physics provides a unique environment in which artificial intelligence can be developed and tested with exceptional rigour," said Enrica Porcari, CERN's chief information officer. "We are proud to work with CERN to explore what large-scale AI models can bring to the growing complexity of particle physics," said Johannes Brandstetter, Mistral's vice president of AI for Engineering. A project schedule, funding figure, computing resources, milestones, access terms and performance benchmarks remain unspecified. CERN has not identified Mistral Large 4 as part of the physics project, and the partners have not announced when development or training will begin. Simon Yoon. Simon Yoon reports on blockchain technology for TokenPost. Send corrections or tips to [email protected].

MarketScreener
Oct 15th, 2026
CERN and Mistral partner to build Flare, a new AI model for physics

CERN and Mistral partner to build Flare, a new AI model for physics Published on 10/15/2026 at 08:14 am EDT - Modified on 10/15/2026 at 09:07 am EDT or by email * Stock Market * Stock Market News * CERN and Mistral partner to build Flare, a new AI model for physics

Crypto Briefing
Oct 15th, 2026
CERN partners with Mistral to build a Large Physics Model

CERN partners with Mistral to build a Large Physics Model. Chart unavailable for this asset. Select Chart to retry. Europe's particle physics lab and its leading AI startup signed a statement of intent to train a foundation model on collider data an hour ago Sponsored: Vera - AI-powered prediction market intelligence, built for serious analysts Explore Vera CERN has spent decades smashing protons together to understand the universe. Now it wants an AI model that can make sense of the wreckage. On October 15, 2026, the European Laboratory for Particle Physics signed a statement of intent with Mistral AI, the French developer behind the Mistral family of language models. The goal is a pretrained Large Physics Model, a foundation model built specifically for high-energy physics. What the two sides are building. The model is expected to train on billions of simulated proton-proton collision events tied to the Large Hadron Collider (LHC). The idea is to give physicists a general-purpose tool rather than a pile of narrow, single-task algorithms. Three applications sit at the top of the list. The first is collision reconstruction, which means working backward from detector signals to figure out what actually happened in a collision. The second is particle identification, sorting out which particles flew out of a given event. The third is anomaly detection, flagging events that don't match what known physics predicts. The data problem driving the deal. CERN does not lack raw material. Its LHC experiments have produced close to one exabyte of data. The High-Luminosity LHC upgrade, projected for around 2030, is expected to raise collision rates by a factor of ten. Traditional analysis pipelines were not designed with that volume in mind, which helps explain why CERN is looking for an AI partner now rather than later. AI, tech, and the markets they move - in one daily briefing. Daily. Free. Join 34,000+ readers across crypto, finance, and policy. Mistral brings the other half of the equation: experience training very large models. The company previewed Mistral Large 4 on October 6, 2026, a model with 1 trillion parameters. How Cryptobriefing got here. CERN and Mistral have held earlier exchanges and presentations dating back to 2025, laying groundwork before anything was put in writing. Neither organization has disclosed timelines, funding figures, or specific milestones beyond the initial agreement. What this means. For physicists, the stakes are practical. If a single pretrained model can handle reconstruction, identification, and anomaly detection reasonably well, research teams could spend less time building bespoke tools and more time asking questions. The risks are the usual ones for an early-stage scientific AI effort. Models trained on simulated data have to prove they hold up against real detector output, and physics demands a level of rigor that tolerates very little hallucination. What to watch next is straightforward. Look for a formal agreement with defined funding and deadlines, any published benchmarks on reconstruction or anomaly detection, and signs of whether the model is being readied ahead of the High-Luminosity era around 2030. Disclosure: This article was edited by Diego Almada Lopez. For more information on how Cryptobriefing create and review content, see its Editorial Policy.

Les Echos
Oct 15th, 2026
With New Material, Mistral completes its fourth acquisition of the year.

With New Material, Mistral completes its fourth acquisition of the year. The French start-up is strengthening its teams to accelerate the adoption of artificial intelligence in businesses, particularly industry and critical sectors. Published on Oct. 15, 2026 at 12:18 Reserved for our subscribers The big Chat is starting to get an appetite. The French artificial intelligence (AI) laboratory Mistral, founded only three and a half years ago, is multiplying acquisitions. It has just acquired the British start-up New Material, specializing in the design of AI-based products. "Mistral acquires New Material to integrate a design-driven approach into cutting-edge AI, thereby tightening the feedback loop between customer solutions and the product," explains this company founded in London in 2020, which has fewer than ten people, in a statement published Wednesday evening. The amount of the transaction was not made public. This content is reserved for La Sélection and Premium subscribers Would you like to read the rest? For readers in a hurry but demanding: no compromise with La Sélection

WQXC
Oct 15th, 2026
CERN and Mistral partner to build Flare, a new AI model for physics.

CERN and Mistral partner to build Flare, a new AI model for physics. By Thomson Reuters Oct 15, 2026 | 8:02 AM By Leo Marchandon Oct 15 (Reuters) - European physics research centre CERN and Mistral AI have established a long-term strategic partnership to develop a foundation model for scientific discovery, the two organisations said on Thursday. The statement of intent gives the French AI company access to one of the world's largest scientific datasets as it seeks to compete on frontier science. Mistral Chief Executive Arthur Mensch wrote in an article published in Le Monde on Thursday that frontier models were "now indispensable to scientific progress" and criticised companies that keep their advantages to themselves. CERN Chief Information Officer Enrica Porcari said the new partnership would "advance AI capabilities that can serve the whole physics community and be shared". The partners aim to build a "Large Physics Model", called "Flare", which CERN said in a statement would be the first foundation model for collider physics, trained on billions of simulated particle collisions. It will use the same idea as the large language models behind generative AI but be trained on collision data rather than text, a CERN spokesperson told Reuters. It is meant to replace some specialised algorithms now used to reconstruct and identify particles. The spokesperson said CERN has been using AI to turn raw data from experiments into pictures of the events for a decade, but with multiple tools used for each step. CERN holds about one exabyte, or 1 million terabytes, of experimental data from the Large Hadron Collider, and expects the volume of collisions to increase tenfold after its ongoing upgrade, expected around 2030. "High-energy physics provides a unique environment in which artificial intelligence can be developed and tested with exceptional rigour," Porcari said. No timeline or budget was disclosed. Both sides said the models could later serve other sciences. The Paris-based lab, broadly viewed as Europe's best chance to catch up with US and Chinese competitors, this month unveiled a new open-weight model it says can challenge its rivals. It will be released to the public on October 27. Users can download open-weight models from platforms and use them on their own hardware. (Reporting by Leo Marchandon in Gdansk; Editing by Toby Chopra, Emelia Sithole-Matarise and Joe Bavier)