research software engineer

Helmholtz-Zentrum für Umweltforschung UFZ

<div></div><div><div><div><div><p>Arbeitsort</p>
Leipzig, Leipzig (Mobiles Arbeiten ist bis zu 50% der Arbeitszeit m&ouml;glich)
<p>Arbeitszeit</p>
100% (39h/ Woche)
<p>Der Arbeitsplatz ist f&uuml;r eine Besetzung in Vollzeit oder in Teilzeit geeignet.</p>
<p>Befristung</p>
befristet / Zun&auml;chst befristet auf 2 Jahre, Option auf Verl&auml;ngerung m&ouml;glich
<p>Verg&uuml;tung</p>
nach TV&ouml;D bis zur Entgeltgruppe 13 inklusive der Sozialleistungen des &ouml;ffentlichen Dienstes
<p>Kontakt</p>
<p>Thomas Schnicke - Thomas.schnicke@ufz.de,
<br>
Kai Morlock &ndash; kai.morlock@ufz.de</p>
<p>Ihre Bewerbung</p>
<p>Um eine <b>faire Auswahl</b> zu gew&auml;hrleisten, reichen Sie bitte Ihre Bewerbung (Anschreiben, Lebenslauf, relevante Anlagen) <b>ohne Foto</b>, Altersangabe und Angaben zum Familienstand &uuml;ber unser <b>Online-Portal</b> ein.</p>
<p>Diversit&auml;t und Inklusion</p>
<p>Das UFZ sch&auml;tzt <b>Vielfalt</b> und setzt sich aktiv f&uuml;r die <b>Chancengleichheit</b> aller Besch&auml;ftigten, unabh&auml;ngig von ihrer Herkunft, Religion, Weltanschauung, Behinderung, des Alters und der sexuellen Identit&auml;t, ein.
<br>
<b>Wir freuen uns auf Menschen, die diverse Hintergr&uuml;nde, Identit&auml;ten und Perspektiven repr&auml;sentieren.</b> Daher ermutigen wir insbesondere Menschen, die von struktureller Diskriminierung betroffen sind, sich bei uns zu bewerben.</p>
</div><div><div><p>Das UFZ</p>
<p>Das Helmholtz-Zentrum f&uuml;r Umweltforschung GmbH &ndash; UFZ ist eine weltweit f&uuml;hrende Institution in der Umweltforschung und Teil der Helmholtz-Gemeinschaft, der gr&ouml;&szlig;ten Wissenschaftsorganisation Deutschlands. Mit rund 1.200 Mitarbeitenden in Leipzig, Halle und Magdeburg forschen wir seit 1991 mit einem transdisziplin&auml;ren Ansatz an den dr&auml;ngenden Herausforderungen unserer Zeit: Biodiversit&auml;tsverlust, Klimawandel und Umweltverschmutzung. Unser Ziel ist es, exzellente Forschung in konkrete L&ouml;sungen f&uuml;r Politik, Wirtschaft und Gesellschaft zu &uuml;bersetzen und als verl&auml;sslicher Partner Transformationsprozesse hin zu einer nachhaltigen und gerechten Zukunft f&uuml;r heutige und kommende Generationen zu unterst&uuml;tzen. Dabei leben wir eine Kultur der Zusammenarbeit, Offenheit und Vielfalt in einem Arbeitsumfeld, das Kreativit&auml;t und pers&ouml;nliche Weiterentwicklung aktiv f&ouml;rdert.
</p></div><div><p>Die Stelle</p>
<p>Forschungssoftware ist heute ein zentraler Bestandteil moderner Wissenschaft und bildet die Grundlage f&uuml;r reproduzierbare, transparente und nachhaltige Forschung. HIFIS &ndash; Helmholtz Federated IT Services (Link) unterst&uuml;tzt die Helmholtz-Gemeinschaft mit f&ouml;derierten IT-Diensten und Softwarel&ouml;sungen, um wissenschaftliche Softwareentwicklung zu professionalisieren, sichtbar und langfristig nutzbar zu machen. In der ausgeschriebenen Position als DevOps Research Software Engineer unterst&uuml;tzt du den zentren&uuml;bergreifenden Aufbau und Betrieb von Komponenten f&uuml;r moderne Tools und Workflows in der Entwicklung von Forschungssoftware. Die Stelle ist im HIFIS Kompetenzcluster Software Services (Link) angesiedelt und am UFZ im Team Research Data Management (Link) verortet. Dabei arbeitest du eng mit wissenschaftlichen Communities sowie den HIFIS-weiten Infrastruktur- und Softwareangeboten zusammen.</p>
</div><p>Ihre Aufgaben</p>
<p>Du verst&auml;rkst das HIFIS-Team mit der Vision, den digitalen Wandel in der Forschung am UFZ und der Helmholtz-Gemeinschaft mitzugestalten, Forschungssoftware als einen wichtigen Teil der Forschung sichtbar zu machen und bringst dich konkret in folgenden Bereichen ein:<br>
<br>
</p><ul><li>Du entwickelst und betreibst Forschungssoftware-bezogene Cloud-Dienste f&uuml;r die Helmholtz Gemeinschaft</li><li>
Du unterst&uuml;tzt Forschende bei der Integration und Nutzung von HIFIS-Diensten in wissenschaftlichen Workflows</li><li>
Du evaluierst und implementierst neue Technologien f&uuml;r die Unterst&uuml;tzung des gesamten Forschungssoftware-Lifecycle</li><li>
Du vertrittst das Helmholtz-Zentrum f&uuml;r Umweltforschung innerhalb von HIFIS und f&ouml;rderst den zentren&uuml;bergreifenden Wissenstransfer</li><li>
Du st&auml;rkst Support, Sichtbarkeit und Weiterentwicklung von HIFIS-Diensten</li></ul><p></p>
<p>Wir bieten</p>
<p></p><ul><li>Flexible Arbeitszeiten f&uuml;r eine ausgewogene Work-Life-Balance</li><li>
Die Mitarbeit in einem interdisziplin&auml;ren, multinationalen und motivierten Team mit hoher technischer Expertise</li><li>
Eine hervorragende technische Ausstattung, M&ouml;glichkeit zum Einsatz und Erprobung neuester Technologien</li><li>
Eine hervorragende Einbindung in nationale und internationale Forschungsnetzwerke und Initiativen im Bereich Forschungsdatenmanagement und -softwareentwicklung</li><li>
Freir&auml;ume, um innovative L&ouml;sungen bei der Unterst&uuml;tzung der Forschung zu entwickeln, und f&uuml;r die Exploration von neuen Technologien</li><li>
Die M&ouml;glichkeit zur pers&ouml;nlichen Weiterentwicklung durch ein umfangreiches Qualifizierungs- und Weiterbildungsangebot</li><li>
Eine T&auml;tigkeit in Leipzig, einer der attraktivsten St&auml;dte Deutschlands, mit hoher Lebensqualit&auml;t und kultureller Vielfalt</li><li>
Besonderheiten, wie einen sch&ouml;nen Wissenschaftscampus mit zentraler Anbindung, Unterst&uuml;tzung bei der Kinderbetreuung, Beratung bei Pflege von Familienangeh&ouml;rigen etc.</li><li>
Ein Jahreseinkommen, je nach Qualifikation, in H&ouml;he von 62.489 &euro; bis 78.760 &euro; brutto, sowie tarifliche Sonderleistungen, wie z. B. betriebliche Altersvorsorge, verm&ouml;genswirksame Leistungen und die M&ouml;glichkeit f&uuml;r ein bezuschusstes Deutschland-Jobticket</li></ul><br>
Deine Arbeit ist integraler Bestandteil eines gr&ouml;&szlig;eren Ganzen, das zur Umsetzung der HIFIS-Vision in der Schaffung eines hervorragenden IT-Dienste-Portfolios f&uuml;r herausragende Forschung in allen Helmholtz-Forschungsbereichen und eine nahtlose und leistungsf&auml;hige IT-Infrastruktur beitr&auml;gt.<br>
Wir freuen uns, wenn Du uns auf diesem Weg unterst&uuml;tzen willst.<br>
Wir hei&szlig;en ausdr&uuml;cklich Bewerbungen von Menschen mit vielf&auml;ltigen Perspektiven und Hintergr&uuml;nden willkommen!<br>
<p></p>
<p>Ihr Profil</p>
<p></p><ul><li>Abgeschlossenes Studium in Informatik oder einem vergleichbarem Fachgebiet mit IT-Schwerpunkt &ndash; QuereinsteigerInnen mit relevanter Erfahrung sind herzlich willkommen</li><li>
Erfahrungen in der Konzeption und dem Betrieb moderner, interoperabler und skalierbarer IT-Architekturen basierend auf Container-Technologien (z.B. Docker, Kubernetes) mit Blick auf Automatisierung von Workflows (CI/CD), IT-Sicherheit und digitale Souver&auml;nit&auml;t</li><li>
Erfahrungen in nachhaltiger Softwareentwicklung und der Gestaltung von Softwareentwicklungsprojekten, vorzugsweise in kollaborativen Teams</li><li>
Vertiefte Kenntnisse in mindestens einer Programmiersprache, z.B. PHP, Python, Java, JS/ES6, Node.JS</li><li>
Hohe Affinit&auml;t zur interdisziplin&auml;ren Zusammenarbeit und technischen Beratung, sowie Umsetzung von Anforderungen</li><li>
Starke Kommunikationsf&auml;higkeiten und Freude an der Vermittlung von Wissen</li><li>
Sehr gute Deutsch- und Englischkenntnisse in Wort und Schrift &ndash; oder die Bereitschaft, diese weiter zu verbessern
</li></ul><br>
Wir m&ouml;chten Dich ausdr&uuml;cklich dazu ermutigen, Dich auch dann zu bewerben, wenn Du nicht alle Anforderungen der Stellenausschreibung vollst&auml;ndig erf&uuml;llst.</div><p></p><div><br>
<p>Bewerbungsfrist: 06.07.2026</p>
</div></div></div><div><div><div>Weitere Karriereinformationen &uuml;ber das UFZ:<br>
www.ufz.de/karriere
<h4 class="jobSectionHeader">LinkedIn @UFZ<b>
</b></h4></div><div><h4 class="jobSectionHeader">Familienfreundlichkeit<b>
</b></h4><h4 class="jobSectionHeader">International Office<b>
</b></h4></div><div><h4 class="jobSectionHeader">Barrierefreiheit<b>
</b></h4><h4 class="jobSectionHeader">Diversity, Equity &amp; Inclusion</h4></div></div></div></div><p></p>

DevOps Research Software Engineer (d/m/w)

<p></p><div><div><div><b>Ref Number</b></div>
<div>B04-07554</div>
<div><b>Professional Expertise</b></div>
<div>Research and Research Support</div>
<div><b>Department</b></div>
<div>UCL BEAMS (B04)</div>
<div><b>Location</b></div>
<div>London</div>
<div><b>Working Pattern</b></div>
<div>Full time</div>
<div><b>Salary</b></div>
<div>&pound;54,931-&pound;64,644</div>
<div><b>Contract Type</b></div>
<div>Fixed-term</div>
<div><b>Working Type</b></div>
<div>Hybrid</div>
<div><b>Available for Secondment</b></div>
<div>No</div>
<div><b>Closing Date</b></div>
<div>19-Jul-2026</div>
</div><p></p><h2 class="jobSectionHeader"><b>About us</b></h2>
<p></p><p>UCL is one of the world's top universities based in the heart of London. It is a modern, outward-looking institution. UCL's staff and former students have included 33 Nobel prizewinners. It is a truly international community: more than one-third of our student body &ndash; more than 50,000 strong &ndash; come from 150 countries and nearly one-third of staff are from outside the UK.</p><p></p>
<h2 class="jobSectionHeader"><b>About the role</b></h2>
<p></p><p>The postholder will design, develop, and maintain software and system components for high-performance data acquisition and trigger systems in large-scale particle physics experiments. This includes contributing to real-time data processing pipelines, distributed computing systems, and hardware&ndash;software integration.</p>
<p>
The role aims to ensure reliable, efficient, and scalable data-taking operations, while supporting research objectives through innovative software development and system optimisation. The postholder will also contribute to collaborative research outputs and provide technical expertise within international experiment collaborations.</p>
<p>
If you have any informal queries about the role, please contact Prof Gavin Hesketh -gavin.hesketh@ucl.ac.uk</p><p></p>
<h2 class="jobSectionHeader"><b>About you</b></h2>
<p></p><p>We are looking for someone with a degree (or equivalent experience) in Physics, Computer Science, Engineering, or a closely related field. You must have demonstrable experience developing software for scientific, real-time, or high-performance systems. It is expected that the successful postholder will have working knowledge of data acquisition or distributed computing systems in a research or industrial context. A strong understanding of programming in C++ and/or Python is needed to carry out the role and experience with hardware-near programming (e.g. FPGA, GPU, or embedded systems) or low-latency computing.</p>
<p>
Knowledge of software development best practices, including version control (e.g. Git), continuous integration, testing, and documentation plus a familiarity with Linux-based environments and command-line tools is essential. The successful postholder will have the ability to design and implement efficient, scalable software solutions for complex systems. Strong problem-solving skills and ability to debug complex software and system-level issues is required.</p>
<p>
The ability to communicate technical concepts clearly to both specialist and non-specialist audiences alongside being able to work effectively both independently and as part of an international collaborative team is essential. Strong organisational skills and ability to manage competing priorities and deadlines is a necessary requirement.</p>
<p>
Experience working on large-scale particle physics experiments is desirable.</p><p></p>
<h2 class="jobSectionHeader"><b>What we offer</b></h2>
<p></p><p>As well as the exciting opportunities this role presents, UCL also offers competitive benefits, such as:</p>
<ul><li>41 days annual leave (including 6 University closure days and 8 UK bank holidays)</li></ul>
<ul><li>Additional 5 days&rsquo; annual leave purchase scheme</li></ul>
<ul><li>Defined benefit career average revalued earnings pension scheme (CARE)</li></ul>
<ul><li>Cycle to work scheme and season ticket loan</li></ul>
<ul><li>Immigration loan and relocation assistance</li></ul>
<ul><li>On-campus nursery</li></ul>
<ul><li>On-campus gym</li></ul>
<ul><li>Enhanced maternity, paternity and adoption pay</li></ul>
<ul><li>Employee assistance programme: Staff Support Service</li></ul>
<ul><li>Discounted medical insurance</li></ul>
<p>
Visit https://www.ucl.ac.uk/work-at-ucl/reward-and-benefits to find out more</p><p></p>
<h2 class="jobSectionHeader"><b>Our commitment to Equality, Diversity and Inclusion</b></h2>
<p></p><p>As London&rsquo;s Global University, we know diversity fosters creativity and innovation. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong.</p>
<p>
We particularly encourage applications from candidates underrepresented in UCL&rsquo;s workforce, including those from Black, Asian and ethnic minority backgrounds, disabled people, and &ndash; for Grade 9 and 10 roles &ndash; women.&rdquo;</p>
<p>
Read more: UCL Equality, Diversity and Inclusion.</p>
<p>
Applicants should also familiarise themselves with UCL&rsquo;s policies on: Prevention of Bullying, Harassment and Sexual Misconduct Policy and Personal Relationships Policy</p></div><p></p>

Research Software Engineer/Physicist (Data Acquisition Systems)

Reflection

<div>
 <strong>
  Our Mission
  <br/>
  <br/>
 </strong>
 Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
 <br/>
 <br/>
 <strong>
  The Roles Mission
  <br/>
  <br/>
 </strong>
 Bridge the gap between research and production by turning cutting-edge algorithms into scalable training systems. You will design and optimize the core infrastructure behind frontier AI models &mdash; from reinforcement learning training loops and distributed GPU training to massive-scale data pipelines.
 <br/>
 <br/>
 Our systems train models across thousands of GPUs and process petabyte-scale datasets. We care deeply about numerical stability, throughput, and reproducibility.
 <br/>
 <br/>
 <strong>
  What This Team Does
  <br/>
  <br/>
 </strong>
 This team owns and evolves the core infrastructure behind our training systems.
 <br/>
 <br/>
 <strong>
  We Focus On
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Reinforcement learning training infrastructure
  </li>
  <li>
   Distributed training and inference systems
  </li>
  <li>
   Experiment infrastructure and reproducibility
  </li>
  <li>
   Large-scale data pipelines
   <br/>
   <br/>
  </li>
 </ul>
 The goal is to build the engineering foundation that allows researchers to iterate quickly while training models at massive scale.
 <br/>
 <br/>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 You will architect and optimize the core training infrastructure that powers our models. This includes RL training loops, distributed GPU systems, and large-scale data pipelines.
 <br/>
 <br/>
 You will work closely with researchers to transform new ideas into reliable, scalable training systems.
 <br/>
 <br/>
 <strong>
  Responsibilities Include
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Designing and optimizing large-scale training loops and data pipelines.
  </li>
  <li>
   Implementing state-of-the-art techniques and ensuring they are numerically stable and computationally efficient.
  </li>
  <li>
   Building internal tooling for launching, monitoring, and reproducing complex experiments.
  </li>
  <li>
   Diagnosing deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls).
  </li>
  <li>
   Translating research prototypes into reusable, production-grade infrastructure.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What You'll Work With
  <br/>
  <br/>
 </strong>
 <strong>
  Distributed Training
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   GPU parallelism (data, tensor, pipeline, expert)
  </li>
  <li>
   Large-scale distributed training infrastructure
  </li>
  <li>
   Communication optimization (NCCL, RDMA, GPU interconnects)
  </li>
  <li>
   FSDP / ZeRO and model sharding
   <br/>
   <br/>
  </li>
 </ul>
 Orchestration &amp; Runtime Systems
 <br/>
 <br/>
 <ul>
  <li>
   Ray, Kubernetes, Slurm
  </li>
  <li>
   Distributed runtimes and async systems
  </li>
  <li>
   Containerization and sandboxing
   <br/>
   <br/>
  </li>
 </ul>
 Frameworks
 <br/>
 <br/>
 <ul>
  <li>
   PyTorch
  </li>
  <li>
   JAX
  </li>
  <li>
   Megatron-style training stacks
  </li>
  <li>
   Triton / custom kernels
   <br/>
   <br/>
  </li>
 </ul>
 Data Infrastructure
 <br/>
 <br/>
 <ul>
  <li>
   Large-scale dataset curation pipelines
  </li>
  <li>
   Deduplication and filtering systems
  </li>
  <li>
   Tokenization and preprocessing
  </li>
  <li>
   Distributed data processing frameworks
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  About You
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   You are a strong software engineer who speaks the language of machine learning.
  </li>
  <li>
   You may not have a PhD, but you know how to implement a research paper.
  </li>
  <li>
   You have deep experience in at least one of the following: Distributed Training &amp; Inference or Data Infrastructure
  </li>
  <li>
   You enjoy working at the boundary between:
  </li>
  <ul>
   <li>
    Machine learning algorithms
   </li>
   <li>
    Distributed systems
   </li>
   <li>
    High-performance computing
   </li>
  </ul>
  <li>
   You care deeply about performance, numerical stability, and reproducibility.
  </li>
  <li>
   You thrive in high-agency environments and enjoy solving hard technical problems.
   <br/>
  </li>
 </ul>
 <strong>
  What We Offer
  <br/>
  <br/>
 </strong>
 We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models.
 <br/>
 <br/>
 We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.
 <br/>
 <br/>
 <ul>
  <li>
   Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.
  </li>
  <li>
   Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.
  </li>
  <li>
   Health &amp; wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.
  </li>
  <li>
   Meals: Lunch and dinner are provided in the office daily.
  </li>
  <li>
   Life &amp; family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
  </li>
  <li>
   Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.
  </li>
  <li>
   Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
  </li>
  <li>
   Team building: We have regular off-sites, happy hours, and team celebrations.
   <br/>
   <br/>
  </li>
 </ul>
</div>


Member of Technical Staff - Research Software Engineer

DeepMind

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor's degree in Computer Science, Physics, Mathematics, Chemistry, Engineering, a related field, or equivalent practical experience.</li>
<li>5 years of experience in machine learning, engineering, and software development using Python.</li>
<li>Experience with data structures or algorithms in an academic or industry setting</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>PhD in a relevant scientific or computational field.</li>
<li>Experience in agentic AI and modern machine learning paradigms (e.g., diffusion, transformers, agentic tools, LLMs).</li>
<li>Knowledge of quantum computing concepts.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.</p><p>
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.</p>
<p>
As a Research Engineer on our Science Team, you will contribute to our Quantum Computing Initiative. This role is an excellent opportunity for a creative and driven engineer who truly burns for the immense possibilities of AI for Science.<br>
<br>
In this role, you will act as the crucial bridge between research and implementation, by seamlessly linking quantum computing research with ML. As an amazing team player, you will collaborate with domain experts in (quantum) computer science, mathematics, computational chemistry, physics, and machine learning.</p><p>
Artificial intelligence will be one of humanity&rsquo;s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.</p><div>
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.</div></div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Deliver on complex research and engineering milestones, assuming end-to-end ownership of implementing and scaling ML solutions (traditional and agentic) at the intersection of quantum computing and machine learning.</li>
<li>Complement the algorithms development pillar by translating classical and quantum algorithms into robust code, balancing implementation time with efficiency, design simplicity, and sustainability.</li>
<li>Facilitate communication between research and engineering by advocating code readability, promoting engineering best practices, and writing effective design documents.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Research Engineer, Quantum Computing, DeepMind

Mistral

<div>
 <strong>
  About Mistral
  <br/>
  <br/>
 </strong>
 At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.
 <br/>
 <br/>
 We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise as well as personal needs. Our offerings include Le Chat, La Plateforme, Mistral Code and Mistral Compute - a suite that brings frontier intelligence to end-users.
 <br/>
 <br/>
 We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited.
 <br/>
 <br/>
 Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers.
 <br/>
 <br/>
 Role Summary
 <br/>
 <br/>
 <strong>
  About The Research Engineering Team
  <br/>
  <br/>
 </strong>
 The team spans
 <strong>
  Platform
 </strong>
 (shared infra &amp; clean code) and
 <strong>
  Embedded
 </strong>
 (inside research squads). Engineers can move along the research&harr;production spectrum as needs or interests evolve.
 <br/>
 <br/>
 As a Research Engineer &ndash; ML track, you&rsquo;ll build and optimise the large-scale learning systems that power our open-weight models. Working hand-in-hand with Research Scientists, you&rsquo;ll either join:
 <br/>
 <br/>
 <ul>
  <li>
   Platform RE Team: Enhance the shared training framework, data pipelines and cluster tooling used by every team; or
  </li>
  <li>
   Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, &hellip;) and turn fresh ideas into repeatable, scalable code
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 What will you do
 <br/>
 <br/>
 <ul>
  <li>
   Accelerate researchers by taking on the heavy parts of large-scale ML pipelines and building robust tools
  </li>
  <li>
   Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs
  </li>
  <li>
   Conduct experiments on the latest deep-learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs)
  </li>
  <li>
   Design, implement and benchmark ML algorithms; write clear, efficient code in Python
  </li>
  <li>
   Deliver prototypes that become production-grade components for Le Chat and our enterprise API
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  About You
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Master&rsquo;s or PhD in Computer Science (or equivalent proven track record)
  </li>
  <li>
   4 + years working on large-scale ML codebases
  </li>
  <li>
   Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s)
  </li>
  <li>
   Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops
  </li>
  <li>
   Strong software-design instincts: testing, code review, CI/CD
  </li>
  <li>
   Self-starter, low-ego, collaborative
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What We Offer
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   💰 Competitive salary and equity
  </li>
  <li>
   🚑 Healthcare: Medical/Dental/Vision covered for you and your family
  </li>
  <li>
   👴🏻 Pension : 401K (6% matching)
  </li>
  <li>
   🏝️ PTO : 18 days
  </li>
  <li>
   🚗 Transportation: Reimburse office parking charges, or $120/month for public transport
  </li>
  <li>
   🏀 Sport: $120/month reimbursement for gym membership
  </li>
  <li>
   🥕 Meal stipend: $400 monthly allowance for meals (solution might evolve as we grow bigger)
  </li>
  <li>
   🌎 Visa sponsorship
  </li>
  <li>
   🤝 Coaching: we offer BetterUp coaching on a voluntary basis
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 By applying, you agree to our Applicant Privacy Policy.
</div>


Research Engineer, Machine Learning

Deloitte

<div>
 Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort,, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system decides - across payers, providers, and life sciences, and for the patients they serve - so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.
 <br/>
 <br/>
 This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes.
 <br/>
 <br/>
 As a Research Engineer on our post-training team, you will design, train, evaluate, and align the models that reason about healthcare - working across the full post-training lifecycle to shape model behavior for clinical and operational decisioning across the industry. Healthcare decisioning is one of the cleanest verifiable-reward domains outside math and code: the problems are hard. We ground that reward in real signals - clinical policy and criteria, adjudicated outcomes, and clinical-expert judgment - so correctness is checkable rather than asserted.
 <br/>
 <br/>
 You will own the post-training stack for our clinical reasoning models end to end - from data and reward design through trained, evaluated models that ship. This is not a prompt-engineering role. We are looking for people who understand not just how to use LLMs, but how to improve and shape model behavior through advanced post-training.
 <br/>
 <br/>
 You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain - you bring the modeling depth.
 <br/>
 <br/>
 We hire on demonstrated depth, not years - the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.
 <br/>
 <br/>
 Work you'll do
 <br/>
 <br/>
 Post-training &amp; alignment
 <br/>
 <br/>
 <ul>
  <li>
   Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning / alignment workflows.
  </li>
  <li>
   Build and optimize training using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, and Constitutional AI, and understand how each affects reasoning quality, safety, latency, cost, and reliability.
  </li>
  <li>
   Train reasoning models for healthcare decisioning using verifiable-reward RL - designing reward signals and verifiers grounded in clinical guidelines, policy and criteria, and adjudicated outcomes.
   <br/>
   <br/>
  </li>
 </ul>
 Reward modeling &amp; data
 <br/>
 <br/>
 <ul>
  <li>
   Develop reward models and preference datasets to improve reasoning quality, factuality, safety, policy adherence, and task performance.
  </li>
  <li>
   Curate, clean, synthesize, and evaluate large-scale instruction, preference, and domain-specific datasets, with rigorous filtering, deduplication, and quality control.
  </li>
  <li>
   Build verification and reward pipelines from our proprietary clinical, claims, and operational data and from clinical-expert labeling - turning guidelines, policy, and adjudicated outcomes into checkable reward signals at scale.
   <br/>
   <br/>
  </li>
 </ul>
 Efficient fine-tuning, training &amp; inference infrastructure
 <br/>
 <br/>
 <ul>
  <li>
   Implement efficient fine-tuning strategies including LoRA, QLoRA, PEFT, and adapter-based approaches; build scalable distributed training using DeepSpeed, FSDP, Megatron-LM, Ray, or equivalent.
  </li>
  <li>
   Optimize inference performance - latency, throughput, quantization, and deployment efficiency - for production, including frameworks such as vLLM, TensorRT-LLM, or TGI.
   <br/>
   <br/>
  </li>
 </ul>
 Small language models &amp; open-weight models
 <br/>
 <br/>
 <ul>
  <li>
   Train and optimize open-weight models such as Llama, Qwen, Mistral, or DeepSeek; build specialized small language models (SLMs) for on-premise and cloud-hybrid deployment with strong performance-per-dollar.
   <br/>
   <br/>
  </li>
 </ul>
 Evaluation, safety &amp; red teaming
 <br/>
 <br/>
 <ul>
  <li>
   Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.
  </li>
  <li>
   Build healthcare-grade evaluation - held-out clinical benchmarks, deployment regression gates, calibration and uncertainty, factuality against ground truth, and bias/fairness evaluation across patient populations and subgroups - co-designed with clinical experts.
  </li>
  <li>
   Apply PHI/HIPAA-aware data handling and produce model documentation suitable for regulated clinical use.
  </li>
  <li>
   Perform red teaming and adversarial testing to identify alignment failures, unsafe behaviors, jailbreak vulnerabilities, and regression risks; collaborate with agentic and application teams to improve tool use, grounding, and long-horizon reasoning.
   <br/>
   <br/>
  </li>
 </ul>
 The team
 <br/>
 <br/>
 Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world. The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.
 <br/>
 <br/>
 You can go deep. The team sub-specializes across post-training research, data and reward engineering, and training and inference infrastructure - you won't be expected to own all of it alone.
 <br/>
 <br/>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.
  </li>
  <li>
   Demonstrated depth training and post-training large transformer-based language models in production or research - this is your craft, not coursework or a one-off fine-tune. Genuine depth including SFT and at least one preference-optimization or RL method, evidenced by shipped models, releases, or research.
  </li>
  <li>
   Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.
  </li>
  <li>
   Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.
  </li>
  <li>
   Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.
  </li>
  <li>
   Strong expertise in PyTorch and modern deep-learning tooling; experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Ray.
  </li>
  <li>
   Experience implementing efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and quantization-aware workflows.
  </li>
  <li>
   Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.
  </li>
  <li>
   Strong grasp of LLM evaluation methodologies, benchmarking, reward modeling, and alignment trade-offs; experience with large-scale and synthetic datasets, filtering, deduplication, and quality-control pipelines.
  </li>
  <li>
   Strong Python engineering skills and production-grade software practices; ability to work through ambiguous, highly complex technical problems in fast-moving environments.
  </li>
  <li>
   Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.
  </li>
  <li>
   Limited immigration sponsorship may be available.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.
  </li>
  <li>
   Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.
  </li>
  <li>
   Experience with multimodal models, speech models, or domain-specific foundation models; experience using large-scale GPU clusters and distributed compute.
  </li>
  <li>
   Contributions to open-source AI projects, research publications, benchmark development, or model releases.
  </li>
  <li>
   Familiarity with safety, governance, and responsible-AI practices; experience in regulated or high-stakes industries such as healthcare, finance, insurance, or public sector.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Compensation
  <br/>
  <br/>
 </strong>
 Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance-based incentive opportunity designed to grow with the value you help create - startup-style upside, with the backing of a committed, well-capitalized platform. The estimated base salary range is $189,200-$372,900 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.
</div>


Research Engineer — Post-Training & Small Language Models (SLMs), Healthcare AI

<div><h3 class="jobSectionHeader"><b>Minimum qualifications:</b></h3><ul>
<li>Bachelor&rsquo;s degree in Computer Science or a related technical field, or equivalent practical experience.</li>
<li>5 years of experience in software development using Python or C++.</li>
<li>Experience in deep learning frameworks, such as JAX or PyTorch.</li>
<li>Experience in standard analysis and scientific computing libraries such as numpy, pandas, and matplotlib.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>PhD with a research focus on Machine Learning, Reinforcement Learning, or Multi-Agent Systems.</li>
<li>Experience training large-scale models on accelerators (TPUs, GPUs) in a distributed environment.</li>
<li>Experience working with language models such as designing agentic harnesses, memory retrieval, or fine-tuning.</li>
<li>A track record of leading complex software projects and a passion for enabling groundbreaking research through engineering.</li>
<li>Deep expertise building and optimizing complex systems in JAX.</li>
<li>A strong background in multi-agent reinforcement learning, algorithmic game theory, or computational economics.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.</p><p>
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.</p>
<p>
Artificial intelligence will be one of humanity&rsquo;s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.</p><div>
We are pushing the boundaries across multiple domains. Our global teams offer learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.</div></div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Contribute to the creation of novel multi-agent learning algorithms and frameworks, with a focus on performance and scalability in Just-In-Time (JIT) compilation, Autograd, and XLA (JAX).</li>
<li>Build and maintain large-scale simulation platforms and end-to-end research pipelines to run experiments on Google DeepMind&rsquo;s cutting-edge infrastructure, including massive TPU pods.</li>
<li>Partner deeply with Research Scientists to transform mathematical concepts and research hypotheses into robust, production-quality code and reproducible experiments.</li>
<li>Lead the engineering direction for complex research projects, establish best practices for code quality and maintainability, and mentor junior engineers on the team.</li>
<li>Optimize every part of the research workflow, from data processing and model training to results analysis, to accelerate the pace of discovery.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Research Engineer, Multi Agent Learning, DeepMind

Search 10000+ Research software engineer jobs near you this July. New roles added regularly across leading industries. Apply today.