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If applying ML / AI in production to improve the relevance of Reddit Notifications excites you, then you’ve found the right place.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead the team that architects and designs notifications relevance at Reddit.</li>\n<li>Guide team on holistic, adaptive systems covering budgeting optimization, candidate retrieval, and ranking.</li>\n<li>Work with ML engineers to design, implement, and optimize machine-learning models that drive personalization and user re-engagement.</li>\n<li>Participate in the full development cycle: design, develop, QA, experiment, analyze, and deploy.</li>\n<li>Build and maintain a diverse team that can collaborate across disciplines to find technical solutions to complex challenges.</li>\n<li>Serve as a thought partner to product and upper management to ensure your team’s plans align with company goals.</li>\n<li>Communicate your team’s work and set expectations with cross-functional stakeholders.</li>\n<li>Help your engineers identify career goals and create development plans to achieve them.</li>\n<li>Constantly seek opportunities to push your engineers &amp; managers outside their comfort zone and turn followers into leaders.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>2+ years of experience building and managing engineering teams.</li>\n<li>5+ years of experience as a Machine Learning Engineer or Software Engineer working on large-scale machine learning systems.</li>\n<li>Deep understanding of building and deploying large-scale recommender systems (retrieval + ranking) in production.</li>\n<li>Hands-on experience working with deep learning models, sequential features and real-time systems.</li>\n<li>Experience with distributed training and inference using tools like Ray, PyTorch Distributed, or similar.</li>\n<li>Familiarity with reinforcement learning or multi-objective optimization in recommendation systems.</li>\n<li>Entrepreneurial and self-directed, innovative, results-oriented, biased towards action in fast-paced environments.</li>\n<li>Able to communicate and discuss complex topics with technical and non-technical audiences.</li>\n<li>Able to tackle ambiguous and undefined problems.</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f723a069-05a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7340793","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$230,000-$322,000 USD","x-skills-required":["Machine Learning Engineer","Software Engineer","Deep Learning Models","Sequential Features","Real-Time Systems","Distributed Training","Inference","Reinforcement Learning","Multi-Objective Optimization"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:46:22.742Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning Engineer, Software Engineer, Deep Learning Models, Sequential Features, Real-Time Systems, Distributed Training, Inference, Reinforcement Learning, Multi-Objective Optimization","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":322000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9cac404c-fb9"},"title":"Senior Solutions Architect","description":"<p>We&#39;re seeking a Senior Solutions Architect to bridge our research frontier and customer reality. 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Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 60,000 businesses.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Adapt models for new conditioning inputs (emotion, speed, prosody, speaker control, etc.).</li>\n<li>Fine-tune and optimize speech models using advanced techniques such as DPO (Direct Preference Optimization), LoRA, and other parameter-efficient methods to improve voice quality and expressiveness.</li>\n<li>Implement post-training optimization techniques (quantization, pruning, distillation) to improve efficiency and latency in real-time speech generation.</li>\n<li>Integrate and test novel architectures, such as neural codecs, diffusion, or flow-matching models, to enhance realism and responsiveness.</li>\n<li>Design and implement new evaluation metrics for TTS systems, including automated Mean Opinion Score (MOS) prediction models for continuous quality assessment.</li>\n<li>Stay updated with the latest research in audio diffusion, autoregressive models, neural codecs, and multimodal LLMs.</li>\n</ul>\n<p><strong>What we&#39;re looking for:</strong></p>\n<ul>\n<li>Strong understanding of generative modelling, ideally applied to sequential or multimodal data.</li>\n<li>Hands-on experience with large language models (LLMs) or similar transformer-based architectures.</li>\n<li>High proficiency in PyTorch, including experience with distributed training and model optimization.</li>\n<li>Solid grasp of time-series modelling and tokenization, preferably in the context of audio or speech.</li>\n<li>Demonstrated ability to prototype quickly, test hypotheses, and iterate efficiently.</li>\n<li>Proven experience in training deep learning models end-to-end, from data preparation to evaluation.</li>\n<li>Strong general software engineering skills, enabling contributions to a large, shared research infrastructure.</li>\n</ul>\n<p><strong>Nice-to have experience</strong></p>\n<ul>\n<li>Familiarity with state-of-the-art architectures in audio and speech generation (e.g., diffusion models, neural codecs, flow-matching models, autoregressive decoders).</li>\n<li>Experience with speech-to-speech or text-to-speech (TTS) systems.</li>\n<li>Evidence of original research contributions, such as publications or open-source work in top-tier venues (e.g., ICASSP, Interspeech, NeurIPS, ICML).</li>\n</ul>\n<p><strong>Why join us?</strong></p>\n<p>We’re living the golden age of AI. The next decade will yield the next iconic companies, and we dare to say we have what it takes to become one. Here’s why,</p>\n<p><strong>Our culture</strong></p>\n<p>At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here.</p>\n<p><strong>Serving 50,000+ customers (and 50% of the Fortune 500)</strong></p>\n<p>We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers and what 1,200+ people say on G2.</p>\n<p><strong>Proprietary AI technology</strong></p>\n<p>Since 2017, we’ve been pioneering advancements in Generative AI. 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Learn more about our commitments to AI Ethics, Safety &amp; Security.</p>\n<p><strong>The good stuff...</strong></p>\n<ul>\n<li>Competitive compensation (salary + stock options + bonus)</li>\n<li>Fully remote from Europe or hybrid work setting with an office in London, Amsterdam, Zurich, Munich</li>\n<li>25 days of annual leave + public holidays</li>\n<li>Great company culture with the option to join regular planning and socials at our hubs</li>\n<li>+ other benefits depending on your location</li>\n</ul>\n<p>You can see more about Who we are and How we work here: https://www.synthesia.io/careers</p>\n<p>LI-MD1</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c7b423ab-4c2","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Synthesia","sameAs":"https://www.synthesia.io/","logo":"https://logos.yubhub.co/synthesia.io.png"},"x-apply-url":"https://jobs.ashbyhq.com/synthesia/ae716439-4fb5-49f3-b0fb-4038f65c1b6f","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["generative modelling","large language models (LLMs)","PyTorch","time-series modelling","tokenization","deep learning models","software engineering"],"x-skills-preferred":["state-of-the-art architectures in audio and speech generation","speech-to-speech or text-to-speech (TTS) systems","original research contributions"],"datePosted":"2026-03-06T18:41:14.441Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"generative modelling, large language models (LLMs), PyTorch, time-series modelling, tokenization, deep learning models, software engineering, state-of-the-art architectures in audio and speech generation, speech-to-speech or text-to-speech (TTS) systems, original research contributions"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d446c7ab-5f0"},"title":"Member of Technical Staff, Applied Scientist - Windows Copilot","description":"<p><strong>Summary</strong></p>\n<p>Microsoft are looking for a talented Member of Technical Staff, Applied Scientist - Windows Copilot at their Redmond office. 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