{"id":720976,"url":"https://alion.io/job/whitecircle-multimodal-ml-engineer","title":"Multimodal ML Engineer","company":{"id":720703,"name":"Whitecircle","domain":"whitecircle.com","url":"https://alion.io/company/whitecircle-2","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":93,"open_postings":13,"ghost_share":0,"stale_share":0.077,"repost_share":0,"time_to_fill_p50_days":107,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Paris, France","London, United Kingdom"],"countries":["FR","GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":58000,"max_usd":124000,"period":"year","method":"role_country_seniority_unknown","sample_n":121},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"DeepSpeed","optional":false},{"name":"DPO","optional":false},{"name":"FSDP","optional":false},{"name":"GRPO","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLaVA","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Qwen","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Whisper","optional":false},{"name":"Anthropic","optional":true},{"name":"Datadog","optional":true},{"name":"Hugging Face","optional":true},{"name":"OpenAI","optional":true},{"name":"Sentry","optional":true}],"status":"live","first_seen_at":"2026-07-02T14:08:59Z","employer_posted_date":"2026-07-02","last_verified_at":"2026-11-02T16:35:25Z","board_verified":true,"closed_at":null,"days_open":123,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":122},"description":"TLDR: Multimodal ML Engineer to train and ship vision, audio, video, and speech models for an AI safety platform that operates at 100M+ API calls/month.\nAbout us\nWhite Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies - simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.\nWe’ve recently raised our Series A funding round, taking our total funding to $70M. Our investors include top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others\n\nWe process over 100M+ API calls every month\n\nWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model\n\nWe’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built - you’re the one we need.\nYou will\nTrain and fine-tune large-scale multimodal models (vision-language, audio, speech) from scratch and from pretrained checkpoints\n\nExtend models across modalities: image understanding, video temporal modeling, long-context processing, and streaming audio\n\nDesign and run experiments: architecture changes, data mixes, training recipes\n\nBuild and maintain multimodal data pipelines - from raw images, video, and audio recordings to training-ready datasets, including synthetic data generation\n\nTrain and optimize MoE architectures for efficient multimodal inference\n\nBuild alignment pipelines: SFT, DPO, GRPO, reward modeling - across modalities, not just text\n\nOptimize models for production: quantization, distillation, batching, streaming and low-latency serving\n\nDeploy models end-to-end: from research checkpoint to production serving\n\nDefine evaluation metrics and benchmarks that actually matter for the product: visual QA, spatial reasoning, video comprehension, speech and audio understanding\n\nYou’ll fit right in if you\n3+ years training large-scale deep learning models in multimodal domains (vision-language, audio, speech, or acoustic)\n\nStrong PyTorch skills with hands-on distributed training experience (DeepSpeed, FSDP, or similar)\n\nDeep experience with multimodal architectures - you understand how vision/audio encoders, projectors, and LLMs fit together (LLaVA, Qwen-VL, InternVL, Audio Flamingo, Omni Qwen, Audio Qwen, Whisper, HuBERT, Conformer, or similar)\n\nHands-on with RLHF/alignment for multimodal: GRPO, DPO, reward modeling - not just for text\n\nExperience with video and/or audio sequence modeling: temporal modeling, long-context processing, efficient attention, streaming inference\n\nTrack record of shipping models to production: you've hit latency targets and optimized inference, not just reported benchmark scores\n\nComfortable with large-scale multimodal dataset curation: image-text pairs, video-instruction data, audio preprocessing, augmentation, synthetic data generation\n\nFamiliar with MoE architectures and their tradeoffs for multimodal workloads\n\nStrong engineering fundamentals: clean code, version control, testing, documentation\n\nA big plus\nUnderstanding of audio signal processing fundamentals (spectrograms, mel features, noise reduction)\n\nWhy White Circle\nCompetitive compensation package, including equity\n\nFlexible time off\n\nPaid time off in line with your local regulations, no matter where you work from\n\nWork from Paris or London (hybrid) + relocation package for Paris\n\nBest medical insurance in France\n\nLearning and development support for courses, conferences, and opportunities to grow your skills\n\nAll the hardware, tools, and services you need\n\nCovered subscriptions for AI agents and IDEs\n\nTeam off-sites twice a year: we’ve recently been to the Alps and to Saint-Tropez\n\nProcess\nIntroductory call with HR (25 min)\n\nTake-home test assignment\n\nTechnical interview with Head of Applied Research (60 min)\n\nFinal conversation with CEO (45 min)","description_format":"text","description_chars":4057,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Flexible time off","Health insurance"],"hiring_locations":[{"name":"France","iso":"FR","kind":"country"},{"name":"United Kingdom","iso":"GB","kind":"country"}],"hiring_excludes":[],"relocation_offered":true,"industries":["AI Safety & 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