{"id":2581384,"url":"https://alion.io/job/mecka-research-scientist-multimodal-foundation-models","title":"Research Scientist – Multimodal Foundation Models","company":{"id":678757,"name":"Mecka","domain":"mecka.ai","url":"https://alion.io/company/mecka","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":88,"open_postings":10,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-10-30T05:48:23Z"}},"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":["Toronto, Canada","New York, United States","San Francisco, United States"],"countries":["CA","US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":185000,"max":210000,"currency":"USD","period":"year","gross":null,"usd_annual":210000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DPO","optional":false},{"name":"Embodied AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GRPO","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Post-training","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Reward Modeling","optional":true}],"status":"live","first_seen_at":"2026-10-15T01:15:04Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-30T13:57:36Z","board_verified":true,"closed_at":null,"days_open":15,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":15},"description":"About Mecka AI\nMecka AI is building the data infrastructure layer for robotics and embodied AI.\nWe design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.\nWe work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.\nThe Role\nWe're looking for an experienced Research Scientist to advance the capabilities of large-scale vision-language and multimodal foundation models.\nYou'll work on challenging research problems spanning model training, post-training, reinforcement learning, and multimodal reasoning. This is a hands-on role for someone who enjoys experimenting with cutting-edge approaches, improving model performance, and translating research breakthroughs into real-world capabilities.\nYou'll have the opportunity to work across the model development lifecycle, from data and training strategies to evaluation and optimization, helping push the boundaries of what multimodal AI systems can understand and accomplish.\nWhat You'll Do\nMultimodal Model Research: Research, develop, and improve vision-language models and multimodal foundation-model architectures.\n\nPost-Training & Reinforcement Learning: Design and implement advanced training strategies, including supervised fine-tuning, preference optimization, and reinforcement learning, to improve model capabilities.\n\nModel Performance & Optimization: Investigate approaches to improve reasoning, generalization, reliability, and training efficiency.\n\nData Quality & Training: Develop strategies for data curation, filtering, and synthetic data generation to improve training outcomes.\n\nEvaluation & Benchmarking: Design experiments, evaluation frameworks, and benchmarks to measure model performance, identify failure modes, and guide research priorities.\n\nLarge-Scale Experimentation: Work with distributed training infrastructure and large-scale datasets to support research and model development.\n\nResearch Innovation: Explore emerging approaches in multimodal learning, model alignment, and foundation-model training.\n\nWhat You Bring\nMust Have\nHands-on experience researching, training, fine-tuning, or post-training large-scale foundation models, particularly vision-language or multimodal models.\n\nStrong understanding of deep learning architectures, optimization techniques, and modern model training methodologies.\n\nPractical experience with supervised fine-tuning (SFT), preference optimization, or reinforcement learning approaches such as DPO, GRPO, PPO, or RLHF.\n\nStrong proficiency in Python and PyTorch, JAX, or comparable deep learning frameworks.\n\nExperience designing, executing, and analyzing large-scale machine learning experiments.\n\nFamiliarity with distributed training systems and GPU-based model development.\n\nStrong research instincts, analytical problem-solving skills, and the ability to independently drive complex technical projects.\n\nExperience working with open-weight multimodal foundation models.\n\nResearch publications on top-tier journals/conferences, open-source contributions, or experience within leading AI research organizations.\n\nNice to Have\nExperience with large-scale model post-training and reinforcement learning for reasoning or alignment.\n\nFamiliarity with multimodal data pipelines involving images, video, or other sensory inputs.\n\nExperience with reward modeling, synthetic data generation, or advanced model evaluation.\n\nExperience improving model capabilities through innovations in training strategies, data quality, or model architecture.\n\nA Note on Applying\nStudies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%. If you don't check every box above but believe you can do the job, we encourage you to apply - we're looking for capability and trajectory, not a perfect checklist match.\nInclusive Hiring at Mecka\nWe are committed to creating an inclusive and supportive candidate experience. Should you require any accommodation whatsoever during the interview process, please inform us without any hesitation. Mecka is dedicated to ensuring equal treatment and opportunity in all phases of recruitment, selection, and employment, in compliance with employment law. We do not discriminate based on gender, race, religion, national origin, ethnicity, disability, gender identity/expression, sexual orientation, veteran or military status, or any other protected category. Mecka is proud to be an equal opportunity employer, fostering a culture of inclusivity and maintaining a work environment that is free from discrimination, harassment, and retaliation.\nUse of Artificial Intelligence in Recruitment\nMecka uses artificial intelligence (AI) responsibly to support administrative and efficiency-focused aspects of our recruitment process. This includes activities such as drafting job descriptions, generating interview questions, note-taking and recordings, and supporting sourcing and scheduling workflows. All candidate evaluations, interviews, and hiring decisions are made by members of the Mecka team. While AI tools may assist with screening and assessment, they do not replace human judgment in selection decisions. Our use of AI is intended to streamline routine tasks, improve consistency, and enhance the overall candidate experience. We are committed to upholding principles of fairness, transparency, and accountability in all hiring activities. 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