{"id":2607207,"url":"https://alion.io/job/maple-ml-research-engineer","title":"ML Research Engineer","company":{"id":4417873,"name":"Maple","domain":"maple.inc","url":"https://alion.io/company/maple-5","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"D","score":40,"open_postings":14,"ghost_share":1,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":120000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"JAX","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Speech Recognition","optional":false},{"name":"Text-to-Speech","optional":false},{"name":"TPU","optional":false},{"name":"Triton","optional":false},{"name":"Voice Agents","optional":false}],"status":"live","first_seen_at":"2025-04-13T05:30:31Z","employer_posted_date":"2025-04-13","last_verified_at":"2026-10-16T03:58:44Z","board_verified":true,"closed_at":null,"days_open":551,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":550},"description":"Hi I’m Aidan, founder of Maple.\nAt Maple, we’re building AI agents that work for restaurants. These agents answer calls, take orders, book appointments, and handle real customer interactions over natural voice.\nBut our bigger mission goes deeper: we’re building automated ontologies that model how businesses actually operate - their services, workflows, constraints, and language - so our agents can adapt to them instantly. We meet businesses where they are, not where software wants them to be.\nWe have many customers, strong revenue growth, years of runway, and backing from world-class investors. I’ll share more once we meet.\nAbout the Role\nAs an ML Research Engineer at Maple, you'll be a part of our core product team transforming cutting-edge research into production-ready voice agents, serving millions of interactions for local businesses. Collaborate with experts from Google Brain, Two Sigma, Stanford, MIT, Columbia, and IBM, rapidly deploying advanced models and systems that directly impact small businesses.\nWe work in person, 5 days a week in our NYC office. Collaboration here is fast, noisy (in the best way), and high-trust. We move quickly, break things intentionally, and fix them just as fast.\nWhat You'll Do\nOptimize speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS) for real-world use, ensuring accuracy in diverse, noisy environments.\n\nFine-tune LLMs with retrieval-augmented generation (RAG), reinforcement learning (RL), and prompt engineering for dynamic, context-aware conversations.\n\nIntegrate AI components into autonomous agents capable of complex tasks like scheduling, order-taking, and issue resolution.\n\nCreate human-in-the-loop and automated systems to monitor performance, detect anomalies, and continuously improve models from real-world feedback.\n\nDevelop pipelines to construct knowledge graphs from business data, powering adaptive AI interactions.\n\nWork with infrastructure teams to scale models efficiently across GPU/TPU clusters and edge devices, minimizing latency.\n\nManage rapid experimentation, training, and highly optimized production inference.\n\nLead evaluations, error analysis, and iterative improvements to maintain robustness and scalability.\n\nBalance research innovation with practical usability by closely working with product and customer teams.\n\nPublish research, contribute to open-source, and present at industry-leading conferences.\n\nWhat We're Looking For\n3-7+ years deploying impactful ML models, ideally in voice, NLP, knowledge graphs, or agent systems.\n\nDeep knowledge in speech recognition, language models, RL/dialogue systems, TTS, ontology systems, or agent orchestration.\n\nProficiency in PyTorch or JAX; optimization experience with CUDA/Triton preferred.\n\nProven ability to minimize latency and resource use on GPUs/TPUs or edge hardware.\n\nStrong data-driven approach with measurable improvements.\n\nPassion for creating intuitive, helpful, and frustration-free AI experiences.\n\nBS, MS, or PhD in Computer Science, Electrical Engineering, Mathematics, or equivalent practical expertise.\n\nHow we work\nWe optimize for leverage. That means great internal tooling, fast CI/CD, and code that scales across many customer types\n\nWe believe in deep ownership. Engineers here talk to users, design features, and ship fast\n\nWe value clarity over process. You’ll spend most of your day building, not waiting on decisions\n\nWe move in person. We’re a tight-knit team that moves fast and solves problems together\n\nWhat we offer\nCompetitive salary + meaningful equity\n\nA real product with real usage and growing revenue\n\nStrong In-person culture, fast feedback loops, and zero bureaucracy\n\nA small team that feels like a founding team\n\nFull health, dental, vision, 401k, life insurance, and unlimited PTO\n\nTools budget, coffee budget, whatever-you-need-to-be-great budget\n\nWant to help reimagine how software works for real-world businesses? 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