{"id":3575426,"url":"https://alion.io/job/slash-digital-machine-learning-engineer-audio-llm-stack","title":"Machine Learning Engineer (Audio & LLM Stack)","company":{"id":4375690,"name":"Slash Digital","domain":"slash-digital.com","url":"https://alion.io/company/slash-digital","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Personio","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pydantic","optional":false},{"name":"Python","optional":false},{"name":"Qwen","optional":false},{"name":"vLLM","optional":false},{"name":"FastAPI","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-10-31T18:28:14Z","employer_posted_date":null,"last_verified_at":"2026-10-31T18:28:14Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Category: Technology \n\nLocation: \n\nAbout Slash\n\nSlash is a hi-tech startup studio with a mission to build tech AI-powered products and scalable digital platforms that create real-world impact. Since 2016, we’ve partnered with ambitious enterprises and government organizations to design, engineer, and launch cutting-edge solutions — with Generative AI at the core of what we do.\nWe specialize in AI-powered application delivery, from product design and high-performance engineering to DevOps and AI operations. Headquartered in Singapore, our global team and clients operate R&D hubs across Southeast Asia. We are a team of entrepreneurs, engineers, and product builders dedicated to solving complex technical challenges and turning bold ideas into impactful technology.\nAbout our Client\n\nOur client’s product is an AI voice-sensing device, a breakthrough wearable that detects the gap between what someone says and how their voice actually sounds. Rooted in Pythagorean acoustic physics and the Navarasa framework, the system functions as a state detector rather than a conventional emotion labeler. The team is a small, fast-moving team building at the intersection of emotion labeling, ancient wisdom, and measurable science.\nAbout the Role\n\nWe are looking for a hands-on Machine Learning Engineer to take complete ownership of our full AI stack. Your primary responsibility will be expanding our speech emotion recognition (SER) model into a physics-based harmonic vocal state engine, alongside building and maintaining our dual-instance production LLM infrastructure. In this role, you will work directly with the Founder and Lead Developer with zero bureaucracy or committee oversight. We need an engineer who excels at owning problems end-to-end.\nKey Responsibilities\n\n1. Harmonic Vocal State Engine (Audio & Physics)\n\nExtend our inherited 30-class speech emotion recognition (SER) model, dataset, checkpoints, and pipeline into a physics-based harmonic detection system using Fourier-derived acoustic analysis and the Navarasa framework.\n\nDesign and implement an in-house model validation methodology from scratch using approaches like Gemini's emotion labeling API, cross-validation on open datasets (IEMOCAP, RAVDESS), or custom ground-truth evaluation pipelines.\n\n2. LLM Infrastructure & Operations\nDeploy and manage a dual-instance vLLM setup on GCP (g2-standard-24 instance): GPU 0: Llama 3.1 8B for fast-lane prompts; GPU 1: Qwen 2.5 32B for reasoning-heavy prompts.\n\nOwn prompt engineering, output validation, Pydantic schema enforcement, retry logic, and quality monitoring across 42 production prompts.\n\nHandle infrastructure scaling and migrations independently without reliance on third-party vendors.\n\n3. Model Quality & Continuous Improvement\n\nBuild evaluation pipelines to detect and catch regressions before users experience them.\n\nContinuously optimize and refine model performance as real-world audio accumulates from our device.\n\nRequirements\nRequirements & Qualifications\n\nAudio Signal Processing:Strong proficiency in the frequency domain (FFT, Fourier analysis, spectral features) and practical experience with audio processing libraries like librosa or torchaudio.\n\nAudio ML Frameworks:Hands-on experience with modern audio ML models (wav2vec2, HuBERT, WavLM) and a deep understanding of training, evaluating, and fine-tuning SER models.\n\nCore Tech Stack:\nPyTorch & Python:Direct experience handling model checkpoints and training loops; ability to write clean, modular, maintainable production-level code.\n\nInfrastructure & MLOps:\n\nGCP & vLLM:Experience managing cloud infrastructure and direct experience deploying and serving models via vLLM.\n\nMethodology & Communication:Proven ability to design rigorous evaluation frameworks and strong written English skills for an async-first team.\n\nNice to Have\n\nExperience designing or building physics-based audio analysis systems.\n\nFamiliarity with the Navarasa framework, acoustic physics, or classical music/acoustic theory systems.\n\nExperience using FastAPI for inference API layers.\n\nPrior experience replacing or migrating away from third-party AI benchmarks or providers.\n\nGenuine personal interest in sound, acoustic physics, consciousness, or the intersection of emotions and modern ML.\n\nHiring Process (Take-Home Assessment)\n\nWe do not conduct whiteboard interviews. Our technical evaluation is a 48-Hour Practical Assessment consisting of two deliverables:\n\nSER Model Assessment:Review our inherited SER validation report and dataset documentation, providing your strategy for validation.\n\nArchitecture Assessment:Provide a 1-page technical evaluation of a Python file from our harmonic engine, detailing its architecture and roadmap.\n\nDetailsOriginally posted on Himalayas","description_format":"text","description_chars":4759,"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":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Web Design","UI/UX Design"],"lifecycle":[{"event":"open","at":"2026-10-31T20:28:30Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":2,"expected_fill_days":27,"reasons":["seen:2","win:early"],"computed_at":"2026-11-03T05:54:40Z"},"pay":null,"html_url":"https://alion.io/job/slash-digital-machine-learning-engineer-audio-llm-stack","json_url":"https://alion.io/job/slash-digital-machine-learning-engineer-audio-llm-stack.json","meta":{"generated_at":"2026-11-03T17:54:35Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":24890,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-04T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4375690},"rest":"https://alion.io/mcp/rest/get_company?id=4375690"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fslash-digital-machine-learning-engineer-audio-llm-stack"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fslash-digital-machine-learning-engineer-audio-llm-stack"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fslash-digital-machine-learning-engineer-audio-llm-stack"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/slash-digital-machine-learning-engineer-audio-llm-stack\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fslash-digital-machine-learning-engineer-audio-llm-stack"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3575426}},{"tool":"search_jobs","arguments":{"company":"slash-digital.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3575426},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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