{"id":1981296,"url":"https://alion.io/job/nvidia-machine-learning-engineer-ai-safety-2","title":"Machine Learning Engineer, AI Safety","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":88,"open_postings":346,"ghost_share":0.014,"stale_share":0.434,"repost_share":0.032,"time_to_fill_p50_days":29,"computed_at":"2026-10-07T05:47:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States","United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":124000,"max":195500,"currency":"USD","period":"year","gross":null,"usd_annual":195500},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Keras","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-06T23:41:18Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-08T01:15:38Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"NVIDIA is in a unique position: we are developing AI-based products across multiple domains, and we collaborate with many interesting AI companies as partners and customers. Ensuring the highest Content Safety possible reduces exposure to inappropriate material. Preventing Bias and Discrimination is essential to both protect individual rights and achieve the best quality of results, including accuracy and completeness of information. By prioritizing safety and fairness, we can ensure that LLMs benefit everyone and contribute to a better future for all.\nOur team also works in the area of safety for generative models for language, robustness, and explainability. Our LLMs are a growing area of AI products, including models and services, and we are committed to ensuring that they are used safely and responsibly. We are looking for a talented Machine Learning Engineer to work on Product Security, Content Safety, ML Fairness and Robustness efforts for LLMs across all of our research and production engineering teams. In this role, you’ll have the opportunity to take on innovative problems in machine learning, particularly focused on safety for multi-modal LLMs. This role is directed at assessing, quantifying, and improving the safety and inclusivity of our LLM models in a scalable fashion. \nWhat you'll be doing:\nDevelop the datasets and models for training and evaluating models and end-to-end systems for Content Safety, ProdSec, Robustness and ML Fairness.\n\nResearch and implement cutting-edge techniques for bias detection and mitigation in LLMs and systems using LLMs like RAGs.\n\nDefine and track key metrics for responsible LLM behavior and usage.\n\nFollow the best MLOps practices of automation, monitoring, scale and safety.\n\nContribute to the MLOps platform and develop safety tools to help ML teams be more effective.\n\nCollaborate with other engineers, data scientists, and researchers to develop and implement solutions to content safety and ML fairness challenges.\n\nWhat we need to see:\nMaster’s or PhD in Computer Science, Electrical Engineering or related field - or equivalent experience.\n\nMinimum of 2+ years of work experience in developing and deploying machine learning models in production.\n\nStrong understanding of machine learning principles and algorithms.\n\nHands-on programming experience in python and in-depth knowledge of machine learning frameworks, like Keras or PyTorch.\n\nBackground in one or more of the following broader areas for 1+ years: Content Safety, ML Fairness, Robustness, AI Model Security, or related areas.\n\nExperience working in a range of the following areas within Content Safety: Hate/Harassment, Sexualized, Harmful/Violent, or other specific areas from your application.\n\nPractice working with large multi-modal datasets and multi-modal models.\n\nGood at problem-solving and analytical ability.\n\nExcellent collaboration and communication skills.\n\nDemonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty.\n\nWays to stand out from the crowd:\nSkilled with alignment/fine-tuning of LLMs - including regular LLMs as well as VLMs (vision-language models) or any-to-text\n\nProven experience with multimodal and/or multilingual content safety, legal, and regulatory compliance.\n\nKnowledge of robustness, including hallucinations, digressions, and generative misinformation.\n\nExperience with GenAI security, including prompt stability, model extraction, confidentiality/data extraction, integrity, availability, and adversarial robustness.\n\nPassion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research, and publication experience.\n\nWith highly competitive salaries and a comprehensive benefits package, Nvidia is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us and our engineering teams are growing fast in some of the hottest state of the art fields: Deep Learning, Artificial Intelligence, and Large Language Models. If you're a creative engineer with a real passion for robust and enjoyable user experiences, we want to hear from you.\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.You will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until October 10, 2026.This posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.","description_format":"text","description_chars":5117,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Servers & Data Center Hardware","Computer Components","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-06T23:41:18Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":29,"reasons":["conf:3","velocity","win:early","comp:junior,brand"],"computed_at":"2026-10-07T05:47:15Z"},"pay":{"stated_usd_annual":195500,"is_top_pay":false},"html_url":"https://alion.io/job/nvidia-machine-learning-engineer-ai-safety-2","json_url":"https://alion.io/job/nvidia-machine-learning-engineer-ai-safety-2.json","meta":{"generated_at":"2026-10-08T01:41:06Z","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","counted_by":"address","units_charged":1,"used_today":2938,"day_limit":5000,"remaining_today":2062,"minute_limit":60,"resets_at":"2026-10-09T00:00:00Z"}},"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":6},"rest":"https://alion.io/mcp/rest/get_company?id=6"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fnvidia-machine-learning-engineer-ai-safety-2"},{"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%2Fnvidia-machine-learning-engineer-ai-safety-2"},{"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%2Fnvidia-machine-learning-engineer-ai-safety-2"},{"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/nvidia-machine-learning-engineer-ai-safety-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fnvidia-machine-learning-engineer-ai-safety-2"}]}