{"id":1601869,"url":"https://alion.io/job/lyft-machine-learning-engineer-safety-and-customer-care-ai","title":"Machine Learning Engineer, Safety and Customer Care AI","company":{"id":234,"name":"Lyft","domain":"lyft.com","url":"https://alion.io/company/lyft","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":82,"open_postings":18,"ghost_share":0,"stale_share":0.722,"repost_share":0,"time_to_fill_p50_days":42,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":73000,"max_usd":167000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Synthetic Data","optional":false},{"name":"LangChain","optional":true},{"name":"PEFT","optional":true}],"status":"live","first_seen_at":"2026-08-06T04:09:50Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-28T20:17:19Z","board_verified":true,"closed_at":null,"days_open":83,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":83},"description":"At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.\nThe Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection. \nAgentic AI is at the center of how we scale that mission. We fine-tune and align open-source models, build AI-powered support agents, and develop end-to-end AI agents for safety case management, systems that reason over complex, high-stakes cases and drive them to resolution. SCC brings together ML, data, backend, and product engineers alongside data scientists and operations partners to transform these systems.\nAs a Machine Learning Engineer on the SCC team, you will fine-tune and align models and build AI Agents that power how riders and drivers get help. Your work spans the full loop: post-training open-source models for our domain, composing them into multi-step agents, and building the evaluation that proves they are safe to ship in a customer-facing, safety-critical setting.\nPost-train and adapt open-source LLMs for SCC use cases using SFT, LoRA, and preference-tuning methods (RLHF, RLAIF, RLVR).\nDesign and build AI-powered support agents and end-to-end agents for safety case management using LangGraph or equivalent agentic frameworks.\nOwn the evaluation data flywheel, offline and online, that defines what \"good\" looks like and build benchmarks for the team to hill-climb.\nTurn interaction feedback into training data and learning signals, closing the data flywheel that continuously improves the models.\nResponsibilities:\nConduct literature review and build post-training framework and lifecycle. Curate and process human and synthetic data for SFT/LoRA/RLHF/RLAIF/RLVR, and iterate on model quality for real support and safety tasks.\nDevelop, evaluate, and productionize AI agents, designing tools, state, and control flow in LangGraph (or equivalent) and taking them through the full agent development lifecycle.\nBuild and scale evaluation frameworks, golden sets, rubric-based grading, LLM-as-judge where appropriate, and regression testing.\nShip models and agents into real-time production, with the monitoring and guardrails needed to operate them safely at millions of interactions a month.\nApply traditional ML (classification, ranking, gradient-boosted trees) where it's the right tool, and partner with product, ops, and data science to scope problems and define success metrics.\nExperience:\n3+ years of industry experience in applied ML/AI, inclusive of an MS or PhD in Computer Science, Machine Learning, Artificial Intelligence or a related technical field.\nPost-training experience with open-source models. Hands-on familiarity with fine-tuning and preference-tuning paradigms such as SFT, LoRA, RLHF, RLAIF, and RLVR.\nAgentic development experience. Built and shipped agents with LangGraph or equivalent frameworks, and comfort with the full agent development lifecycle.\nExperience with AI/LLM evaluation. Designed metrics and built offline/online evaluation for generative systems.\nExperience deploying ML/AI applications to real-time production use cases.\nStrong programming skills in Python and hands-on experience with PyTorch.\n Preferred:Experience applying ML/AI to customer support or trust & safety workflows - agent assist, routing, resolution recommendation, or abuse/safety detection.\nPhD in Computer Science, Machine Learning, Statistics, or a related technical field.\nPublications at top-tier peer-reviewed research venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR).\n\nBenefits:\nExtended health and dental coverage options, along with life insurance and disability benefits\nMental health benefits\nFamily building benefits\nChild care and pet benefits\nAccess to a Lyft funded Health Care Savings Account\nRRSP plan with company match to help save for your future\nIn addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service \nLyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.\nSubsidized commuter benefits and Lyft ride credits\nLyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.\nLyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule - Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid\nThe expected base pay range for this position in the Toronto area is CAD $118,800 - CAD $148,500, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.\nLyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.\nThis job fills an existing vacancy.","description_format":"text","description_chars":6732,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity","Life insurance"],"hiring_locations":[{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Shared Mobility","Ride Hailing"],"lifecycle":[{"event":"open","at":"2026-10-01T19:31:50Z"}],"visa":[],"liveness":{"score":15,"band":"cold","label":"Long shot","p_open":1,"p_active":0.404,"p_room":0.36,"age_days":83,"expected_fill_days":42,"reasons":["conf:3","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-28T05:59:10Z"},"pay":null,"html_url":"https://alion.io/job/lyft-machine-learning-engineer-safety-and-customer-care-ai","json_url":"https://alion.io/job/lyft-machine-learning-engineer-safety-and-customer-care-ai.json","meta":{"generated_at":"2026-10-28T22:14:01Z","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":30052,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-29T00:00:00Z"}},"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":234},"rest":"https://alion.io/mcp/rest/get_company?id=234"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Flyft-machine-learning-engineer-safety-and-customer-care-ai"},{"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%2Flyft-machine-learning-engineer-safety-and-customer-care-ai"},{"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%2Flyft-machine-learning-engineer-safety-and-customer-care-ai"},{"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/lyft-machine-learning-engineer-safety-and-customer-care-ai\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Flyft-machine-learning-engineer-safety-and-customer-care-ai"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":1601869}},{"tool":"search_jobs","arguments":{"company":"lyft.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":1601869},"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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