{"id":660817,"url":"https://alion.io/job/goodinside-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":674687,"name":"Good Inside","domain":"goodinside.com","url":"https://alion.io/company/goodinside","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-29T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"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":205000,"max":235000,"currency":"USD","period":"year","gross":null,"usd_annual":235000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Core ML","optional":false},{"name":"ElevenLabs","optional":false},{"name":"Embeddings","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Go","optional":false},{"name":"Hugging Face","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"Recommender Systems","optional":false},{"name":"TypeScript","optional":false},{"name":"CI/CD","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-02-10T20:33:27Z","employer_posted_date":"2026-07-10","last_verified_at":"2026-09-29T06:14:17Z","board_verified":true,"closed_at":null,"days_open":231,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":230},"description":"Who We Are\nGood Inside is redefining parenting - not as something that should “just come naturally,” but as a skill to learn and practice. Founded by Dr. Becky Kennedy and Dr. Erica Belsky, we combine sturdy leadership with innovative technology to give parents personalized guidance, AI-powered support, and a global community.\nOur mission: help parents raise resilient, confident kids in a changing world. We’ve already reached millions, and we’re just getting started. We’re refining our product and expanding our reach to empower even more families.\nWe’re looking for bold, high-ownership problem-solvers who want to build something new, tackle big challenges, and be at the forefront of change.\nThe Opportunity\nGood Inside is seeking a Machine Learning Engineer to join our Engineering team. This is not a research or data science role - we’re looking for a strong backend engineer who has hands-on experience shipping ML-powered features in production. You’ll work at the intersection of backend systems and machine learning, building the infrastructure and services that bring personalized, intelligent experiences to our users.\nYou should be comfortable working with ML APIs, understanding core ML concepts, and integrating models into reliable, scalable backend systems. Your primary identity is as a software engineer - someone who writes clean, production-grade code - with the added ability to reason about ML systems and bring them to life in our product.\nYou will collaborate closely with cross-functional partners, including product, design, mobile, and data teams, to build high-quality features that serve our users’ needs. Your ability to blend backend engineering excellence with practical ML knowledge will be essential as we continue to evolve and scale the Good Inside platform.\nWhat You’ll Own\nDesign, build, and maintain backend services and APIs that power ML-driven features across the Good Inside platform\nIntegrate and orchestrate ML models and third-party ML APIs (e.g., LLM providers, recommendation engines, embeddings services) into production systems\nBuild data pipelines and infrastructure to support model serving, feature storage, and real-time personalization\nCollaborate closely with product, mobile, and design teams to translate ML capabilities into user-facing features\nOwn the reliability, performance, and scalability of ML-adjacent backend systems\nDevelop clean, maintainable, and well-documented code aligned with defined project scope\nProvide clear documentation of architectural decisions, implementation details, and handoff materials upon project completion\nProvide input on feature scope and sequencing to support timely and successful delivery of project deliverables\nYour Skills and Experience\n5+ years of professional software engineering experience, with a strong focus on backend development\nDemonstrated experience shipping ML-powered features or products in a production environment\nWorking knowledge of ML concepts (e.g., embeddings, classification, recommendation systems, LLMs) - you don’t need to train models, but you need to understand how they work and when to use them\nHands-on experience integrating ML APIs and services (e.g., OpenAI, Anthropic, ElevenLabs, HuggingFace, AWS SageMaker, or similar)\nProficiency in Python and/or another backend language (Go, Java, TypeScript/Node, etc.)\nExperience with cloud infrastructure (AWS, GCP, or Azure) and containerized deployments\nFamiliarity with data stores and pipelines relevant to ML workloads (e.g., vector databases, feature stores, streaming systems)\nExcellent interpersonal, verbal, and written communication skills\nStrong collaboration abilities and cross-functional relationship-building\nSelf-starter with strong analytical and problem-solving skills\nAbility to stay organized and deliver results in a fast-paced, changing environment\nComputer Science degree or equivalent\nAt least 2 years of experience in house as a ML Engineer \nPreferred Experience\nStartup Growth Experience: This isn’t your first time helping a high-growth startup scale. You are excited by the challenge and love creating and learning from the bottom up.\nExperience with LLM Application Development: You’ve built applications on top of large language models - prompt engineering, RAG pipelines, conversational AI, or similar - and understand the practical challenges of shipping LLM-powered features.\nInfrastructure & DevOps Fluency: Experience with CI/CD, monitoring, observability, and production-readiness for ML systems.\nPrior experience with recommendation systems, personalization engines, or content ranking algorithms in a user-facing product.\nWhat We Offer\nCompetitive Compensation: base salary for this role will be $205k - $235k \nCompany Equity\nComprehensive benefits package\n401k + Company match\nTime off to recharge\nA high-ownership, high-performance, high-collaboration culture\nEqual Employment Opportunity\nGood Inside is an equal opportunity employer and as such, we do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by applicable laws.\nWe are dedicated to growing a diverse team of highly talented people. As much as we believe in focusing on the parent behind the parenting and the child behind the behavior, we believe in focusing on the personbehind the job. We’re dedicated to building a workplace where we give each other the strategies, support, and space we each need to thrive-believing in and bringing out the good inside of everyone..\nIf you require any accommodations during the recruitment process, whether it be alternate forms of material, accessible meeting rooms, etc., please let us know and we will work with you to meet your needs.\nFor information about Good Inside's privacy practices, see our Privacy Policy. California applicants, please also see our CA Applicant Privacy Notice.","description_format":"text","description_chars":5933,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["401k plan","Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-10T21:30:19Z"}],"liveness":{"score":7,"band":"cold","label":"Long shot","p_open":1,"p_active":0.257,"p_room":0.28,"age_days":230,"expected_fill_days":30,"reasons":["conf:16","win:tail","crowd:"],"computed_at":"2026-09-29T05:45:00Z"},"pay":{"stated_usd_annual":235000,"is_top_pay":true},"html_url":"https://alion.io/job/goodinside-machine-learning-engineer","json_url":"https://alion.io/job/goodinside-machine-learning-engineer.json","meta":{"generated_at":"2026-09-30T03:14:51Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2393,"day_limit":5000,"remaining_today":2607,"minute_limit":60,"resets_at":"2026-10-01T00:00:00Z"}}}