{"id":1730974,"url":"https://alion.io/job/mlb-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":678408,"name":"MLB","domain":"mlb.com","url":"https://alion.io/company/mlb-5","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","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":"2026-10-03T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":180000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-03-18T00:00:00Z","employer_posted_date":"2026-03-18","last_verified_at":"2026-10-04T00:53:55Z","board_verified":true,"closed_at":null,"days_open":200,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":199},"description":"Job Description:\nSummary\nThe New York Mets are seeking an Applied AI Engineer to build and ship production-grade AI capabilities that improve decision-making and streamline workflows across Baseball Operations. This role sits at the intersection of Baseball Systems (software engineering), Data Engineering, Baseball Analytics, Performance Technology, and modern generative AI. You will develop reliable AI-powered applications such as retrieval-augmented generation (RAG), secure tool/data integrations (MCP-style patterns), and agentic workflows that can support deep work and research, while ensuring strong standards for quality, security, privacy, and operational excellence.\nThis is a hands-on role for an engineer who can translate ambiguous baseball operations and player development needs into working software, iterate quickly with stakeholders, and harden solutions into durable systems used daily by analysts, coaches, scouts, and baseball operations staff.\nNote: This role will require extensive in-person collaboration and innovation alongside stakeholders, analysts, and engineers. Applicants must be local to NYC (or willing to relocate) and be able to travel to Citi Field regularly. Travel to Spring Training and affiliates may also be required.\nEssential Duties & Responsibilities\nDesign, build and maintain AI-powered product experiences that provide intuitive access to baseball information and statistics and workflows across internal systems.\n\nDevelop end-to-end RAG pipelines (ingest, chunking, embedding, retrieval, generation) with strong attention to answer quality, baseball relevancy, analytics accuracy, latency, and cost.\n\nImplement secure tool and data connectors for AI assistants and agents using standardized patterns (MCP-style), enabling safe interaction with internal APIs, data warehouses, and services.\n\nBuild agentic workflows that can execute multi-step tasks (research, analysis, synthesis, report generation) with clear guardrails, auditability, and human-in-the-loop approvals.\n\nPartner closely with Product, Analytics, Performance Technology, Player Development, and Baseball operations stakeholders to frame problems, define success, and deliver measurable impact.\n\nContribute to shared engineering standards and reusable components so AI capabilities scale across multiple products and teams.\n\nEnsure strong security and privacy practices (access control, data boundaries, logging and auditing, and safe handling of sensitive information).\n\nParticipate in high-availability support expectations as needed during critical operational periods throughout the baseball season, and during feature releases.\n\nQualifications\nBachelor’s degree in Computer Science or equivalent practical experience.\n\n5+ years of professional software engineering experience, including building and operating production services.\n\nDemonstrated experience building LLM-powered applications in production (prompt/program design, structured outputs, tool calling, reliability patterns).\n\nPractical experience with retrieval systems (search, embeddings, vector databases, ranking, chunking/indexing) and applying them to real workflows.\n\nStrong experience with modern backend and data integration fundamentals: APIs, SQL, distributed systems, and cloud infrastructure (GCP/AWS/Azure)\n\nProficiency in one or more production languages commonly used for AI applications and services (e.g. Python and/or Typescript)\n\nApproach work as highly collaborative and stakeholder-driven, with the ability to operate in ambiguity while iterating quickly.\n\nStrong written and verbal communication skills with the ability to explain tradeoffs to technical and non-technical audiences.\n\nExperience working in sports, health, finance, or other high-stakes analytics-heavy environments, preferred.\n\nStrong interest in baseball and comfort working within the culture of a professional sports organization, preferred\n\nThe above information is intended to describe the general nature, type, and level of work to be performed. The information is not intended to be an exhaustive or complete list of all responsibilities, duties, and skillsrequiredfor this position. Nothing in this job description restricts management’s right to assign or reassign duties and responsibilities to this job at any time.The individual selected may perform other related duties as assigned or requested.\nThe New York Mets value the unique qualities individuals with various backgrounds and experiences can offer the organization. Our continued success depends heavily on the quality of our workforce. The Organization is committed to providing employees with the opportunity todevelop totheir fullest potential.\nSalary Range: $180,000 - $200,000\nFor technical reasons, we strongly advise to not use an .edu email address when applying. Thank you very much.","description_format":"text","description_chars":4832,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-03T00:07:13Z"}],"visa":[],"liveness":{"score":9,"band":"cold","label":"Long shot","p_open":1,"p_active":0.308,"p_room":0.28,"age_days":199,"expected_fill_days":41,"reasons":["conf:5","win:tail","crowd:"],"computed_at":"2026-10-03T05:45:00Z"},"pay":{"stated_usd_annual":200000,"is_top_pay":false},"html_url":"https://alion.io/job/mlb-applied-ai-engineer","json_url":"https://alion.io/job/mlb-applied-ai-engineer.json","meta":{"generated_at":"2026-10-04T01:09:06Z","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":1476,"day_limit":5000,"remaining_today":3524,"minute_limit":60,"resets_at":"2026-10-05T00:00:00Z"}}}