{"id":59301,"url":"https://alion.io/job/clay-software-engineer-applied-ai","title":"Software Engineer, Applied AI","company":{"id":1484,"name":"Clay","domain":"clay.com","url":"https://alion.io/company/clay","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":78,"open_postings":9,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":75,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Backend","role_family":"Backend","seniority":null,"employment_type":"full_time","work_mode":"hybrid","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":170000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenAI","optional":false},{"name":"Tool Use","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon Aurora","optional":true},{"name":"Amazon ECS","optional":true},{"name":"AWS","optional":true},{"name":"AWS Fargate","optional":true},{"name":"AWS Lambda","optional":true},{"name":"ClickHouse","optional":true},{"name":"Datadog","optional":true},{"name":"Hybrid Search","optional":true},{"name":"LLM","optional":true},{"name":"OpenSearch","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Python","optional":true},{"name":"RAG","optional":true},{"name":"Redis","optional":true},{"name":"Slack","optional":true},{"name":"Terraform","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-08-14T22:37:36Z","employer_posted_date":"2026-08-14","last_verified_at":"2026-10-17T09:55:46Z","board_verified":true,"closed_at":null,"days_open":63,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":63},"description":"About Clay\nAI is unleashing the biggest wave of company creation in history. Clay's mission is to be the engine those companies use to grow to their full potential.\nClay predicts the next best action for your business and then helps you take it. We started by aggregating the best data for B2B companies. Then, we built the infrastructure to run any personalized campaign on top of it, including emails, ads, landing pages, and workflows that make reps more productive. Now, we're building agents that can help grow your company for you. We're already helping thousands of customers - including Anthropic, OpenAI, Google, and Visa - go to market with unique data, signals, and AI research.\nIn 2026, we raised a $115M Series D at $7.1B Valuation led by Wellington - and are on track to cross $200M in revenue this quarter. We also launched a $1M Scholarship fund for GTME Education.\nSome things to know about us:\nOur community includes 17,000+ customers, 200+ integration partners, 125+ agencies, 50+ Clay clubs, and 30k members on Slack.\n\nOur culture is unique inside and outside of work. Our team members are also DJs, activists, writers, clowns, marathoners, skydivers, psychedelic therapists, social workers, and more.\n\nAll employees can work with world-class coaches who specialize in creativity, management, and more.\n\nOur operating principles - including negative maintenance and non-attached action - guide our work. Read more about them here.\n\nRead about us in the NYT, Forbes, First Round Review, and more.\n\nHear from our employees directly on our Glassdoor page!\nAbout the Team\nClay's product is increasingly powered by AI agents - systems that research, enrich, and take action on behalf of our users, not just generate text. These aren't lightweight copilots layered onto an existing product; they're long-horizon agents built to take on the kind of multi-step, judgment-heavy work that skilled GTM teams spend real time on today. Several teams are working on different layers of this: agents that execute real go-to-market workflows end-to-end, and the shared platform (harness, memory, tools, retrieval, evals) that those agents run on.\nThis role is a shared entry point across those teams. Depending on your background and interests, you'll be matched to a specific team as you move through the process - but every team here is working on the same underlying problem: closing the gap between an agent that looks good in a demo and one that's dependable enough to run unattended in production.\nAbout the Role\nYou'll work closely with product, research-adjacent teammates, and other engineers to make sure agents aren't just capable, but reliable, steerable, and worth trusting with real work. That means the job isn't only about improving model behavior in isolation - it's about turning those improvements into measurable gains in task completion, reliability, and time saved for the people using them.\nWhat You'll Do\nDepending on the team, you might work on:\nAgent products\nDesign and iterate on agent behavior across real GTM workflows. For example, sourcing a Total Addressable Market (TAM) list, which in practice means navigating ambiguous Ideal Customer Profile (ICP) definitions, reconciling conflicting signals across data sources, and making judgment calls that experienced analysts spend real time on.\n\nMap manual, multi-step workflows that GTM teams do today and turn them into agent-driven flows that are as good as, or better than, a human doing it by hand.\n\nBuild and run evals that measure whether an agent actually completed the task correctly - not just whether the output looked plausible - and use them to catch regressions and failure modes.\n\nAnalyze real failures in production and systematically improve robustness.\n\nWork with product to take agent flows from early prototype through closed beta and into general availability, and help define what \"good\" looks like for each one.\n\nAgent platform & infrastructure\nBuild the core agent harness that other teams build on top of, including memory systems, tool infrastructure, and retrieval architecture\n\nImprove agent performance through prompting strategies, tool-use design, and context construction\n\nDesign guardrails and safety checks so agents behave predictably in production\n\nBuild a cross-surface evals framework so every team building on the platform can measure quality, regressions, and performance the same way\n\nBuild feedback loops that turn real usage and production logs into better prompts, tools, and eval coverage over time\n\nSupport teams building their own forks or variants of the managed agent for their specific use case\n\nWhat You'll Bring\nExperience building or shipping production systems with LLMs or agents. This might look like multi-step agent orchestration, prompting and tool-use design, retrieval, structured extraction, or fine-tuning.\n\nStrong backend fundamentals in APIs, databases, distributed systems.\n\nExperience with model or agent evaluation: designing evals, measuring regressions, or turning fuzzy quality questions into measurable signals\n\nA systems-and-outcomes mindset. You care about whether the product actually works for users, not just about model metrics in isolation\n\nComfort debugging messy, real-world failures and a bias toward shipping and iterating quickly in a space where best practices are still being figured out\n\nNice to Haves\nExperience with agent frameworks, tool-calling systems, or retrieval architectures (vector search, hybrid search, RAG)\n\nExperience building or maintaining eval/benchmark infrastructure for LLM-based systems, or running fine-tuning in production\n\nExperience with GTM, sales, or marketing workflows (e.g. lead sourcing, enrichment, audience building)\n\nFamiliarity with Clay's stack: React, TypeScript, Python, AWS (Aurora/Postgres, ECS/Fargate, Lambda, OpenSearch, Clickhouse, Elasticache/Redis), Terraform, Datadog\n\nA growth mindset - we're building a team that's curious, open-minded, and happy to invest in each other's learning, not just their own","description_format":"text","description_chars":6020,"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":["Sales Enablement","Lead Generation","Marketing & Sales AI","B2B Company & Contact Data"],"lifecycle":[{"event":"open","at":"2026-08-14T22:37:36Z"}],"visa":[],"liveness":{"score":46,"band":"ok","label":"Likely open","p_open":1,"p_active":0.758,"p_room":0.6,"age_days":63,"expected_fill_days":75,"reasons":["conf:3","velocity","win:late","crowd:"],"computed_at":"2026-10-17T05:46:00Z"},"pay":{"stated_usd_annual":300000,"is_top_pay":true},"html_url":"https://alion.io/job/clay-software-engineer-applied-ai","json_url":"https://alion.io/job/clay-software-engineer-applied-ai.json","meta":{"generated_at":"2026-10-17T11:32:47Z","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":15118,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"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":1484},"rest":"https://alion.io/mcp/rest/get_company?id=1484"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fclay-software-engineer-applied-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%2Fclay-software-engineer-applied-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%2Fclay-software-engineer-applied-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/clay-software-engineer-applied-ai\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fclay-software-engineer-applied-ai"}]}