Ai Platform Engineer Jobs

Scale AI is seeking a highly motivated Senior Accountant to join our growing accounting team and assist in preparing day to day corporate accounting operations, supporting the month end close process, and helping to implement systems and processes that will support Scale as we continue to grow. You will also gain exposure to working with our international entities as we continue our expansion globally, working on some of the highest value aspects of the busienss. The ideal candidate thrives in a high-growth start-up, is detail-oriented, and has excellent interpersonal and communication skills. Additionally, the candidate has demonstrated the ability to build scalable cross-functional relationships through systems and process implementation. We hope you will join our team! You Will: Prepare journal entries and day to day corporate accounting activities, support the month end close process, and provide timely and accurate month-end close financials that are U.S. GAAP compliant Build or enhance balance sheet account reconciliation workpaper including reviewing and performing some clean-up of historical reconciliations and related balances Collaborate within Accounting and Finance teams on metrics, flux analysis, forecast, and projections and support preparation of the monthly reporting package Prepare documents supporting internal and external audits and ensure the successful completion of those audits Support the implementation of new systems, tools, and processes to streamline close and build scalable solutions to support the growth of the Company Identify and drive process improvements to gain efficiencies and reduce close timeline Develop, maintain and improve internal controls which relate to assigned areas Ideally You Have: Bachelor’s degree in Accounting; CPA or in the process of working towards one is preferred. 3+ years of relevant accounting experience; Combination of public accounting and industry experience preferred. Strong knowledge of U.S. GAAP. Ex

Scale Labs, Research Scientist — Frontier Risk Evaluations As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following: Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities; Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems; Publish evaluation methodologies and write technical reports for policymakers. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting technical research collaboratively. You should be comfortable building and instrumenting ML pipelines, writing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes. A track record of published research in m

Role Overview Scale’s EMEA GTM team is scaling rapidly across complex, high-stakes government markets. Our commercial motion spans country leads, account executives, strategists, solutions engineering, engagement management, partnerships, delivery, finance, legal, and product - all operating in fast-moving, region-specific environments. We are hiring a Sales Enablement Lead to build the enablement engine for EMEA GTM. This role will report to the Head of Commercial Strategy and Operations and will be responsible for ensuring every seller, strategist, and field partner has the knowledge, materials, onboarding, operating rhythms, and deal support needed to execute with speed and quality. This is a hands-on builder role. You will create the infrastructure that helps the team ramp faster, sell more consistently, reuse what works, and translate a complex AI product and public-sector GTM motion into practical field execution. What You’ll Do Build and own EMEA GTM onboarding Design and run a structured onboarding programme for new EMEA GTM hires across sales, strategy, solutions, and adjacent commercial roles. Create role-specific ramp plans, learning paths, certification moments, manager check-ins, and practical field exercises. Partner with Commercial Operations, People, Recruiting, and GTM and EPD leadership to ensure new hires understand Scale’s products, market context, customers, sales process, operating model, and expectations. Own the EMEA GTM knowledge base Build and maintain a centralised source of truth for EMEA GTM materials, including pitch decks, account planning templates, customer FAQs, qualification guides, use-case libraries, demos, proposal examples, talk tracks, win/loss learnings, and market-specific collateral. Keep materials current, easy to find, and clearly organised by market, role, product area, customer segment, and sales stage. Partner with central Sales Enablement, Product Marketing, Solutions Engineering, Legal, Finance, Delivery, and Pr

Scale is looking for a Support Systems & Routing Specialist to own and optimize the core infrastructure powering our contributor support operations. In this role, you will be responsible for configuring and maintaining systems like Zendesk, designing routing logic, and building scalable frameworks that ensure tickets are assigned accurately and efficiently. As our operations continue to grow, this role will play a critical part in ensuring our support systems scale seamlessly with increasing volume and complexity. You will partner closely with Support Ops, Product, Engineering, and cross-functional stakeholders to translate operational needs into robust system configurations. You will be part of a highly detail-oriented and systems-driven team, where your work directly impacts operational efficiency and contributor experience. This role is ideal for someone who thrives in building and improving systems that others rely on daily, and who enjoys solving complex operational challenges at scale. You will: Own end-to-end Zendesk configuration, including ticket forms, fields, macros, triggers, automations, SLAs, and views Design and maintain routing logic to ensure accurate ticket distribution across skills, queues, and teams Build and manage the support agent skills framework (skills, tiers, certifications, queue structures) Monitor and optimize queue health, load balancing, and ticket assignment accuracy Configure and maintain integrations across support tools and reporting systems Troubleshoot system issues and partner with Engineering or vendors when needed Collaborate with Support Ops, T&S, TPMs, and Product to implement and improve workflows Document system configurations, workflows, and best practices clearly Provide training and guidance to stakeholders on system usage and routing logic Ideally you’d have: Experience administering Zendesk or similar support CRM systems Experience designing routing logic or managing skills-based systems in high-volume envir

About Bolna Bolna is Voice AI infrastructure built for India - and now for the world. We help businesses deploy intelligent voice agents that can call, converse, and convert in any language, at scale. From collections to customer support to sales, our agents handle millions of conversations so humans don’t have to. We’re a YC F25 company, backed by General Catalyst, with 1,050+ paying customers and growing fast. Our team of ~25 is based in Bengaluru. The Role Every voice AI agent Bolna deploys makes real-time judgment calls - when to speak, when to go silent, when a customer is done talking, when to hand off. We’re building automated systems to grade these calls at scale, using LLMs as judges of call quality. But before you trust a model’s judgment, you verify it against a human’s. That’s this role. You’ll listen to real calls, annotate what actually happened, and check whether our automated systems - LLM-as-judge evals and quantitative signal detection - got it right. It’s precise, high-attention work, and it sits right at the center of how we know our voice agents are actually working. This is an internship role for someone early in their career who wants hands-on exposure to how a voice AI company builds trust in its own AI. What You’ll Do Annotation Listen to and annotate real customer calls - transcription review, issue tagging, labeling - using tools like Label Studio Follow (and help sharpen) annotation guidelines for a multilingual environment (Hindi, English, Hinglish, ) Verifying LLM-as-Judge Evaluations For calls flagged by our automated eval pipeline, verify whether the model’s call was actually correct - for example, confirming whether a detected barge-in (agent/customer talking over each other) genuinely happened by listening to the audio Mark agreements and disagreements clearly, with reasoning, so we can measure and improve model accuracy over time All tools needed for this will be provided Verifying Quantitative Measures Check system-flagged quantit

At Bolna, we’re building tools that change the way teams leverage Voice AI. We’re looking for a Founding Machine Learning Engineer to own the end-to-end lifecycle of building, evaluating, deploying, and improving models that power millions of production conversations. This is a high-impact, high ownership role where you won’t just work on Bolna’s ML stack—you’ll help build the foundation it scales on. Our team includes IIT alumni with experience at Bain, Atlassian, Uber, Zomato, and LinkedIn, and is backed by leading investors. Responsibilities: Build the data engine - Design pipelines to source and clean conversational voice data across Indian languages, accents, and telephony conditions. Fine-tune models that ship - Fine tune and train models to improve accuracy, speed, and reliability across different use-cases. Define what "good" means - Build evaluation datasets and benchmarks for transcription accuracy, voice naturalness, interruption handling, latency, and end-to-end conversation quality. Set up human-in-the-loop pipelines to capture subjective quality at scale. Ship to production - Work with the engineering team to deploy models into a latency-sensitive, high-volume system. Monitor performance in the wild, debug regressions, and iterate fast. Required Skills: 3+ years of hands-on ML experience with deep practical real-world experience in training models. Strong Python and PyTorch fundamentals with exposure in distributed training, and modern fine-tuning techniques (LoRA, QLoRA, DPO, RLHF, etc.). Training data as a first-class problem. Experience designing data pipelines from collection, cleaning, labeling, deduplication, augmentation and treating data quality as a core engineering discipline. Rigorous about evaluation. You know that "looks good in a demo" is not a benchmark. You build the evals before you trust the model. Speech model experience is a plus with real-time / streaming inference experience where you would have contributed to latency optimization

this is test job

lorem upsum

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey – from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams. Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers. What's the opportunity? Fin's Machine Learning team is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands. We are an extremely product focussed team. We work in partnership with Product and Design functions of teams we support. Our team's dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test. We are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy. What will I be doing? Play an active role in hiring, mentoring and career development of other engineers Raise the bar for technical standards, performance, reliability, and operational excellence Identify areas

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey – from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams. Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers. What's the opportunity? Fin's Machine Learning team is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands. We are an extremely product focussed team. We work in partnership with Product and Design functions of teams we support. Our team's dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test. We are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy. What will I be doing? Identify areas where ML can create value for our customers Identify the right ML framing of product problems Working with teammates and Product and Design stakeholders Conduct exploratory da