--- title: 'Software Engineer, Full-Stack at Ema' canonical: 'https://feeny.ai/job/software-engineer-full-stack-ema-san-francisco-zetv3r0q2r6n' type: 'job' last_seen: '2026-10-14' --- # Software Engineer, Full-Stack at Ema - **Company:** Ema - **Location:** San Francisco, CA - **Employment:** full-time - **Posted:** 2026-10-01 - **Last confirmed live:** 2026-10-14 - **Apply:** https://jobs.ashbyhq.com/ema/a3e07250-51f9-4518-ad2e-19d7825f5234 ## Job description ## About Ema Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale. Build agents that learn from the work they do. Ema builds AI employees that carry out complex workflows across enterprise applications. Our ML team works on the loop that makes them better: production traces become data, data becomes training and evaluation, and better agents produce better traces. The hard part is deciding which intervention will improve behavior in the next real workflow. The problem space - Harnesses and inference-time compute. Design context, tools, skills and orchestration for multi-step agents, including work across documents, slides, images, audio and video. Test where extra reasoning, search or verification earns its latency and cost. Build self-improvement loops with explicit permissions, evaluation gates and rollback. - Agent post-training. Curate trajectories for SFT, optimize preferences, or run RL on real agent tasks. Investigate methods such as DPO, GRPO or DAPO where they fit; compare process and outcome supervision, shape rewards, and distill useful frontier behavior into smaller models. Measure whether gains transfer beyond the training environment. - Environments and rewards. Turn enterprise workflows into reproducible training and evaluation environments: fixture tenants, simulated users who may get impatient and leave, and rewards grounded in verifiable outcomes. Find the shortcuts an agent can exploit before a training run optimizes for them. - Data engines and evaluation. Mine production agent-steps for failures; build curated corpora and useful synthetic augmentation. Calibrate judges against human labels, construct behavior-level benchmarks from real workflows, and quantify data quality, performance uplift and reliability across stochastic runs. - Retrieval, memory and context graphs. Connect enterprise information with user- and tenant-level learnings. Separate failures of retrieval from failures to use retrieved context; test what to retain, update and retrieve so that past experience improves the next decision. - Quality per dollar. Build and evaluate routing, ensembles, caching and small-model specialization. Measure downstream task success alongside latency and cost; a cheaper model is useful only if the complete agent still succeeds. You’ll go deep in a subset of these areas. Projects combine applied research with the engineering needed to make the result work in production. Own the experiment and the system Most projects develop over roughly four to six months, with useful improvements shipping along the way. You’ll define the problem and baseline, build the data or environment needed to test it, run experiments, and own serving and integration. Follow the system through deployment, monitoring and failure analysis until it is ready for a clear engineering handoff. You’ll work with researchers and engineers across the Bay Area, Vancouver and India. We’re hiring from junior through senior levels, with project scope matched to your experience. ## What we’re looking for - Depth you can defend. Substantial work in at least one of agent/tool-use systems, post-training, reward modeling or RL environments, retrieval and memory, or evaluation design. Be ready to explain the mechanism, the alternatives you rejected and the failure modes you found. One area you can teach us beats five you’ve touched. - Evidence of zero-to-one ownership. A system, model or research project you took from an ambiguous problem to a working result. We want to understand your contribution, the tradeoffs you made, and what changed when the work met real users or realistic tasks. - Statistical judgment. You can size an experiment, choose meaningful baselines and held-out tests, and account for variation across tasks, seeds and repeated runs. You can distinguish a real improvement from judge bias, data leakage or a benchmark shortcut. - Production engineering judgment. You can debug across the model and system boundary, isolate a failure, and turn the result into maintainable production code. - Honest measurement. You would rather retire your own approach after a clean negative result than ship an improvement that disappears under a stronger evaluation. A master’s or PhD in a relevant field, or equivalent work or research experience. Papers, substantial open-source contributions, trained models and well-documented experiments can demonstrate that depth. ## Experience that would be especially useful - Open-model post-training with TRL, veRL, OpenRLHF, or similar or a custom loop, especially debugging reward hacking or unstable optimization. - Interactive agent environments/harnesses for software engineering, web or tool use; large-scale trace analysis, data curation or synthetic generation. - Designing systems to support complex, long-horizon agent work across a multitude of modalities and platforms - Serving with vLLM or SGLang, distillation, quantization, or multi-node GPU training. - Practical security work on prompt injection, data governance or permission boundaries for agents that can act and improve themselves. These are project-specific strengths; post-training experience is optional, and no candidate needs the entire list. Bring a repository, paper, model or technical write-up that lets us examine how you think and what you built. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. Ema Unlimited is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics. ## About Ema ## Company Overview - **One-liner**: Ema provides a universal agentic control plane that enables enterprises to build, deploy, and scale autonomous AI agents to automate complex workflows across every business function. - **Entity Type**: Private (Series A) - **Headquarters**: San Francisco, California, United States - **Founded**: March 2023 - **Founders**: Surojit Chatterjee (Co-Founder & CEO), Souvik Sen (Co-Founder & CTO) ## Core Business - **Primary industry**: Enterprise AI Software / Generative AI / Agentic AI - **Target customers**: Large enterprises across healthcare, financial services, legal, and technology sectors (B2B, Enterprise) - **Mission or purpose statement**: To give business and IT leaders one place to build, deploy, and scale AI Agents across every function—HR, Finance, Sales, Customer Support, Legal, and beyond. The company believes the future of work is "humans multiplied by AI Employees," not humans replaced by AI. ## Products & Services - **Ema Universal Agentic Control Plane**: A platform for building, deploying, and scaling AI Agents across all enterprise functions. - **EmaFusion™**: A patented AI fusion layer that blends 100+ LLMs to optimize for accuracy, cost, and latency on every query, without vendor lock-in. - **GWE™ (Generative Workflow Engine)**: The orchestration core that breaks complex enterprise workflows into multi-agent steps, with built-in human-in-the-loop, audit trails, and async operations. - **Enterprise Context Graph**: Deep, permissioned memory across an organization's data, systems, and processes so every AI Employee knows the business from Day One. ## Market Standing - **Valuation**: Not publicly disclosed - **Total Funding**: $75 million (across multiple rounds) - **Seed Round** (March 2024): $25M led by Prosus Ventures and S32 - **Series A** (July 2024): $36M led by Accel and S32 - **Additional Series A** (March 2026): $14M from 9 investors - **Notable Investors/Partners**: Accel, S32 (Section 32), Prosus Ventures, Hitachi Ventures, KPMG (strategic investment) - **Growth Signals**: - Employee headcount grew 89.2% year-over-year to 155 people. - Operates in 6 countries (US, India, UK, Canada, UAE, Germany). - Trusted by global enterprises across highly regulated industries. - Holds certifications including ISO 42001 (world's first AI management standard), SOC 2 Type II, ISO 27001, and HIPAA. - Supports on-premises and air-gapped deployment, a significant differentiator for security-conscious enterprises. ## Competitive Advantages - **EmaFusion™ Technology**: A proprietary multi-model approach that blends over 100 LLMs, optimizing for accuracy, cost, and latency while avoiding vendor lock-in. - **Enterprise-Grade Security & Compliance**: One of the few agentic AI platforms that supports on-premises and air-gapped deployment, with certifications like ISO 42001, SOC 2 Type II, ISO 27001, and HIPAA. - **Deep Enterprise Integration**: Pre-integrated with 200+ enterprise systems, with a permissioned "Enterprise Context Graph" that provides deep, institutional memory. - **Strong Strategic Backing**: Backed by top-tier VCs (Accel, S32) and a strategic investment from KPMG, signaling strong enterprise credibility. ## Strategic Focus - **Product Expansion**: Continuing to build out the agentic AI platform to cover more enterprise functions and workflows. - **Enterprise Adoption**: Deepening relationships with large, regulated enterprises (healthcare, financial services, legal). - **Global Scale**: Expanding operations across key markets (US, India, UK, Canada, UAE, Germany) to support a global customer base. - **Trust & Compliance**: Maintaining and expanding certifications (ISO, SOC 2, HIPAA) to meet the highest enterprise security standards. ## Why Work Here - **Culture**: Ema describes itself as an "AI-first" company that hires "fewer people with a hunger and growth mindset" and augments the human workforce with AI employees. Core values include being innovative, trusted, relentlessly driven, and bold. - **Engineering Philosophy**: The company has a published "Engineering Manifesto" and emphasizes hiring "builders and creators." They believe a "10x team is better than a 10x individual" and operate with a "no wait, no boundaries" approach. - **Work Environment**: "Everyone builds" is a key principle—employees are expected to write software, create original content, and build new processes regardless of their formal role. - **Remote/Hybrid Policy**: Not explicitly stated, but offices are located in the SF Bay Area (US), India, UK, and Canada, suggesting a global, likely hybrid-friendly setup. - **Talent Pool**: The company attracts talent from top tech companies (Google, Meta, Amazon, Coinbase, Databricks) and top universities (IIT Delhi), indicating a high-caliber peer group. - **Open Roles (as of mid-2026)**: 32 active job postings, including roles in ML Engineering, Backend Engineering, DevOps, Product Management, Security & Compliance, and Marketing. ## Sources 1. [ema.ai/careers](https://www.ema.ai/careers) 2. [jobs.ashbyhq.com/ema](https://jobs.ashbyhq.com/ema) 3. [Crunchbase - Ema](https://www.crunchbase.com/organization/ema-8724) 4. [LinkedIn - Ema](https://www.linkedin.com/company/ema-unlimited) 5. [Tracxn - Ema](https://tracxn.com/d/companies/ema/__ksb3jUKrtsxUjw6V-pJ_Daac843jAQUG62LqeRWEGJ0) ## Other roles at Ema - [Devops Engineer, US](https://feeny.ai/job/devops-engineer-us-ema-mountain-view-6xs6vam1xfxy) — Mountain View, CA - [IT Engineer](https://feeny.ai/job/it-engineer-ema-san-francisco-1g6631qr9fjg) — San Francisco, CA - [AI Resident UK/EMEA](https://feeny.ai/job/ai-resident-uk-emea-ema-united-kingdom-499aq5tctc5a) — United Kingdom - [Sales Development Representative](https://feeny.ai/job/sales-development-representative-ema-united-states-5xq9tbb76n09) — United States - [Software Engineer, Machine Learning](https://feeny.ai/job/software-engineer-machine-learning-ema-vancouver-chp4xdz8xx6m) — Vancouver, Canada - [Field Marketing Manager, EMEA](https://feeny.ai/job/field-marketing-manager-emea-ema-united-kingdom-ftj8q0ayw809) — United Kingdom - [Software Engineer, Full-Stack](https://feeny.ai/job/software-engineer-full-stack-ema-vancouver-ktj8sbzdgr3w) — Vancouver, Canada - [Partner Manager - UK](https://feeny.ai/job/partner-manager-uk-ema-united-kingdom-wjefgt06nw9f) — United Kingdom - [Product Manager, UK](https://feeny.ai/job/product-manager-uk-ema-united-kingdom-c5rn0ymc65x6) — United Kingdom - [Engineering Leader](https://feeny.ai/job/engineering-leader-ema-united-kingdom-0pkrba3j842v) — United Kingdom