Platform and Backend Engineer Intern at DeepMechanix

Location: Remote, India-based · US HQ: San Jose, CA Duration: 3 months, May–August 2026 Stipend: ₹75,000–₹1,25,000/month, set on demonstrated depth Return offer eligible: Yes — full-time role + RSUs for strong performers About DeepMechanixWe are building superintelligence for the design and engineering of physical infrastructure, the hardware that civilization runs on. We are starting with static equipment: pressure vessels, heat exchangers, reactors, and related pressure-containing equipment that the global chemical, energy, and pharmaceutical industries depend on.Engineering workflows in this domain are largely unchanged since the 1990s. Engineers transcribe datasheets by hand, perform ASME code calculations in decades-old desktop applications, and produce deliverables through manual review cycles that can take weeks. We are building the software layer that replaces the manual parts of this workflow while preserving the rigorous compliance and traceability that industrial customers require. Our first product combines custom retrieval over ASME standards with automation of engineering software. We are pre-seed with revenue, targeting a seed round in late 2026.Our research work is being published externally. The product is live and shipping to pilot customers in India and the US.What This Internship OffersCompensation Monthly stipend of ₹75,000–₹1,25,000/month for a 3-month engagement. The top band is reserved for candidates demonstrating both research-grade depth and production engineering quality.Return offer and equity exposure: Structured as a pipeline to a full-time AI/ML Engineer role. Strong performers receive a return offer with increased compensation and an RSU grant tied to the company's exit outcome.Mentorship and authorship: Direct technical collaboration with the founding team. Novel research contributions, building on the ASME PVP 2026 framework, are eligible for co-authorship on papers or patent filings.Letter of recommendation: A detailed letter of recommendation useful for applications to top US and European research programs (e.g., Stanford, CMU, MIT, Berkeley, ETH Zürich, EPFL).Resume signal: Experience with a US-incorporated deep-tech AI startup, a novel regulated industrial domain, founder-level technical collaboration, published external work, and equity exposure.What You Will OwnThis role owns the platform layer of the DeepMechanix product: the APIs, ingestion pipelines, storage architecture, LLM orchestration, and observability infrastructure that turn a research-grade AI prototype into software that we will actually deploy inside a regulated environment.Active workstreams during the internship:FastAPI service layer: Exposing ingestion, inference, and calculation endpoints with proper async handling, typed contracts, and well-defined failure modes. Building the internal service surface the AI team depends on.Document ingestion pipeline: Moving files from Azure Blob Storage through parsing, embedding, and vector storage. The pipeline has to survive the documents customers actually send, not the documents we wish they would send.LLM orchestration layer: Routing requests across Claude, GPT, and Gemini providers with per-model rate limiting, provider fallbacks, and semantic caching. Building the cost and latency instrumentation that makes model-selection decisions data-driven rather than intuitive.Storage architecture: Postgres for structured data, vector storage for retrieval, and programmatic SQLite writes to engineering project files.Observability: Structured JSON logging, Langfuse tracing, and cost and latency dashboards per LLM call. Building the system that tells us something broke before customers do.Local LLM serving experiments: Azure-based self-hosted inference for workloads where hosted providers are too expensive or too slow. Memory management, batching, and throughput characterization where it matters.Required Skills & Candidate ProfileHow We WorkOur mandatory operating requirement is fluency with agentic coding tools. We ship daily using Claude Code, Cursor, Codex, and adjacent agent-first environments. Onboarding establishes these patterns, but strong candidates have already internalized and leveraged them to compress implementation cycles. Working hours: Primarily async, with a 30-minute daily synchronous standup call during the 8–11 AM IST window.Core stack:AI layer: Prompt engineering, context control, structured instruction design, retrieval systems (embeddings, vector databases, RAG-based workflows using manual pipelines and frameworks such as LangChain, LangGraph etc.)LLM-assisted development: Code generation, refactoring, debugging, code review, and architectural reasoning using Cursor and Claude CodePython backend: Core Python, OOP, unit testing, pip, virtualenv, async patterns, FastAPIHTTP fundamentals: Authentication, authorization, caching, background jobs, messaging queuesData layer: PostgreSQL (normalized schema design, CRUD, joins, subqueries, aggregations), Azure Blob Storage, AWS S3Tooling and DevOps: CLI, Git fundamentals (branching, merging, pull requests), Linux shell scripting, Docker, GitHub Actions CIObservability and caching: Langfuse, structured logging Familiarity and strong ability to use agentic coding tools can make up for unfamiliarity with specific items in the stack above.Education RequirementsEnrolled in the final year of a Bachelor's Degree  in Computer Science, Computer Engineering, or a related field. or Fresh graduates (What We Are Looking ForThe bar is high: we prioritize proof of execution over profile. We are looking for exceptional individuals who fit one of the following archetypes.System Builders: You have deployed a backend system serving real traffic (SaaS, bot, or library) with production-grade tests, CI, and error handling. Localhost projects do not qualify.Systems Engineers: You have demonstrated depth in infrastructure or data engineering through significant open-source contributions or validated research/startup projects.Competitors: You have a proven track record of execution under pressure, evidenced by hackathon wins or competitive programming results.Academic Achievers: CGPA 9.0+ in CS from a top-tier institution (IIT/BITS/NIT) with mastery of core systems subjects, paired with independent building evidence.Useful but not required: Distributed systems concepts (idempotency, eventual consistency, circuit breakers), data pipeline design under failure conditions, and infrastructure as code (Terraform, Pulumi, or CDK equivalent).How to ApplyAttach a PDF CV. Include in the body:A link to your GitHub profile and to the specific repository you most want us to reviewThree to five specific examples of your strongest work, each with a direct link — deployed services, open-source contributions, system design documentsA URL to at least one backend system you have deployed that is still running or, if no longer running, the archived system design document and repositoryA short note (3–5 sentences) on the agentic coding tools you use today, how you use them, and one specific example where they materially changed how you approached a problemSelection process:15-30 minute screening call90-minute technical interview 30-minute founder conversation on fit and working style

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