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AI Engineer & Researcher
AI Engineer & ResearcherBaseThesis Labs • Bengaluru, Karnataka, India
AI Engineer & Researcher

AI Engineer & Researcher

BaseThesis Labs • Bengaluru, Karnataka, India
7 hours ago
Job description

about

we believe the next decade of computing isn't about better agents or faster models.

it's about machines that remember you, respond naturally, and feel like they understand context across time and modality.

right now, every ai assistant forgets you exist when you close the session. every voice agent sounds scripted and breaks on interrupts. every "breakthrough" is just better prompts on the same broken and painfully complex architecture.

we're building the infrastructure layer that makes ai interactions actually feel natural - adaptive memory systems, real-time voice orchestration, self-evolving agents that evolve from experiences by rewriting their own code, and multimodal context that doesn't fall apart.

think : the default stack that every multimodal agent will need, but doesn't exist yet.

our team is :

  • a 25-year-old ai-first builder (that’s me) from IIT-M and BITS Pilani who’s been building self evolving, multi-modal agents for enterprises
  • Ashok, who cofounded DriveU and scaled it to ₹125 crore ARR and profitability
  • Raveen, cofounder of Myntra, Baby Oye and Multiply Ventures

we also run a private community of 140+ curated ai builders / researchers from labs like anthropic, open ai, cartesia, ultravox, microsoft research, as well as veterans from aws & am

our research thesis

we're betting on three-layer adaptive memory with reinforcement learning :

  • layer 1 (working memory) : recent context, lasts minutes, conversation-specific
  • layer 2 (episodic memory) : important past interactions, lasts weeks, user-specific
  • layer 3 (semantic memory) : learned patterns / preferences, lasts forever, continuously refined
  • the rl component : the agent gets feedback signals (explicit corrections, conversation success, user retention) and uses policy gradients to learn :

  • what's worth moving from working to episodic memory?
  • what patterns in episodic memory should become semantic?
  • when to surface which memory layer during conversations?
  • this isn't novel ml research - we're applying existing rl techniques (you know them) to a new problem : continuous memory improvement for conversational agents.

    we're also building real-time voice orchestration with :

  • predictive vad (anticipate interrupts before they happen)
  • multi-agent coordination (handle 3+ speakers / agents smoothly)
  • latency-adaptive quality (degrade gracefully under network constraints)
  • what you'll actually do (with me)

  • audit ultravox, sesame, elevenlabs, openai realtime api with me
  • design prototype adaptive memory system (three layers + rl)
  • ship first voice agent demo with working memory
  • build multi-agent orchestration (handling 2+ speakers)
  • implement predictive vad (not just silence detection)
  • create test harnesses for latency, context retention, ux flow
  • package everything as usable sdk
  • write documentation that doesn't suck or confuse
  • iterate based on what breaks in production
  • either scale the infra or spin out a product
  • mentor teams at our hackathons (we're running quarterly builder events)
  • collaborate with fellow lab researchers to turn research papers, frameworks, and technical ideas into tangible demos or proof-of-concepts.
  • contribute to reusable internal libraries, api’s, and infrastructure that power all basethesis projects.
  • not using off-the-shelf solutions (again). evaluating sesame, ultravox, elevenLabs, openai realtime api, and building what they lack.
  • work across voice, vision, and text-based interfaces- integrating models into cohesive user experiences.
  • document learnings as “lab notes” or public prototypes. clarity and storytelling are part of the craft.
  • who this is for

    required qualifications

  • you've shipped something that had real users (product, side project, open source tool)
  • you understand system design at an architectural level, not just api integration
  • you can write production-quality code fast (we're judging on speed + quality, not just one)
  • you think from first principles : "why does rag fail for real-time agents?" not "everyone uses rag so we should too"
  • you're comfortable with navigating ambiguity initially
  • you can explain complex technical decisions simply (this matters for docs + future hiring later)
  • preferred qualifications (not required, just helpful)

  • experience with websockets, webrtc, or real-time audio / video pipelines
  • familiarity with reinforcement learning (grpo, ppo, dqn, policy gradients)
  • you've read papers on memory systems, hci, or voice ai (or you will after reading this)
  • you've built ml models beyond just fine-tuning llms
  • you've debugged production systems under load and lived to tell the stories
  • you've contributed to open source or have a github people actually look at
  • what you'll get

  • you're not maintaining legacy code or optimizing conversion rates by 0.3%.
  • you're solving genuinely hard problems that don't have stack overflow or chatgpt answers.
  • space to work on cutting edge research, no pressure to serve legacy customers
  • you define the entire memory + multimodal stack with me from scratch
  • market rate salary and founding engineer equity in something that will matter
  • workspace buzzing with the most hardcore builders & the best research minds in blr
  • when people reference "how basethesis handles context" in 2027, that's your architecture
  • direct access to ai builders / researchers at openai, deepmind, anthropic, xAI, amd
  • a community of hundreds hardcore builders and top researchers in frontier labs
  • the downside :

  • first couple months you're researching, tinkering and coding 10-12 hours a day
  • working on cutting edge research means we rapidly learn and iterate every day
  • we might and will fail multiple times before we finally land on mars
  • why you might want to do this

    you're probably 2-5 years out of college. worked at a startup or built side projects with users.

    you're frustrated by abstraction layer or wrapper culture.

    you want to build infrastructure that's technically hard and which requires deep understanding of the entire stack.

    you're okay with :

  • figuring it out together (we don't have all the answers)
  • intense first 6 months proving this matters
  • rapidly learning and iterating with an obsession towards the mision
  • you're excited by :

  • defining a new technical approach to ai memory
  • building in public, open sourcing some parts
  • being in the room when we decide together on what to build next
  • ownership of hard technical problems
  • if you're reading this thinking "f

  • it, i want to try" - that's the sign to apply.
  • you're probably more ready than you think. capability over credentials.

    let's build

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    Ai Engineer • Bengaluru, Karnataka, India

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