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ai/ml research intern

ai ml software developer

TechnoSphere, Inc.

<div>
 
 Job Description
 
 
 Mandatory Skills
 
 
 
 
 AI/ML, ML Ops, Java,Python
 
 
 
 
 JD
 
 
 
 
 We are looking for a skilled MLOps Engineer to join our team and help us build, deploy, and maintain robust and scalable machine learning systems. You will be responsible for the full lifecycle of our ML pipelines, from data ingestion to model serving. This is a hands-on role where you will design and implement automated workflows, ensure data quality, and manage model deployments in a production environment.
 
 
 Responsibilities
 
 
 
 
 Data and Feature Pipelines: Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like Kubeflow Pipelines and Vertex AI Pipelines.
 
 
 Feature Engineering: Implement and containerize feature engineering logic for diverse datasets, ensuring reusability and scalability.
 
 
 Data Validation: Integrate and manage data validation processes, including leveraging advanced techniques like AI Agents and the Generative Language API to automatically detect and remediate data quality issues.
 
 
 Model Training and Experimentation:
 
 
 Set up and maintain automated continuous training (CT) pipelines using Vertex AI Pipelines (Schedules) and Cloud Scheduler.
 
 
 Implement experiment tracking to log and compare model parameters, metrics, and artifacts.
 
 
 Configure and execute Hyperparameter Tuning jobs using Vertex AI Training to optimize model performance.
 
 
 Model Management: Establish a robust Model Versioning system to manage and store model artifacts securely in a centralized repository (Cloud Storage).
 
 
 Deployment and Serving:
 
 
 Containerize ML models and their dependencies using Docker and manage images with Artifact Registry.
 
 
 Build and maintain CI/CD workflows for ML models, ensuring seamless and automated deployment.
 
 
 Configure and manage low-latency production serving environments using Vertex AI Endpoints for real-time inference.
 
 
 Qualifications
 
 
 
 
 Strong experience with Google Cloud Platform (GCP) services, specifically in the MLOps and ML domain (Vertex AI, Kubeflow, Cloud Storage, Artifact Registry).
 
 
 Proven ability to design and implement end-to-end ML pipelines for data management, model training, and deployment.
 
 
 Hands-on experience with containerization technologies like Docker.
 
 
 Familiarity with CI/CD practices and pipeline automation.
 
 
 Knowledge of ML frameworks like TensorFlow, and experience with experiment tracking and hyperparameter tuning.
 
 
 Excellent problem-solving skills and a strong understanding of the ML lifecycle.
 
 
 Experience with the Generative Language API (Gemini model) or other AI Agent integrations is a plus.
 
 
 
</div>

AI/ML software developer

CodeRabbit

<div></div><div><div><div><div><h2 class="jobSectionHeader">Location</h2>
San Francisco
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Location Type</h2>
Hybrid
</div><div><h2 class="jobSectionHeader">Department</h2>
Marketing
</div><div><h2 class="jobSectionHeader">Compensation</h2>
<ul><li>$170K &ndash; $225K &bull; Offers Equity</li>
</ul></div></div><div><div>
</div><div><div><h2 class="jobSectionHeader">About CodeRabbit</h2>
CodeRabbit is an innovative research and development company focused on building extraordinarily productive human-machine collaboration systems. Our primary goal is to create the next generation of Gen AI-driven code reviewers: a symbiotic partnership between humans and advanced algorithms that significantly outperforms individual engineers. We combine language models with human ingenuity to push the boundaries of software development efficiency and quality.
<h2 class="jobSectionHeader">Why this role, why now</h2>
CodeRabbit is the AI code review platform trusted by thousands of engineering teams &mdash; and we're at the inflection point where great field marketing turns developer awareness into a high-velocity pipeline engine. We're hiring a senior operator to own our North American field motion end-to-end: the events we show up at, the communities we plug into, and the partner ecosystem we build with (GitHub, GitLab, AWS, Google Cloud).
This is not a &quot;support the calendar&quot; role. You will set the strategy, defend the budget, and be measured on sourced and influenced pipelines. You'll be the most visible marketer in the room when CodeRabbit shows up at KubeCon, GitHub Universe, and the AI dev summits that matter.
<h2 class="jobSectionHeader">What you'll own</h2>
<ul><li>Regional and Global field strategy. Decide which events, sponsorships, dinners, roundtables, and meetups CodeRabbit invests in &mdash; and which we pass on. Build both the regional and global plan and defend it with data.
</li><li>Pipeline from the field. Partner with Sales and SDR leadership to design programs that generate qualified meetings and accelerate open opps. Own the number for sourced and influenced field pipelines.
</li><li>Marquee event execution. Run our presence at top developer and DevOps conferences end-to-end &mdash; booth, content, speakers, swag, pre/post-event ABM, on-site staffing, and follow-up.
</li><li>Community and developer programs. Build relationships with the local engineering communities, user groups, and AI/ML meetups where our buyers and champions already gather.
</li><li>Partner and ecosystem co-marketing. Drive joint field activations with GitHub, GitLab, AWS, Google Cloud, and other ecosystem partners &mdash; from co-hosted dinners to co-branded booths.
</li><li>Customer and executive engagement. Produce the customer advisory board sessions, executive roundtables, and CISO/VP Eng dinners that turn champions into references and references into expansions.
</li><li>Budget and vendors. Own the regional field budget, negotiate hard with venues and agencies, and run a tight P&amp;L on every program.
</li><li>Measurement. Define the KPIs, build the reporting, and present results to marketing and GTM leadership. We will know what every dollar produced.
</li></ul><h2 class="jobSectionHeader">What we're looking for</h2>
<ul><li>10+ years in field, event, or integrated marketing &mdash; most of it at B2B SaaS or developer tools companies.
</li><li>A real track record of running flagship technical events (think KubeCon, re:Invent, GitHub Universe scale) from strategy through measurable pipeline outcomes.
</li><li>Deep fluency in the developer tools and DevOps landscape. You don't need to write code, but you should know the difference between a PR and a push, and why engineers care about code review.
</li><li>Tight working relationships with Sales &mdash; you've co-built ABM motions, target account event strategies, and post-event SLAs that actually get followed.
</li><li>Data-driven by default. Comfortable in Salesforce, HubSpot, and a BI tool. You can pull your own pipeline reports and don't outsource your numbers.
</li><li>Operator's instincts. You ship on time, on budget, and with taste. You've recovered from a venue cancellation, a no-show keynote, and a shipping disaster &mdash; and the program still hit its goal.
</li><li>Excellent writer and communicator. You can brief an exec, draft a sponsor pitch, and rewrite a session abstract in the same afternoon.
</li></ul><h2 class="jobSectionHeader">Nice to have</h2>
<ul><li>Experience marketing to engineering leaders, platform/DevOps teams, or AI/ML practitioners.
</li><li>Time at a Series A&ndash;C company that scaled past $10M ARR.
</li><li>Hands-on experience with PLG or community-led growth motions.
</li><li>Existing relationships at GitHub, GitLab, AWS, or Google Cloud partner programs.
</li></ul><h2 class="jobSectionHeader">Why CodeRabbit</h2>
<ul><li>Real ownership. You're the field function &mdash; not a coordinator inside one. The roadmap is yours to write.
</li><li>A market that's moving. AI code review is one of the most adopted AI use cases in engineering today. The pipeline is there if you go get it.
</li><li>Builders around you. You'll work alongside engineers, researchers, and a GTM team that ship fast and care about craft.
</li><li>Flex. SF-based hybrid with travel for the events that matter.
</li></ul><h2 class="jobSectionHeader">Our Values</h2>
<ul><li>Collaborative Humans: We build together, not in silos
</li><li>Fearless Innovators: We explore bold ideas and iterate quickly
</li><li>Persistent Builders: We embrace hard problems and see them through
</li><li>Impact-Driven: We focus on tools that meaningfully improve developer workflows
</li><li>Adaptive Learners: We evolve with the technology we&rsquo;re shaping
</li></ul>CodeRabbit is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.</div></div></div></div></div>

Field Marketing Manager Lead

Handshake

<div>
 
 About Handshake
 
 
 
 Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.
 
 
 
 Why Join Handshake Now
 
 
 
 <ul>
 <li>
 Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel
 </li>
 <li>
 Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world&rsquo;s top educational institutions
 </li>
 <li>
 Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others
 </li>
 <li>
 Build a massive, fast-growing business with billions in revenue
 
 
 </li>
 </ul>
 
 About The Role
 
 
 
 We&rsquo;re hiring an
 
 Associate Software Engineer
 
 to help build our Reinforcement Learning Environments (RLE) platform&mdash;the systems where frontier AI models learn to complete real-world work.
 
 
 This is a hands-on, growth-oriented role where you&rsquo;ll contribute to building core systems, learn quickly from senior engineers, and gain exposure to cutting-edge AI infrastructure.
 
 
 
 Location:
 
 San Francisco, CA (in-office, 5 days/week)
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Contribute to building RLE environments and supporting infrastructure
 </li>
 <li>
 Implement features across backend systems and frontend interfaces
 </li>
 <li>
 Work with senior engineers to improve system reliability and performance
 </li>
 <li>
 Help build modular workflow domains (e.g., engineering, finance, legal)
 </li>
 <li>
 Support data pipelines that power model training and evaluation
 
 
 </li>
 </ul>
 What We&rsquo;re Looking For
 
 
 <ul>
 <li>
 0&ndash;2 years of experience (internships or full-time) in software engineering
 </li>
 <li>
 Familiarity with backend development and APIs
 </li>
 <li>
 Working knowledge of ReactJS and TypeScript
 </li>
 <li>
 Understanding of databases (e.g., PostgreSQL) and basic system design
 </li>
 <li>
 Eagerness to learn and operate in fast-paced environments
 
 
 </li>
 </ul>
 Nice to Have
 
 
 <ul>
 <li>
 Exposure to cloud platforms (AWS, GCP)
 </li>
 <li>
 Experience with data pipelines or ML-adjacent systems
 </li>
 <li>
 Interest in AI/ML or simulation systems
 
 
 </li>
 </ul>
 What Success Looks Like
 
 
 <ul>
 <li>
 Ships reliable features that improve RLE functionality
 </li>
 <li>
 Grows into owning small components independently
 </li>
 <li>
 Contributes to faster iteration and better developer velocity
 
 
 </li>
 </ul>
 
 Perks
 
 
 
 Handshake delivers benefits that help you feel supported&mdash;and thrive at work and in life.
 
 
 The below benefits are for full-time US employees.
 
 
 🎯 Ownership: Equity in a fast-growing company
 
 
 💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching
 
 
 🍼 Family Support: Paid parental leave, fertility benefits, parental coachin
 
 
 💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
 
 
 📚 Growth: $2,000 learning stipend, ongoing development
 
 
 💻 Office: Commuting support, free lunch, and gym in our SF office
 
 
 🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days
 
 
 🤝 Connection: Team outings &amp; referral bonuses
 
 
 Compensation Range: $135K - $150K
 
 
</div>

Associate Software Engineer, RLE

<div>
 
 About Handshake
 
 
 
 Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.
 
 
 
 Why Join Handshake Now
 
 
 
 <ul>
 <li>
 Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel
 </li>
 <li>
 Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world&rsquo;s top educational institutions
 </li>
 <li>
 Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others
 </li>
 <li>
 Build a massive, fast-growing business with billions in revenue
 
 
 </li>
 </ul>
 
 About The Role
 
 
 
 We&rsquo;re hiring a
 
 Software Engineer
 
 to build our Reinforcement Learning Environments (RLE) platform&mdash;the interactive systems where frontier AI models learn to complete real-world work.
 
 
 This role owns meaningful components end-to-end and plays a key part in scaling systems that power model training and evaluation.
 
 
 
 Location:
 
 San Francisco, CA (in-office, 5 days/week)
 
 
 What You&rsquo;ll Do
 
 
 <ul>
 <li>
 Build and scale core components of RLE environments and infrastructure
 </li>
 <li>
 Design and implement backend systems and data pipelines
 </li>
 <li>
 Translate ambiguous product and research needs into production systems
 </li>
 <li>
 Develop modular, reusable workflow domains
 </li>
 <li>
 Improve reliability, observability, and system performance
 
 
 </li>
 </ul>
 What We&rsquo;re Looking For
 
 
 <ul>
 <li>
 2&ndash;4 years of experience in backend, distributed systems, or ML-adjacent infrastructure
 </li>
 <li>
 Strong proficiency in ReactJS and TypeScript
 </li>
 <li>
 Solid experience with relational databases (PostgreSQL) and data modeling
 </li>
 <li>
 Experience with cloud infrastructure (AWS/GCP) and CI/CD
 </li>
 <li>
 Ability to independently drive projects from design to production
 
 
 </li>
 </ul>
 Nice to Have
 
 
 <ul>
 <li>
 Experience with simulation systems or evaluation platforms
 </li>
 <li>
 Exposure to ML systems or working with research teams
 </li>
 <li>
 Experience in fast-moving, operations-heavy environments
 
 
 </li>
 </ul>
 What Success Looks Like
 
 
 <ul>
 <li>
 Owns and delivers key RLE components end-to-end
 </li>
 <li>
 Improves system scalability and developer velocity
 </li>
 <li>
 Enables faster launch of new domains with high-quality data
 
 
 </li>
 </ul>
 
 Perks
 
 
 
 Handshake delivers benefits that help you feel supported&mdash;and thrive at work and in life.
 
 
 The below benefits are for full-time US employees.
 
 
 🎯 Ownership: Equity in a fast-growing company
 
 
 💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching
 
 
 🍼 Family Support: Paid parental leave, fertility benefits, parental coaching
 
 
 💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
 
 
 📚 Growth: $2,000 learning stipend, ongoing development
 
 
 💻 Office: Commuting support, free lunch, and gym in our SF office
 
 
 🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days
 
 
 🤝 Connection: Team outings &amp; referral bonuses
 
 
 Compensation Range: $157.5K - $175K
 
 
</div>

Software Engineer I, RLE

<div>About Handshake
Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.
Why join Handshake now:<ul><li>
Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel</li><li>
Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world&rsquo;s top educational institutions</li><li>
Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others</li><li>
Build a massive, fast-growing business with billions in revenue</li></ul><h2 class="jobSectionHeader">
About the Role</h2>
We&rsquo;re hiring a Software Engineer to build our Reinforcement Learning Environments (RLE) platform&mdash;the interactive systems where frontier AI models learn to complete real-world work.
This role owns meaningful components end-to-end and plays a key part in scaling systems that power model training and evaluation.
Location: San Francisco, CA (in-office, 5 days/week)<h2 class="jobSectionHeader">
What You&rsquo;ll Do</h2><ul><li>
Build and scale core components of RLE environments and infrastructure</li><li>
Design and implement backend systems and data pipelines</li><li>
Translate ambiguous product and research needs into production systems</li><li>
Develop modular, reusable workflow domains</li><li>
Improve reliability, observability, and system performance</li></ul><h2 class="jobSectionHeader">
What We&rsquo;re Looking For</h2><ul><li>
4-6 years of experience in backend, distributed systems, or ML-adjacent infrastructure</li><li>
Strong proficiency in ReactJS and TypeScript</li><li>
Solid experience with relational databases (PostgreSQL) and data modeling</li><li>
Experience with cloud infrastructure (AWS/GCP) and CI/CD</li><li>
Ability to independently drive projects from design to production</li></ul><h2 class="jobSectionHeader">
Nice to Have</h2><ul><li>
Experience with simulation systems or evaluation platforms</li><li>
Exposure to ML systems or working with research teams</li><li>
Experience in fast-moving, operations-heavy environments</li></ul><h2 class="jobSectionHeader">
What Success Looks Like</h2><ul><li>
Owns and delivers key RLE components end-to-end</li><li>
Improves system scalability and developer velocity</li><li>
Enables faster launch of new domains with high-quality data</li></ul><h2 class="jobSectionHeader">
Perks</h2>
Handshake delivers benefits that help you feel supported&mdash;and thrive at work and in life.
The below benefits are for full-time US employees.
Ownership: Equity in a fast-growing company
Financial Wellness: 401(k) match, competitive compensation, financial coaching
Family Support: Paid parental leave, fertility benefits, parental coaching
Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
Growth: $2,000 learning stipend, ongoing development
Office: Commuting support, free lunch, and gym in our SF office
Time Off: Flexible PTO, 15 holidays + 2 flex days
Connection: Team outings &amp; referral bonuses
Compensation Range: $189K - $210K</div>

Software Engineer II, RLE

FluidStack

<div><div><div>San Francisco, CA,Austin, TX
 </div></div><div>Full Time</div></div>
 <div><h1 class="jobSectionHeader">About Fluidstack</h1>
At Fluidstack, we build the compute, data centers, and power that will fuel artificial superintelligence. We supply GWs of compute capabilities to the world&rsquo;s biggest AI Labs at industry-defining speeds.
Our team is small, fast, and obsessed with quality. We own outcomes end-to-end, challenge assumptions, and treat our customers' problems as our own. No task is beneath anyone here.
There are a few thousand people who will shape the trajectory of superintelligence. Come and be one of them.
<h1 class="jobSectionHeader">About the role</h1>
We're hiring a Product Manager to own our AI platform roadmap, including managed inference and agent platforms. You'll define how Fluidstack enables customers to deploy, scale, and optimize LLM inference workloads&mdash;from model serving and routing to agent orchestration and compound AI systems. This role requires balancing customer needs for low latency and high throughput with the operational realities of GPU utilization, cost efficiency, and platform reliability. You'll work across engineering, ML research, and go-to-market teams to position Fluidstack against inference-first competitors like Together AI, Fireworks, Baseten, Modal, and Replicate.
<h1 class="jobSectionHeader">What you'll do</h1>
<ul><li>Own the product strategy and roadmap for managed inference services, including model deployment, autoscaling, multi-LoRA serving, and inference optimization
</li><li>Define requirements for agent platform capabilities: structured outputs, function calling, memory primitives, tool integration, and multi-step reasoning workflows
</li><li>Drive decisions on which inference optimizations to prioritize: speculative decoding, continuous batching, KV cache management, quantization support, and custom kernel integration
</li><li>Partner with ML infrastructure engineers to design APIs, SDKs, and deployment workflows that support model fine-tuning, version management, and A/B testing
</li><li>Work with datacenter teams to optimize GPU allocation strategies&mdash;balancing dedicated vs. serverless deployments, cold start latency, and cost-per-token economics
</li><li>Analyze competitive offerings from Together AI (inference optimization stack), Fireworks (custom inference engine), Baseten (training-to-inference integration), and Modal (serverless architecture)
</li><li>Define pricing models that align with customer usage patterns (tokens, requests, GPU-hours) while maintaining healthy unit economics
</li><li>Conduct customer research to understand inference workload requirements: latency SLAs, throughput targets, model size constraints, and integration needs
</li><li>Translate customer feedback into feature specifications&mdash;including support for new model architectures, framework integrations (vLLM, TensorRT-LLM, TGI), and observability tooling
</li><li>Build go-to-market materials: reference architectures, performance benchmarks, cost calculators, and migration guides for customers moving from self-hosted or competing platforms
</li></ul><h1 class="jobSectionHeader">About you</h1>
<ul><li>5+ years product management experience with at least 3 years focused on AI/ML infrastructure, inference platforms, or developer tools
</li><li>Strong technical understanding of transformer architectures, inference optimization techniques, and production ML systems
</li><li>Experience building products for technical users deploying LLMs in production (ML engineers, research scientists, AI application developers)
</li><li>Track record of shipping features that improved inference latency, throughput, or cost efficiency&mdash;backed by quantitative metrics
</li><li>Deep familiarity with the inference ecosystem: serving frameworks (vLLM, TensorRT-LLM, TGI), model formats (GGUF, SafeTensors), and API standards (OpenAI-compatible endpoints)
</li><li>Understanding of GPU memory constraints, batching strategies, and the tradeoffs between latency-optimized vs. throughput-optimized serving
</li><li>Ability to translate complex technical concepts (speculative decoding, PagedAttention, Multi-LoRA) into clear customer value propositions
</li><li>Experience conducting competitive analysis in the inference market, including pricing elasticity, feature differentiation, and customer acquisition patterns
</li><li>Comfortable working with engineering teams to debug performance bottlenecks, analyze profiling data, and prioritize kernel-level optimizations
</li><li>Bonus: Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT), compound AI patterns, or model fine-tuning workflows
</li></ul><h1 class="jobSectionHeader">Compensation</h1>
To provide greater transparency to candidates, we share base pay ranges for all US-based job postings. Our compensation package includes base salary, equity, benefits, and for applicable roles, commissions plans. Our cash compensation range for this role is $180,000-$250,000. Final offers vary based on geography, candidate experience, relevant credentials, and other factors. Outstanding candidates may be eligible for adjusted terms plus meaningful equity.
We are committed to pay equity and transparency.
Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans&rsquo; status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.
You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application- someone from our recruiting team will be in touch.</div>

Product Manager, AI Platform

Lila Sciences

<div>Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), ML engineers build and operate the systems that turn generative models and reasoning frameworks into production capabilities powering automated scientific discovery across Lila's life science domains.

We are seeking a Principal ML Engineer to design, build, and scale the ML infrastructure behind models spanning biological sequence design, molecular structure prediction, antibody engineering, and multimodal scientific reasoning. You will own critical systems end to end, from training pipelines and distributed compute to model deployment and integration into Lila's closed-loop discovery engine.

This is a high-impact IC role for someone who operates at the intersection of ML systems engineering and life science applications. You will shape the technical direction for how ML models are trained, evaluated, and deployed at scale, collaborate closely with AI scientists and experimental researchers to close the computational-experimental loop, and drive Lila's ML infrastructure toward the next generation of capabilities.

What You'll Be Building
<ul>
<li>Design, build, and optimize large-scale training pipelines for generative models on biological and chemical data, including distributed training across GPU clusters</li>
<li>Own production ML systems end to end: model deployment, serving infrastructure, monitoring, and reliability for models used in Lila's scientific workflows</li>
<li>Architect ML infrastructure that supports rapid iteration across sequence design, structure prediction, and multimodal scientific reasoning workloads</li>
<li>Drive the engineering side of Lila's &quot;Lab-in-the-Loop&quot; lifecycle: build pipeline models, integrate experimental feedback loops, and ensure model outputs are actionable for downstream scientific workflows</li>
<li>Define and advance ML engineering standards, tooling, and best practices across the AI organization</li>
<li>Collaborate with AI scientists to translate research prototypes into robust, scalable production systems, bridging the research-to-deployment gap</li>
</ul>
What You'll Need to Succeed
<ul>
<li>Master's degree or higher in Computer Science, Machine Learning, or a related quantitative field (or Bachelor's with equivalent professional experience)</li>
<li>10+ years of hands-on experience building and operating production ML systems at scale</li>
<li>Deep expertise in distributed training infrastructure, including experience with large-scale GPU clusters (AWS, GCP, or on-prem)</li>
<li>Strong software engineering fundamentals: system design, production-grade code, CI/CD, observability, and reliability practices</li>
<li>Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) with experience optimizing training and inference performance</li>
<li>Demonstrated ability to drive technical direction for ML infrastructure independently, from architecture through implementation</li>
<li>Track record of cross-functional collaboration with research scientists, translating between ML methodology and engineering execution</li>
</ul>
Bonus Points For
<ul>
<li>Experience building training or inference infrastructure for generative models applied to biological sequences, molecular structures, or scientific data</li>
<li>Experience with agentic frameworks, active learning loops, or closed-loop experimental workflows</li>
<li>Contributions to open-source ML tools, frameworks, or infrastructure projects</li>
<li>Familiarity with at least one life science domain (molecular biology, genomics, protein engineering, or nucleic acid design)</li>
<li>Experience with model evaluation frameworks for scientific applications where ground truth is sparse or delayed</li></ul><div> 
About LILA

Lila Sciences is building Scientific Superintelligence&trade; to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory&trade; instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We're All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.</div></div>

Principal, Machine Learning Engineer

Explore 350+ Ai ml software developer in San Francisco this May. Find roles across top companies and apply now to boost your career.