About Merge Labs:

Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. We’re pursuing this goal by developingfundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone to use.

About the team:

Merge is building the next generation of brain-computer interfaces by combining recent advances in synthetic biology, neuroscience, AI, and non-invasive imaging. To support this mission, we are building a cross-functional data-science group which sits at the intersection of computational modeling, neuroscience, and biomolecular engineering. This group collaborates extensively with wet-lab scientists, automation engineers, and data engineers to create ML frameworks that accelerate molecule discovery and device optimization.

About the role:

We’re hiring a Senior / Principal ML Scientist to design and scale Bayesian optimization and reinforcement-learning frameworks that guide molecular engineering campaigns through iterative design–build–test–learn (DBTL) cycles. Starting from a blank slate, you’ll first architect the company’s closed-loop optimization backbone– building the data and modeling foundations that connect experiments to these ML frameworks. Over time, you’ll help translate these prototypes into production pipelines that measurably improve experimental throughput and discovery success across multiple biomolecular and neuroengineering verticals.

In this role, you will:

• Build the scientific and engineering scaffolding for active-learning and closed-loop optimization, including data ingestion, ML modeling, and library design.
• Collaborate with wet-lab scientists to define tractable optimization objectives and encode domain specific priors and constraints.
• Prototype representation-learning and acquisition-strategy using internal and public datasets; benchmark and validate model performance.
• Integrate ML models with experimental data streams and serve to non-domain experts for model democratization.
• Extend ML frameworks to handle multi-objective or constrained optimization problems.
• Stay up-to-date with the latest research in Bayesian optimization, active learning, and RL, and prototype novel algorithms that can be deployed to improve the company’s discovery or development workflows.
• Contribute to the long-term research roadmap and serve as a thought-leader for scientists

You might thrive in this role if you have:

• Strong grounding in probabilistic modeling, uncertainty quantification, and representation learning.
• Working knowledge of preference optimization and transfer-learning strategies
• Proficiency in Python / PyTorch / BoTorch / Pyro (or similar) and comfort writing clean, reproducible production grade code.
• Experience bridging machine learning and experimental science – working with sparse, noisy, and or high-cost data.
• A collaborative, systems-level mindset.

For more information about hiring at Merge, please visit our Hiring FAQ

About Analog Devices

Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today’s innovators stay Ahead of What’s Possible. Learn more at www.analog.com and on LinkedIn and X.

About the team

Merge is building the next generation of brain-computer interfaces by combining recent advances in synthetic biology, neuroscience, AI, and non-invasive imaging. To support this mission, we are building a cross-functional data-science group which sits at the intersection of computational modeling, neuroscience, and biomolecular engineering. This group collaborates extensively with wet-lab scientists, automation engineers, and data engineers to create ML frameworks that accelerate molecule discovery and device optimization.

The Analog Garage is ADI’s Innovation Lab, located in the heart of downtown Boston. We pioneer breakthrough technologies to solve high-impact problems that drive tangible value. Bringing together engineers, research scientists, and business leaders, we develop new technologies and solutions in a fast-moving, experiment-focused startup atmosphere.

About the role

The Algorithmic Solutions Group develops cutting-edge, efficient algorithms to bring intelligence to the physical world. We fuse state-of-the-art machine learning with deep domain expertise to convert raw physical data into actionable insights, solving the hard problems where off-the-shelf solutions fall short.

We are seeking a Staff Research Scientist, Artificial Intelligence to operate at the intersection of modern AI and the physical world. This position challenges you to rethink AI breakthroughs, extending them beyond text and images to master complex physical signals—from multimodal sensory data to precision actuators and RF systems. You will architect and validate novel solutions that fuse modern AI with ADI technologies at the edge.

Key Responsibilities

Strategic Problem Definition: Collaborate with business leads and domain experts to identify opportunities where Modern AI can solve previously impossible problems. You will filter “hype” from “value,” focusing on challenges that require deep technical innovation rather than off-the-shelf models.

• Research-to-Product: Lead technical execution from mathematical conceptualization to proof-of-concept. You will partner with researchers and engineers across the organization to bridge the gap between abstract research papers and validated solutions.
• Architecting Physical AI: Design next-generation neural architectures and custom training paradigms tailored to the physics of the data. You will investigate the inner workings of training dynamics and loss landscapes to develop robust learning strategies for complex physical signals.
• Efficient AI: Bridge the gap between massive foundation models and edge constraints. You will research techniques in model distillation, optimization, and neural architecture search to deploy “Modern AI” on efficient compute platforms.
• Thought Leadership: Maintain a deep awareness of the global AI research landscape. You will bring the best ideas from the academic community into ADI and mentor junior engineers.

The Ideal Candidate

You are a rigorous researcher and a pragmatic builder who thrives on complexity. You bring a “first-principles” understanding of deep learning, capable of deriving and modifying architectures from scratch.

• Educational & Professional Background: You hold a PhD degree in Computer Science, Electrical Engineering, or related area, and have 3+ years of industry experience translating complex theory into working systems.
• Deep Expertise in Modern AI: You possess deep technical mastery of modern architectures such as Transformers, State Space Models (e.g., Mamba), and Diffusion Models. You are equally comfortable with advanced training paradigms such as Self-Supervised Learning (SSL), Reinforcement Learning, and Flow Matching, or techniques for learning from limited data (few-shot/meta-learning). You understand the mathematics behind these methods and can adapt them to novel modalities.
• Engineering Excellence: You combine theoretical depth with expert-level proficiency in PyTorch or JAX. You are experienced in developing, deploying, and optimizing models using modern frameworks and cloud platforms.
• Innovation Mindset: You navigate the ambiguity of early-stage innovation with creative persistence, translating open challenges into concrete technical roadmaps. You excel at decision-making under uncertainty, justifying how your architectural trade-offs directly address the problem and create value.

In Brief

• We’re an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives.
•  Lead Bayesian Health’s AI/ML organization with a hands-on, scrappy approach: setting technical vision, rolling up your sleeves on critical modeling work, and building a world-class team that ships breakthrough ML products saving lives in hospitals nationwide.

Who We Are

Bayesian Health’s mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We’re a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data.

We’re funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association’s venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year. Read more about our recent publication in Nature Medicine that associates our products with lives saved.

What You’ll Do

As Director of AI/ML, you’ll set the technical vision and strategy for Bayesian Health’s machine learning organization while building and leading a high-performing team of data scientists and ML engineers. You’ll partner deeply with Engineering to architect scalable data warehousing and ML infrastructure that enables rapid model development and reliable production deployment. At our stage, you’ll also roll up your sleeves on critical IC work: prototyping models, evaluating system performance, and debugging production issues. This role requires thriving in scrappy, early-stage environments where you’re building the plane while flying it, translating clinical needs into technical roadmaps, and getting your hands dirty to ship breakthrough healthcare products.

Responsibilities

• Team Leadership: Build, mentor, and scale a world-class AI/ML team, establishing technical standards, career development frameworks, and a culture of excellence and ownership.
• Technical Vision & Infrastructure: Define and execute the ML roadmap while partnering closely with Engineering to architect data warehousing solutions, ML infrastructure, and data pipelines that enable the team to rapidly prototype and deploy models at scale.
• Hands-On Modeling & Evaluation: Contribute directly to critical modeling, evaluation, and analysis work, from studies to model performance experiments, ensuring the team ships high-quality ML systems that deliver measurable clinical impact.
• Cross-Functional Partnership: Collaborate with Engineering, Product, and Clinical to translate complex clinical workflows into ML opportunities, and communicate model performance and impact to technical and non-technical stakeholders including customers and investors.

Minimum Qualifications

• Experience leading ML organizations through 0-1 product development in healthcare or clinical settings, thriving in environments with limited tooling and infrastructure.

• Hands-on experience with clinical data standards (HL7, FHIR, EHR) and healthcare ML challenges including data quality, time-series forecasting, and anomaly detection.

• Strong technical background in data platform architecture, including modern data warehousing solutions (Snowflake, Databricks, Redshift), streaming data systems, and ML infrastructure tools.

• Track record of publishing research, speaking at conferences, or contributing to the broader ML community while delivering business results.

• You bring passion and enthusiasm to your work, and are excited to join a growing team to Get Stuff Done and save lives!

In Brief

• We’re an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives.
• Part Data Scientist (building models), part Applied Scientist (productionizing models), and part MLE (deploying, maintaining), also known as “Full Stack Data Scientist” – someone who wants to own the end-to-end effectiveness of their real-time models in a live, clinical AI product.

Who We Are

Bayesian Health’s mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We’re a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data.

We’re funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association’s venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year.

What You’ll Do

As a Staff Machine Learning Engineer, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren’t afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model’s predictions by reading and writing production-grade Python and SQL code.

Responsibilities

• Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods.
• Productionizing: The same models that you develop with production-grade python.

• Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploying production-grade Python code to implement those strategies.
• MLOps: Build infrastructure that enables ML model development and deployment in production systems.

Minimum Qualifications

• Ph.D. in a relevant field plus 3+ years relevant experience, or a relevant Master’s degree and 5+ years experience shipping ML based software products.
• Experience owning your ML models from prototyping to production.
• Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems.
• Experience using MLOps tools such as SageMaker and MLFlow.

Preferred Qualifications

• Experience going 0-1 and shipping high impact AI/ML products.
• Experience building solutions within healthcare and/or familiarity working with messy health data.
• Experience working with enterprise customers, and the agility and responsiveness they require.
• Comfortable interpreting / leveraging state-of-the-art peer-reviewed methods or tools in designing your approach.
• Excitement for Bayesian’s mission and being a bar raiser so we can accelerate the pace at which we create value.

About Gridware

Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io.

Role Description

We’re looking for a Senior Firmware Engineer to help shape the future of Gridware’s connected devices. In this role, you’ll design and optimize firmware that powers resilient, low-power networks—leveraging peer-to-peer, mesh (802.15.4, Zigbee, Thread), Matter, and emerging NTN protocols. Your work will expand device connectivity while reducing dependency on costly gateways, directly impacting performance, reliability, and uptime across a rapidly scaling fleet.

What You’ll Do

• Design and implement firmware for next-gen wireless communication protocols.
• Build and customize mesh networking solutions to extend device coverage and resilience.
• Optimize communication stacks for maximum efficiency under real-world constraints.
• Prototype, test, and iterate quickly with hardware in the loop.
• Partner with hardware and systems teams on protocol design and integration.
• Debug, validate, and tune performance across diverse environments.

What We’re Looking For

• 5+ years of professional experience in embedded/firmware development.
• Proven expertise with wireless communication protocols.
• Direct, hands-on experience with mesh networking (e.g., 802.15.4, Zigbee, Thread, or similar).
• Strong foundation in low-power system design.
• Experience prototyping and testing with hardware.
• Excellent debugging and optimization skills at the firmware/protocol layer.

Bonus Points

• Experience scaling firmware across large, distributed IoT networks.
• Familiarity with Matter, Thread, or similar IoT ecosystems.
• Knowledge of NTN or long-range, low-power communication systems.
• Background customizing and optimizing communication stacks for efficiency.
• Understanding of physical layer trade-offs (bandwidth, latency, power).